task_type
stringclasses 1
value | dataset
stringclasses 1
value | input
stringlengths 11
389
| output
stringlengths 43
461
| situation
stringclasses 1
value | label
stringclasses 1
value | extra
stringclasses 1
value | instruction
stringclasses 1
value |
|---|---|---|---|---|---|---|---|
generation
|
absa-quad
|
['We did tip , I guess the model /waitress just wanted more and complained to the manager .']
|
[['waitress', 'service general', 'negative', 'complained']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['And even more so unpleasant because it was so costly for such an unpleasant experience .']
|
[['NULL', 'restaurant general', 'negative', 'unpleasant'], ['NULL', 'restaurant prices', 'negative', 'costly']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['I choose to go with one of the special , the braised lamb shank in red wine , which was excellent .']
|
[['braised lamb shank in red wine', 'food quality', 'positive', 'excellent']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['I was so stunned , and I left the dinner hungry and majorly disappointing .']
|
[['NULL', 'restaurant general', 'negative', 'disappointing']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['People are always friendly .']
|
[['People', 'service general', 'positive', 'friendly']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['I recently tried Suan and I thought that it was great .']
|
[['Suan', 'restaurant general', 'positive', 'great']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Too bad I had paid an extra $ 2 for the stone bowl .']
|
[['stone bowl', 'food prices', 'negative', 'bad']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
["I like the ambience , it 's very dark and original ."]
|
[['ambience', 'ambience general', 'positive', 'like'], ['ambience', 'ambience general', 'positive', 'dark'], ['ambience', 'ambience general', 'positive', 'original']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Pizza was a little soggy .']
|
[['Pizza', 'food quality', 'negative', 'soggy']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['great food , great wine list , great service in a great neighborhood ...']
|
[['food', 'food quality', 'positive', 'great'], ['wine list', 'drinks style_options', 'positive', 'great'], ['service', 'service general', 'positive', 'great'], ['neighborhood', 'location general', 'positive', 'great']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['If you want something really different than try Jekyll and Hyde .']
|
[['Jekyll and Hyde', 'restaurant general', 'positive', 'different']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The food is good .']
|
[['food', 'food quality', 'positive', 'good']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['I was almost amused by the fact that she was turning away customers at 9pm on a Friday night because she `` had a BBQ to go to `` that night - WTF ? ?']
|
[['NULL', 'service general', 'negative', 'WTF']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['My wife had barely touched that mess of a dish .']
|
[['dish', 'food quality', 'negative', 'mess']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['I recommend the jelly fish , drunken chicken and the soupy dumplings , certainly the stir fry blue crab .']
|
[['jelly fish', 'food quality', 'positive', 'recommend'], ['drunken chicken', 'food quality', 'positive', 'recommend'], ['soupy dumplings', 'food quality', 'positive', 'recommend'], ['stir fry blue crab', 'food quality', 'positive', 'recommend']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
["Thalia is a beautiful restaurant with beautiful people serving you , but the food does n't quite match up ."]
|
[['people', 'service general', 'positive', 'beautiful'], ['food', 'food quality', 'negative', "does n't quite match up"], ['Thalia', 'ambience general', 'positive', 'beautiful']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
["We wo n't go to this place again for a good meal ."]
|
[['meal', 'food quality', 'negative', 'good']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['i happen to have a policy that goes along with a little bit of self-respect , which includes not letting a waiter intimidate me , i.e . make me feel bad asking for trivialities like water , or the check .']
|
[['waiter', 'service general', 'negative', 'bad']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['My friends settled for rice dishes , but we came back the following day to try the dim sum , which was good ... not outstanding , but good .']
|
[['dim sum', 'food quality', 'neutral', 'good'], ['dim sum', 'food quality', 'neutral', 'not outstanding']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The signs , the specials menus , food , and even all the waitstaff are ALL TOTALLY Japanese .']
|
[['signs', 'restaurant miscellaneous', 'positive', 'Japanese'], ['specials menus', 'food style_options', 'positive', 'Japanese'], ['food', 'food quality', 'positive', 'Japanese'], ['waitstaff', 'service general', 'positive', 'Japanese']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['When we inquired about ports - the waitress listed off several but did not know taste variations or cost .']
|
[['waitress', 'service general', 'negative', 'did not know']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['This establishment is the real deal .']
