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license: mit |
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# UCTopic |
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This repository contains the code of model UCTopic and an easy-to-use tool UCTopicTool used for <strong>Topic Mining</strong>, <strong>Unsupervised Aspect Extractioin</strong> or <strong>Phrase Retrieval</strong>. |
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Our ACL 2022 paper [UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining](https://arxiv.org/abs/2202.13469). |
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# Quick Links |
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- [Overview](#overview) |
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- [Pretrained Model](#pretrained-model) |
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- [Getting Started](#getting-started) |
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- [UCTopic Model](#uctopic-model) |
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- [UCTopicTool](#uctopictool) |
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- [Experiments in Paper](#experiments) |
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- [Requirements](#requirements) |
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- [Datasets](#datasets) |
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- [Entity Clustering](#entity-clustering) |
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- [Topic Mining](#topic-mining) |
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- [Pretraining](#pretraining) |
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- [Contact](#contact) |
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- [Citation](#citation) |
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# Overview |
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We propose UCTopic, a novel unsupervised contrastive learning framework for context-aware phrase representations and topic mining. UCTopic is pretrained in a large scale to distinguish if the contexts of two phrase mentions have the same semantics. The key to pretraining is positive pair construction from our phrase-oriented assumptions. However, we find traditional in-batch negatives cause performance decay when finetuning on a dataset with small topic numbers. Hence, we propose cluster-assisted contrastive learning(CCL) which largely reduces noisy negatives by selecting negatives from clusters and further improves phrase representations for topics accordingly. |
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# Pretrained Model |
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Our released model: |
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| Model | Note| |
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|:-------------------------------|------| |
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|[uctopic-base](https://drive.google.com/file/d/1XQzi4E9ctdI373CK5O-pXQyBvOONssp1/view?usp=sharing)| Pretrained UCTopic model based on [LUKE-BASE](https://arxiv.org/abs/2010.01057) |
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Unzip to get `uctopic-base` folder. |
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# Getting Started |
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We provide an easy-to-use phrase representation tool based on our UCTopic model. To use the tool, first install the uctopic package from PyPI |
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```bash |
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pip install uctopic |
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``` |
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Or directly install it from our code |
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```bash |
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python setup.py install |
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``` |
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## UCTopic Model |
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After installing the package, you can load our model by just two lines of code |
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```python |
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from uctopic import UCTopic |
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model = UCTopic.from_pretrained('JiachengLi/uctopic-base') |
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``` |
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The model will automatically download pre-trained parameters from [HuggingFace's models](https://huggingface.co/models). If you encounter any problem when directly loading the models by HuggingFace's API, you can also download the models manually from the above table and use `model = UCTopic.from_pretrained({PATH TO THE DOWNLOAD MODEL})`. |
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To get pre-trained <strong>phrase representations</strong>, our model inputs are same as [LUKE](https://huggingface.co/docs/transformers/model_doc/luke). Note: please input only <strong>ONE</strong> span each time, otherwise, will have performance decay according to our empirical results. |
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```python |
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from uctopic import UCTopicTokenizer, UCTopic |
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tokenizer = UCTopicTokenizer.from_pretrained('JiachengLi/uctopic-base') |
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model = UCTopic.from_pretrained('JiachengLi/uctopic-base') |
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text = "Beyoncé lives in Los Angeles." |
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entity_spans = [(17, 28)] # character-based entity span corresponding to "Los Angeles" |
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inputs = tokenizer(text, entity_spans=entity_spans, add_prefix_space=True, return_tensors="pt") |
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outputs, phrase_repr = model(**inputs) |
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``` |
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`phrase_repr` is the phrase embedding (size `[768]`) of the phrase `Los Angeles`. `outputs` has the same format as the outputs from `LUKE`. |
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## UCTopicTool |
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We provide a tool `UCTopicTool` built on `UCTopic` for efficient phrase encoding, topic mining (or unsupervised aspect extraction) or phrase retrieval. |
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### Initialization |
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`UCTopicTool` is initialized by giving the `model_name_or_path` and `device`. |
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```python |
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from uctopic import UCTopicTool |
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topic_tool = UCTopicTool('JiachengLi/uctopic-base', device='cuda:0') |
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``` |
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### Phrase Encoding |
