{"url":"/method/context2vec","slug":"context2vec","name":"context2vec","full_name":"context2vec","full_name_withheld":false,"description_markdown":"**context2vec** is an unsupervised model for learning generic context embedding of wide sentential contexts, using a bidirectional [LSTM](https://paperswithcode.com/method/lstm). A large plain text corpora is trained on to learn a neural model that embeds entire sentential contexts and target words in the same low-dimensional space, which\r\nis optimized to reflect inter-dependencies between targets and their entire sentential context as a whole. \r\n\r\nIn contrast to word2vec that use context modeling mostly internally and considers the target word embeddings as their main output, the focus of context2vec is the context representation. context2vec achieves its objective by assigning similar embeddings to sentential contexts and their associated target words.","description_state":"present","introduced_year":null,"introduced_by":{"title":"context2vec: Learning Generic Context Embedding with Bidirectional LSTM","paper":"/paper/context2vec-learning-generic-context","first_author":"Oren Melamud","n_authors":3,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/context2vec-learning-generic-context"},"source":{"url":"https://aclanthology.org/K16-1006","title":"context2vec: Learning Generic Context Embedding with Bidirectional LSTM","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Natural Language Processing","area_id":"natural-language-processing","collection":"Contextualized Word Embeddings","url":"/methods/category/contextualized-word-embeddings","pwc_aliases":[]},{"area":"Natural Language Processing","area_id":"natural-language-processing","collection":"Word Embeddings","url":"/methods/category/word-embeddings","pwc_aliases":[]}],"n_papers_tagged":6,"archive_num_papers":6,"papers_newest_first":[{"paper":"/paper/always-keep-your-target-in-mind-studying-1","title":"Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitution","date":"2022-06-07","arxiv_id":"2206.11815","n_code_links":1,"syntology":null},{"paper":null,"title":"Token Level Identification of Multiword Expressions Using Contextual Information","date":"2020-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"title":"A Comparative Study of Lexical Substitution Approaches based on Neural Language Models","date":"2020-05-29","arxiv_id":"2006.00031","n_code_links":0,"syntology":null},{"paper":null,"title":"Word Usage Similarity Estimation with Sentence Representations and Automatic Substitutes","date":"2019-05-20","arxiv_id":"1905.08377","n_code_links":0,"syntology":null},{"paper":null,"title":"Lexical Substitution for Evaluating Compositional Distributional Models","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/context2vec-learning-generic-context","title":"context2vec: Learning Generic Context Embedding with Bidirectional LSTM","date":"2016-08-01","arxiv_id":null,"n_code_links":0,"syntology":null}],"papers_shown":6,"tasks":[{"task":"/task/word-sense-induction","name":"Word Sense Induction","papers":3},{"task":"/task/data-augmentation","name":"Data Augmentation","papers":2},{"task":"/task/relation-extraction","name":"Relation Extraction","papers":2},{"task":"/task/sentence","name":"Sentence","papers":2},{"task":"/task/word-embeddings","name":"Word Embeddings","papers":2},{"task":"/task/chunking","name":"Chunking","papers":1},{"task":"/task/coreference-resolution","name":"Coreference Resolution","papers":1},{"task":"/task/named-entity-recognition-ner","name":"Named Entity Recognition (NER)","papers":1},{"task":"/task/natural-language-inference","name":"Natural Language Inference","papers":1},{"task":"/task/semantic-role-labeling","name":"Semantic Role Labeling","papers":1},{"task":"/task/sentence-embeddings","name":"Sentence Embeddings","papers":1},{"task":"/task/sentence-similarity","name":"Sentence Similarity","papers":1},{"task":"/task/sentiment-analysis","name":"Sentiment Analysis","papers":1},{"task":"/task/word-sense-disambiguation","name":"Word Sense Disambiguation","papers":1}],"tasks_shown":14,"n_tasks":14,"usage_by_year":[{"year":"2016","papers":1},{"year":"2018","papers":1},{"year":"2019","papers":1},{"year":"2020","papers":2},{"year":"2022","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/context2vec"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}