language: en
license: mit
tags:
- keyphrase-extraction
datasets:
- midas/inspec
metrics:
- seqeval
widget:
- text: >-
Keyphrase extraction is a technique in text analysis where you extract the
important keyphrases from a text. Since this is a time-consuming process,
Artificial Intelligence is used to automate it. Currently, classical
machine learning methods, that use statistics and linguistics, are widely
used for the extraction process. The fact that these methods have been
widely used in the community has the advantage that there are many
easy-to-use libraries. Now with the recent innovations in deep learning
methods (such as recurrent neural networks and transformers, GANS, …),
keyphrase extraction can be improved. These new methods also focus on the
semantics and context of a document, which is quite an improvement.
example_title: Example 1
- text: >-
In this work, we explore how to learn taskspecific language models aimed
towards learning rich representation of keyphrases fromtext documents. We
experiment with different masking strategies for pre-training transformer
language models (LMs) in discriminative as well as generative settings. In
thediscriminative setting, we introduce a newpre-training objective -
Keyphrase BoundaryInfilling with Replacement (KBIR), showinglarge gains in
performance (upto 9.26 pointsin F1) over SOTA, when LM pre-trained
usingKBIR is fine-tuned for the task of keyphraseextraction. In the
generative setting, we introduce a new pre-training setup for BART -
KeyBART, that reproduces the keyphrases relatedto the input text in the
CatSeq format, insteadof the denoised original input. This also ledto
gains in performance (upto 4.33 points inF1@M) over SOTA for keyphrase
generation.Additionally, we also fine-tune the pre-trainedlanguage models
on named entity recognition(NER), question answering (QA), relation
extraction (RE), abstractive summarization andachieve comparable
performance with that ofthe SOTA, showing that learning rich
representation of keyphrases is indeed beneficial formany other
fundamental NLP tasks.
example_title: Example 2
model-index:
- name: DeDeckerThomas/keyphrase-extraction-kbir-inspec
results:
- task:
type: keyphrase-extraction
name: Keyphrase Extraction
dataset:
type: midas/inspec
name: inspec
metrics:
- type: seqeval
value: 0.588
name: F1-score
** Work in progress **
🔑 Keyphrase Extraction model: KBIR-inspec
Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a text. Since this is a time-consuming process, Artificial Intelligence is used to automate it. Currently, classical machine learning methods, that use statistics and linguistics, are widely used for the extraction process. The fact that these methods have been widely used in the community has the advantage that there are many easy-to-use libraries. Now with the recent innovations in deep learning methods (such as recurrent neural networks and transformers, GANS, …), keyphrase extraction can be improved. These new methods also focus on the semantics and context of a document, which is quite an improvement.
📓 Model Description
This model is a fine-tuned KBIR model on the Inspec dataset. KBIR or Keyphrase Boundary Infilling with Replacement is a pre-trained model which utilizes a multi-task learning setup for optimizing a combined loss of Masked Language Modeling (MLM), Keyphrase Boundary Infilling (KBI) and Keyphrase Replacement Classification (KRC). You can find more information about the architecture in this paper: https://arxiv.org/abs/2112.08547.
The model is fine-tuned as a token classification problem where the text is labeled using the BIO scheme.
| Label | Description |
|---|---|
| B | At the beginning of a keyphrase |
| I | Inside a keyphrase |
| O | Outside a keyphrase |
Kulkarni, Mayank, Debanjan Mahata, Ravneet Arora, and Rajarshi Bhowmik. "Learning Rich Representation of Keyphrases from Text." arXiv preprint arXiv:2112.08547 (2021). Sahrawat, Dhruva, Debanjan Mahata, Haimin Zhang, Mayank Kulkarni, Agniv Sharma, Rakesh Gosangi, Amanda Stent, Yaman Kumar, Rajiv Ratn Shah, and Roger Zimmermann. "Keyphrase extraction as sequence labeling using contextualized embeddings." In European Conference on Information Retrieval, pp. 328-335. Springer, Cham, 2020.
✋ Intended uses & limitations
🛑 Limitations
- This keyphrase extraction model is very domain-specific and will perform very well on abstracts of scientific papers.
- Only works for English documents.
- For a custom model, please consult the training notebook (link incoming).