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Phrases are encoded by our method `UCTopicTool.encode` in batches, which is more efficient than `UCTopic`. |
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```python |
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phrases = [["This place is so much bigger than others!", (0, 10)], |
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["It was totally packed and loud.", (15, 21)], |
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["Service was on the slower side.", (0, 7)], |
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["I ordered 2 mojitos: 1 lime and 1 mango.", (12, 19)], |
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["The ingredient weren't really fresh.", (4, 14)]] |
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embeddings = topic_tool.encode(phrases) # len(embeddings) is equal to len(phrases) |
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``` |
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**Note**: Each instance in `phrases` contains only one sentence and one span (character-level position) in format `[sentence, span]`. |
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Arguments for `UCTopicTool.encode` are as follows, |
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* **phrase** (List) - A list of `[sentence, span]` to be encoded. |
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* **return_numpy** (bool, *optional*, defaults to `False`) - Return `numpy.array` or `torch.Tensor`. |
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* **normalize_to_unit** (bool, *optional*, defaults to `True`) - Normalize all embeddings to unit vectors. |
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* **keepdim** (bool, *optional*, defaults to `True`) - Keep dimension size `[instance_number, hidden_size]`. |
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* **batch_size** (int, *optional*, defaults to `64`) - The size of mini-batch in the model. |
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### Topic Mining and Unsupervised Aspect Extraction |
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The method `UCTopicTool.topic_mining` can mine topical phrases or conduct aspect extraction from sentences with or without spans. |
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```python |
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sentences = ["This place is so much bigger than others!", |
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"It was totally packed and loud.", |
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"Service was on the slower side.", |
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"I ordered 2 mojitos: 1 lime and 1 mango.", |
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"The ingredient weren't really fresh."] |
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spans = [[(0, 10)], # This place |
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[(15, 21), (26, 30)], # packed; loud |
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[(0, 7)], # Service |
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[(12, 19), (21, 27), (32, 39)], # mojitos; 1 lime; 1 mango |
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[(4, 14)]] # ingredient |
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# len(sentences) is equal to len(spans) |
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output_data, topic_phrase_dict = tool.topic_mining(sentences, spans, \ |
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n_clusters=[15, 25]) |
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# predict topic for new phrases |
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phrases = [["The food here is amazing!", (4, 8)], |
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["Lovely ambiance with live music!", (21, 31)]] |
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topics = tool.predict_topic(phrases) |
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``` |
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**Note**: If `spans` is not given, `UCTopicTool` will extract noun phrases by [spaCy](https://spacy.io/). |
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Arguments for `UCTopicTool.topic_mining` are as follows, |
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Data arguments: |
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* **sentences** (List) - A List of sentences for topic mining. |
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* **spans** (List, *optional*, defaults to `None`) - A list of span list corresponding sentences, e.g., `[[(0, 9), (5, 7)], [(1, 2)]]` and `len(sentences)==len(spans)`. If None, automatically mine phrases from noun chunks. |
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Clustering arguments: |
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* **n_clusters** (int or List, *optional*, defaults to `2`) - The number of topics. When `n_clusters` is a list, `n_clusters[0]` and `n_clusters[1]` will be the minimum and maximum numbers to search, `n_clusters[2]` is the search step length (if not provided, default to 1). |
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* **meric** (str, *optional*, defaults to `"cosine"`) - The metric to measure the distance between vectors. `"cosine"` or `"euclidean"`. |
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* **batch_size** (int, *optional*, defaults to `64`) - The size of mini-batch for phrase encoding. |
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* **max_iter** (int, *optional*, defaults to `300`) - The maximum iteration number of kmeans. |
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CCL-finetune arguments: |
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* **ccl_finetune** (bool, *optional*, defaults to `True`) - Whether to conduct CCL-finetuning in the paper. |
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* **batch_size_finetune** (int, *optional*, defaults to `8`) - The size of mini-batch for finetuning. |
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* **max_finetune_num** (int, *optional*, defaults to `100000`) - The maximum number of training instances for finetuning. |
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* **finetune_step** (int, *optional*, defaults to `2000`) - The number of training steps for finetuning. |
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* **contrastive_num** (int, *optional*, defaults to `5`) - The number of negatives in contrastive learning. |
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* **positive_ratio** (float, *optional*, defaults to `0.1`) - The ratio of the most confident instances for finetuning. |
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* **n_sampling** (int, *optional*, defaults to `10000`) - The number of sampled examples for cluster number confirmation and finetuning. Set to `-1` to use the whole dataset. |
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* **n_workers** (int, *optional*, defaults to `8`) - The number of workers for preprocessing data. |
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Returns for `UCTopicTool.topic_mining` are as follows, |