|
[['establishment', 'restaurant general', 'positive', 'real deal']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Go there to relax and feel like your somewhere else .']
|
[['NULL', 'ambience general', 'positive', 'relax']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['We also asked for Hooka six times and the waiter kept telling us one minute and never returning with the Hooka .']
|
[['waiter', 'service general', 'negative', 'asked for Hooka six times']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['This place is worth an one-hour drive .']
|
[['place', 'restaurant general', 'positive', 'worth']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['the waitstaffs are nice though .']
|
[['waitstaffs', 'service general', 'positive', 'nice']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The food was exceptional .']
|
[['food', 'food quality', 'positive', 'exceptional']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['I have never left a restaurant feeling as if i was abused , and wasted my hard earned money .']
|
[['restaurant', 'restaurant general', 'negative', 'abused'], ['restaurant', 'restaurant prices', 'negative', 'abused']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Kind , attentive wait staff .']
|
[['wait staff', 'service general', 'positive', 'Kind'], ['wait staff', 'service general', 'positive', 'attentive']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Decor is charming .']
|
[['Decor', 'ambience general', 'positive', 'charming']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The waitress , seems to be more concerned of looking good than actually waitressing .']
|
[['waitress', 'service general', 'negative', 'more concerned of looking good']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
["I have been to Roth 's twice and both times were very disappointing ."]
|
[["Roth 's", 'restaurant general', 'negative', 'disappointing']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
["When you add it all together , it just does n't seem worth it to me ... especially considering the prices ."]
|
[['NULL', 'restaurant general', 'negative', "does n't seem worth it"]]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['We went here for lunch a couple of weeks ago on a Saturday , and I was thoroughly impressed with the food .']
|
[['food', 'food quality', 'positive', 'impressed']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The sake ’ s complimented the courses very well and is successfully easing me into the sake world .']
|
[['sake ’ s', 'drinks quality', 'positive', 'very well'], ['sake ’ s', 'drinks quality', 'positive', 'successfully']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Go hungry and enjoy .']
|
[['NULL', 'restaurant general', 'positive', 'enjoy']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
["You can get a completely delish martini in a glass ( that 's about 2 1/2 drinks ) for $ 8.50 ( I recommend the Vanilla Shanty , mmmm ! ) in a great homey setting with great music ."]
|
[['martini', 'drinks quality', 'positive', 'delish'], ['martini', 'drinks style_options', 'positive', 'delish'], ['martini', 'drinks prices', 'positive', 'delish'], ['Vanilla Shanty', 'drinks quality', 'positive', 'recommend'], ['setting', 'ambience general', 'positive', 'homey'], ['music', 'ambience general', 'positive', 'great']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['More Williamsburg Garbage']
|
[['NULL', 'restaurant general', 'negative', 'Garbage']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['This is an amazing place to try some roti rolls .']
|
[['roti rolls', 'food quality', 'positive', 'try']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Judging from previous posts this used to be a good place , but not any longer .']
|
[['place', 'restaurant general', 'negative', 'used to be a good']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['I wish they would change back to what it was before .']
|
[['NULL', 'restaurant general', 'negative', 'change back']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
["It is quite a spectacular scene i 'll give them that ."]
|
[['scene', 'ambience general', 'positive', 'spectacular']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['A Thai restaurant out of rice during dinner ?']
|
[['Thai restaurant', 'restaurant miscellaneous', 'negative', 'out of rice']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The bar is very well stocked with interesting beers and well priced wines .']
|
[['bar', 'drinks style_options', 'positive', 'well stocked'], ['beers', 'drinks style_options', 'positive', 'interesting'], ['wines', 'drinks prices', 'positive', 'well priced']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The staff there is very attentive and down to earth .']
|
[['staff', 'service general', 'positive', 'attentive'], ['staff', 'service general', 'positive', 'down to earth']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The kitchen however , is almost always slow .']
|
[['kitchen', 'service general', 'negative', 'slow']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Loved it !']
|
[['NULL', 'restaurant general', 'positive', 'Loved']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['It may be a bit packed on weekends , but the vibe is good and it is the best French food you will find in the area .']
|
[['NULL', 'ambience general', 'neutral', 'packed'], ['vibe', 'ambience general', 'positive', 'good'], ['French food', 'food quality', 'positive', 'best']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Our teenage kids love it , too .']