❓ How to use
# Define post_process functions
def concat_tokens_by_tag(keyphrases):
keyphrase_tokens = []
for id, label in keyphrases:
if label == "B":
keyphrase_tokens.append([id])
elif label == "I":
if len(keyphrase_tokens) > 0:
keyphrase_tokens[len(keyphrase_tokens) - 1].append(id)
return keyphrase_tokens
def extract_keyphrases(example, predictions, tokenizer, index=0):
keyphrases_list = [
(id, idx2label[label])
for id, label in zip(
np.array(example["input_ids"]).squeeze().tolist(), predictions[index]
)
if idx2label[label] in ["B", "I"]
]
processed_keyphrases = concat_tokens_by_tag(keyphrases_list)
extracted_kps = tokenizer.batch_decode(
processed_keyphrases,
skip_special_tokens=True,
clean_up_tokenization_spaces=True,
)
return np.unique([kp.strip() for kp in extracted_kps])
# Load model and tokenizer
model_name = "DeDeckerThomas/keyphrase-extraction-kbir-inspec"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)
# Inference
text = """
Keyphrase extraction is a technique in text analysis where you extract the important keyphrases
from a text. Since this is a time-consuming process, Artificial Intelligence is used to automate it.
Currently, classical machine learning methods, that use statistics and linguistics, are widely used
for the extraction process. The fact that these methods have been widely used in the community has
the advantage that there are many easy-to-use libraries. Now with the recent innovations in
deep learning methods (such as recurrent neural networks and transformers, GANS, …),
keyphrase extraction can be improved. These new methods also focus on the semantics
and context of a document, which is quite an improvement.
""".replace("\n", "")
encoded_input = tokenizer(
text,
truncation=True,
padding="max_length",
max_length=max_length,
return_tensors="pt",
)
output = model(**encoded_input)
logits = output.logits.detach().numpy()
predictions = np.argmax(logits, axis=2)
extracted_kps = extract_keyphrases(encoded_input, predictions, tokenizer)
print("***** Input Document *****")
print(text)
print("***** Prediction *****")
print(extracted_kps)
***** Input Document *****
Keyphrase extraction is a technique in text analysis where you extract the important keyphrases
from a text. Since this is a time-consuming process, Artificial Intelligence is used to automate it.
Currently, classical machine learning methods, that use statistics and linguistics, are widely used
for the extraction process. The fact that these methods have been widely used in the community has
the advantage that there are many easy-to-use libraries. Now with the recent innovations in
deep learning methods (such as recurrent neural networks and transformers, GANS, …),
keyphrase extraction can be improved. These new methods also focus on the semantics
and context of a document, which is quite an improvement.
***** Prediction *****
['Artificial Intelligence' 'GANS' 'Keyphrase extraction'
'classical machine learning' 'deep learning methods'
'keyphrase extraction' 'linguistics' 'recurrent neural networks'
'semantics' 'statistics' 'text analysis' 'transformers']
📚 Training Dataset
👷♂️ Training procedure
For more information, you can take a look at the training notebook (link incoming).
Preprocessing
def preprocess_fuction(all_samples_per_split):
tokenized_samples = tokenizer.batch_encode_plus(
all_samples_per_split[dataset_document_column],
padding="max_length",
truncation=True,
is_split_into_words=True,
max_length=max_length,
)
total_adjusted_labels = []
for k in range(0, len(tokenized_samples["input_ids"])):
prev_wid = -1
word_ids_list = tokenized_samples.word_ids(batch_index=k)
existing_label_ids = all_samples_per_split[dataset_biotags_column][k]
i = -1
adjusted_label_ids = []
for wid in word_ids_list:
if wid is None:
adjusted_label_ids.append(lbl2idx["O"])
elif wid != prev_wid:
i = i + 1
adjusted_label_ids.append(lbl2idx[existing_label_ids[i]])
prev_wid = wid
else:
adjusted_label_ids.append(
lbl2idx[
f"{'I' if existing_label_ids[i] == 'B' else existing_label_ids[i]}"
]
)
total_adjusted_labels.append(adjusted_label_ids)
tokenized_samples["labels"] = total_adjusted_labels
return tokenized_samples
📝Evaluation results
One of the traditional evaluation methods are the precision, recall and F1-score @k,m where k is the number that stands for the first k predicted keyphrases and m for the average amount of predicted keyphrases. The model achieves the following results on the Inspec test set:
| Dataset | P@5 | R@5 | F1@5 | P@10 | R@10 | F1@10 | P@M | R@M | F1@M |
|---|---|---|---|---|---|---|---|---|---|
| Inspec Test Set | 0.53 | 0.47 | 0.46 | 0.36 | 0.58 | 0.41 | 0.58 | 0.60 | 0.56 |
For more information on the evaluation process, you can take a look at the keyphrase extraction evaluation notebook.