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* **output_data** (List) - A list of sentences and corresponding phrases and topic numbers. Each element is `[sentence, [[start1, end1, topic1], [start2, end2, topic2]]]`. |
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* **topic_phrase_dict** (Dict) - A dictionary of topics and the list of phrases under a topic. The phrases are sorted by their confidence scores. E.g., `{topic: [[phrase1, score1], [phrase2, score2]]}`. |
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The method `UCTopicTool.predict_topic` predicts the topic ids for new phrases based on your training results from `UCTopicTool.topic_mining`. The inputs of `UCTopicTool.predict_topic` are same as `UCTopicTool.encode` and returns a list of topic ids (int). |
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### Phrase Similarities and Retrieval |
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The method `UCTopicTool.similarity` compute the cosine similarities between two groups of phrases: |
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```python |
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phrases_a = [["This place is so much bigger than others!", (0, 10)], |
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["It was totally packed and loud.", (15, 21)]] |
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phrases_b = [["Service was on the slower side.", (0, 7)], |
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["I ordered 2 mojitos: 1 lime and 1 mango.", (12, 19)], |
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["The ingredient weren't really fresh.", (4, 14)]] |
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similarities = tool.similarity(phrases_a, phrases_b) |
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``` |
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Arguments for `UCTopicTool.similarity` are as follows, |
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* **queries** (List) - A list of `[sentence, span]` as queries. |
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* **keys** (List or `numpy.array`) - A list of `[sentence, span]` as keys or phrase representations (`numpy.array`) from `UCTopicTool.encode`. |
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* **batch_size** (int, *optional*, defaults to `64`) - The size of mini-batch in the model. |
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`UCTopicTool.similarity` returns a `numpy.array` contains the similarities between phrase pairs in two groups. |
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The methods `UCTopicTool.build_index` and `UCTopicTool.search` are used for phrase retrieval: |
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```python |
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phrases = [["This place is so much bigger than others!", (0, 10)], |
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["It was totally packed and loud.", (15, 21)], |
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["Service was on the slower side.", (0, 7)], |
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["I ordered 2 mojitos: 1 lime and 1 mango.", (12, 19)], |
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["The ingredient weren't really fresh.", (4, 14)]] |
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# query multiple phrases |
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query1 = [["The food here is amazing!", (4, 8)], |
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["Lovely ambiance with live music!", (21, 31)]] |
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# query single phrases |
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query2 = ["The food here is amazing!", (4, 8)] |
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tool.build_index(phrases) |
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results = tool.search(query1, top_k=3) |
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# or |
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results = tool.search(query2, top_k=3) |
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``` |
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We also support [faiss](https://github.com/facebookresearch/faiss), an efficient similarity search library. Just install the package following [instructions](https://github.com/facebookresearch/faiss/blob/main/INSTALL.md) here and `UCTopicTool` will automatically use `faiss` for efficient search. |
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`UCTopicTool.search` returns the ranked top k phrases for each query. |
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### Save and Load finetuned UCTopicTool |
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The methods `UCTopicTool.save` and `UCTopicTool.load` are used for save and load all paramters of `UCTopicTool`. |
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Save: |
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```python |
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tool = UCTopicTool('JiachengLi/uctopic-base', 'cuda:0') |
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# finetune UCTopic with CCL |
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output_data, topic_phrase_dict = tool.topic_mining(sentences, spans, \ |
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n_clusters=[15, 25]) |
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tool.save(**your directory**) |
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``` |
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Load: |
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```python |
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tool = UCTopicTool('JiachengLi/uctopic-base', 'cuda:0') |
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tool.load(**your directory**) |
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``` |
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The loaded parameters will be used for all methods (for encoding, topic mining, phrase similarities and retrieval) introduced above. |
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# Experiments |
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In this section, we re-implement experiments in our paper. |
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## Requirements |
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First, install PyTorch by following the instructions from [the official website](https://pytorch.org). To faithfully reproduce our results, please use the correct `1.9.0` version corresponding to your platforms/CUDA versions. |
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Then run the following script to install the remaining dependencies, |
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```bash |
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pip install -r requirements.txt |
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``` |
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Download `en_core_web_sm` model from spacy, |
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```bash |
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python -m spacy download en_core_web_sm |
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``` |