|
[['NULL', 'restaurant general', 'positive', 'love']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['i love their chicken pasta cant remember the name but is sooo good']
|
[['chicken pasta', 'food quality', 'positive', 'love']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Not only is it an adventure getting to this somewhat hidden spot , once you enter the unmarked wooden doors , the zen and intimate décor will make you feel like you ’ re no longer in the city .']
|
[['spot', 'location general', 'neutral', 'hidden'], ['unmarked wooden doors', 'ambience general', 'positive', 'feel like you ’ re no longer in the city'], ['décor', 'ambience general', 'positive', 'intimate']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['great toppings definitely a place you need to check out for late night munchies or a mid day boost !']
|
[['toppings', 'food quality', 'positive', 'great'], ['place', 'restaurant miscellaneous', 'positive', 'great']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['A real dissapointment .']
|
[['NULL', 'food quality', 'negative', 'dissapointment']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['This place is incredibly tiny .']
|
[['place', 'ambience general', 'negative', 'tiny']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The food and service were fine , however the maitre-D was incredibly unwelcoming and arrogant .']
|
[['food', 'food quality', 'positive', 'fine'], ['service', 'service general', 'positive', 'fine'], ['maitre-D', 'service general', 'negative', 'unwelcoming'], ['maitre-D', 'service general', 'negative', 'arrogant']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The duck confit is always amazing and the foie gras terrine with figs was out of this world .']
|
[['foie gras terrine with figs', 'food quality', 'positive', 'out of this world'], ['duck confit', 'food quality', 'positive', 'amazing']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Most importantly , food is excellent .']
|
[['food', 'food quality', 'positive', 'excellent']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['I ordered the smoked salmon and roe appetizer and it was off flavor .']
|
[['smoked salmon and roe appetizer', 'food quality', 'negative', 'off flavor']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Maggot in the food !']
|
[['food', 'food quality', 'negative', 'Maggot']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Salads were fantastic .']
|
[['Salads', 'food quality', 'positive', 'fantastic']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['We had the lobster sandwich and it was FANTASTIC .']
|
[['lobster sandwich', 'food quality', 'positive', 'FANTASTIC']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['I thought this place was totally overrated .']
|
[['place', 'restaurant general', 'negative', 'overrated']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
["I 've lived in NY for 5 years and this place has it all ."]
|
[['place', 'restaurant general', 'positive', 'has it all']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Nobody at this restaurant will give firm answers about anything and in the end , not one person takes responsibility for anything .']
|
[['NULL', 'service general', 'negative', 'not one person takes responsibility for anything']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Ask for Usha , the nicest bartender in manhattan .']
|
[['Usha', 'service general', 'positive', 'nicest']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
["They forgot a sandwich , did n't include plastic forks , and did n't include pita with the hummus platter ."]
|
[['NULL', 'service general', 'negative', 'forgot']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The service was the only thing good about this restaurant .']
|
[['service', 'service general', 'positive', 'good']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['We waited at the bar and had martinis that were just right .']
|
[['martinis', 'drinks quality', 'positive', 'right']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Thius is a must for anyone who loves Shabu-Shabu .']
|
[['Shabu-Shabu', 'food quality', 'positive', 'loves']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['It was totally overpriced - fish and chips was about $ 15 ...']
|
[['NULL', 'food prices', 'negative', 'overpriced'], ['fish and chips', 'food prices', 'negative', 'about $ 15']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['They have a huge selection of different cream cheeses and all of their salads are great .']
|
[['salads', 'food quality', 'positive', 'great'], ['cream cheeses', 'food style_options', 'positive', 'huge'], ['cream cheeses', 'food style_options', 'positive', 'different']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Very pleased']
|
[['NULL', 'restaurant general', 'positive', 'pleased']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['I would not expect this for a $ 55 dinner .']
|
[['dinner', 'food quality', 'negative', 'would not expect'], ['dinner', 'food prices', 'negative', '$ 55']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['I got hair in my food 2 times of then !']
|
[['food', 'food quality', 'negative', 'got hair in my food']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Noodle pudding is exactly the type of service and food I enjoy .']
