{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/semantic-structure-and-interpretability-of","title":"Semantic Structure and Interpretability of Word Embeddings","arxiv_id":"1711.00331","date":"2017-11-01","proceeding":null,"authors":["Lutfi Kerem Senel","Ihsan Utlu","Veysel Yucesoy","Aykut Koc","Tolga Cukur"],"abstract":"Dense word embeddings, which encode semantic meanings of words to low\ndimensional vector spaces have become very popular in natural language\nprocessing (NLP) research due to their state-of-the-art performances in many\nNLP tasks. Word embeddings are substantially successful in capturing semantic\nrelations among words, so a meaningful semantic structure must be present in\nthe respective vector spaces. However, in many cases, this semantic structure\nis broadly and heterogeneously distributed across the embedding dimensions,\nwhich makes interpretation a big challenge. In this study, we propose a\nstatistical method to uncover the latent semantic structure in the dense word\nembeddings. To perform our analysis we introduce a new dataset (SEMCAT) that\ncontains more than 6500 words semantically grouped under 110 categories. We\nfurther propose a method to quantify the interpretability of the word\nembeddings; the proposed method is a practical alternative to the classical\nword intrusion test that requires human intervention.","url_abs":"http://arxiv.org/abs/1711.00331v3","url_pdf":"http://arxiv.org/pdf/1711.00331v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"semantic-structure-and-interpretability-of","repo_url":"https://github.com/avaapm/SEMCATdataset2018","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"semantic-structure-and-interpretability-of","repo_url":"https://github.com/ficstamas/word_embedding_interpretability","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[{"slug":"semcat","name":"SEMCAT","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.00331","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}