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## Datasets |
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The downstream datasets used in our experiments can be downloaded from [here](https://drive.google.com/file/d/1dVIp9li1Wdh0JgU8slsWm0ObcitbQtSL/view?usp=sharing). |
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## Entity Clustering |
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The config file of entity clustering is `clustering/consts.py` and most arguments are self-explained. Please setup `--gpu` and `--data_path` before running. The clustering scores will be printed. |
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Clustering with our pre-trained phrase embeddings. |
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```bash |
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python clustering.py --gpu 0 |
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``` |
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Clustering with our pre-trained phrase embeddings and Cluster-Assisted Constrastive Learning (CCL) proposed in our paper. |
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```bash |
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python clustering_ccl_finetune.py --gpu 0 |
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``` |
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## Topic Mining |
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The config file of entity clustering is `topic_modeling/consts.py`. |
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**Key Argument Table** |
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| Arguments | Description | |
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|:-----------------|:-----------:| |
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| --num_classes |**Min** and **Max** number of classes, e.g., `[5, 15]`. Our model will find the class number by [silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html).| |
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| --sample_num_cluster |Number of sampled phrases to confirm class number.| |
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| --sample_num_finetune|Number of sampled phrases for CCL finetuning.| |
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| --contrastive_num|Number of negative classes for CCL finetuning.| |
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| --finetune_step | CCL finetuning steps (maximum global steps for finetuning).| |
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**Tips**: Please tune `--batch_size` or `--contrastive_num` for suitable GPU memory usage. |
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Topic mining with our pre-trained phrase embeddings and Cluster-Assisted Constrastive Learning (CCL) proposed in our paper. |
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```bash |
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python find_topic.py --gpu 0 |
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``` |
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**Outputs** |
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We output three files under `topic_results`: |
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| File Name | Description | |
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|:-----------------|:-----------:| |
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| `merged_phraes_pred_prob.pickle` |A dictionary of phrases and their topic number and prediction probability. A topic of a phrase is merged from all phrase mentioins. `{phrase: [topic_id, probability]}`, e.g., {'fair prices': [0, 0.34889686]}| |
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| `phrase_instances_pred.json`| A list of all mined phrase mentions. Each element is `[[doc_id, start, end, phrase_mention], topic_id]`.| |
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| `topics_phrases.json`|A dictionary of topics and corresponding phrases sorted by probability. `{'topic_id': [[phrase1, prob1], [phrase2, prob2]]}`| |
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### Pretraining |
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**Data** |
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For unsupervised pretraining of UCTopic, we use article and span with links from English Wikipedia and Wikidata. Our processed dataset can be downloaded from [here](https://drive.google.com/file/d/1wflsmhPI9J0ZA6aVRl2mQjHIE6JIvzAv/view?usp=sharing). |
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**Training scripts** |
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We provide example training scripts and our default training parameters for unsupervised training of UCTopic in `run_example.sh`. |
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```bash |
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bash run_example.sh |
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``` |
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Arguments description can be found in `pretrain.py`. All the other arguments are standard Huggingface's `transformers` training arguments. |
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**Convert models** |
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Our pretrained checkpoints are slightly different from the checkpoint `uctopic-base`. Please refer `convert_uctopic_parameters.py` to convert it. |
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# Contact |
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If you have any questions related to the code or the paper, feel free to email Jiacheng (`[email protected]`). If you encounter any problems when using the code, or want to report a bug, you can open an issue. Please try to specify the problem with details so we can help you better and quicker! |
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# Citation |
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Please cite our paper if you use UCTopic in your work: |
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```bibtex |
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@inproceedings{Li2022UCTopicUC, |
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title = "{UCT}opic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining", |
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author = "Li, Jiacheng and |
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Shang, Jingbo and |
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McAuley, Julian", |
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booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", |
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month = may, |
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year = "2022", |
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address = "Dublin, Ireland", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2022.acl-long.426", |
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doi = "10.18653/v1/2022.acl-long.426", |
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pages = "6159--6169" |
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} |
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``` |