|
[['service', 'service general', 'positive', 'enjoy'], ['food', 'food quality', 'positive', 'enjoy']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
["Volare virgins or weekly regulars , everyone gets treated the same and you ca n't ask for more than that when the service is this friendly ."]
|
[['service', 'service general', 'positive', 'friendly']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Ive asked a cart attendant for a lotus leaf wrapped rice and she replied back rice and just walked away .']
|
[['cart attendant', 'service general', 'negative', 'walked away']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['This place is always very crowded and popular .']
|
[['place', 'restaurant miscellaneous', 'positive', 'crowded'], ['place', 'restaurant miscellaneous', 'positive', 'popular']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The design and atmosphere is just as good .']
|
[['design', 'ambience general', 'positive', 'good'], ['atmosphere', 'ambience general', 'positive', 'good']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Interesting selection , good wines , service fine , fun decor .']
|
[['wines', 'drinks quality', 'positive', 'good'], ['service', 'service general', 'positive', 'fine'], ['decor', 'ambience general', 'positive', 'fun'], ['selection', 'food style_options', 'positive', 'Interesting']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['first it took us a long time to find the place .']
|
[['place', 'restaurant miscellaneous', 'negative', 'took us a long time']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The manager was rude and handled the situation extremely poorly .']
|
[['manager', 'service general', 'negative', 'rude']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['But the best pork souvlaki I ever had is the main thing .']
|
[['pork souvlaki', 'food quality', 'positive', 'best']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['I wish I could like this place more , and I wish someone would retrain the staff .']
|
[['staff', 'service general', 'negative', 'retrain']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Also , they do not take credit card so come with cash !']
|
[['NULL', 'restaurant miscellaneous', 'neutral', 'come with cash']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Every time I have a special occasion with my boyfriend I have a hard time going anywhere else .']
|
[['NULL', 'restaurant miscellaneous', 'positive', 'have a hard time going anywhere else']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Dumbfoundingly Poor']
|
[['NULL', 'restaurant general', 'negative', 'Dumbfoundingly Poor']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Great food , great prices , great service .']
|
[['food', 'food quality', 'positive', 'Great'], ['NULL', 'restaurant prices', 'positive', 'great'], ['service', 'service general', 'positive', 'great']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['My son and his girlfriend both wanted cheeseburgers and they were huge !']
|
[['cheeseburgers', 'food style_options', 'neutral', 'huge']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Good for dates or with friends .']
|
[['NULL', 'restaurant miscellaneous', 'positive', 'Good']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['I CAN EAT HERE EVERY DAY OF THE WEEK REALLY LOL LOVE THIS PLACE ... )']
|
[['PLACE', 'restaurant general', 'positive', 'LOVE']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['This guy refused to seat her and she left , followed shortly by the four of us , but not before I told him that in my 40 years of world travel , including Paris , that I had never seen such a display of bad behavior by a frontman in a restaurant .']
|
[['frontman', 'service general', 'negative', 'bad']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
["Thank you everyone at Water 's Edge ."]
|
[["Water 's Edge", 'restaurant general', 'positive', 'Thank you']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Overall , decent food at a good price , with friendly people .']
|
[['food', 'food quality', 'positive', 'decent'], ['food', 'food prices', 'positive', 'good'], ['people', 'service general', 'positive', 'friendly']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['I showed it to the manager , and he smilingly apologized and brought us two free desserts ( but did not ask us what we wanted and so brought the last two desserts we would have asked for ) .']
|
[['manager', 'service general', 'positive', 'smilingly']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Service is average .']
|
[['Service', 'service general', 'neutral', 'average']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['I LOVE their Thai']
|
[['Thai', 'food quality', 'positive', 'LOVE']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['The waitress moved our table practically into the bathroom and when we asked to cancel our dinner orders because we did not want to eat sitting on the toilet , we were told no ...']
|
[['waitress', 'service general', 'negative', 'sitting on the toilet']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Probably would not go again ...']
|
[['NULL', 'restaurant general', 'negative', 'not go again']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
||
generation
|
absa-quad
|
['Tiny dessert was $ 8.00 ... just plain overpriced for what it is .']
|
[['dessert', 'food quality', 'negative', 'plain'], ['dessert', 'food style_options', 'negative', 'Tiny'], ['dessert', 'food prices', 'negative', 'overpriced']]
|
none
|
Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'
|
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