{"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/topic-modeling-over-short-texts-by","title":"Topic Modeling over Short Texts by Incorporating Word Embeddings","arxiv_id":"1609.08496","date":"2016-09-27","proceeding":null,"authors":["Jipeng Qiang","Ping Chen","Tong Wang","Xindong Wu"],"abstract":"Inferring topics from the overwhelming amount of short texts becomes a\ncritical but challenging task for many content analysis tasks, such as content\ncharactering, user interest profiling, and emerging topic detecting. Existing\nmethods such as probabilistic latent semantic analysis (PLSA) and latent\nDirichlet allocation (LDA) cannot solve this prob- lem very well since only\nvery limited word co-occurrence information is available in short texts. This\npaper studies how to incorporate the external word correlation knowledge into\nshort texts to improve the coherence of topic modeling. Based on recent results\nin word embeddings that learn se- mantically representations for words from a\nlarge corpus, we introduce a novel method, Embedding-based Topic Model (ETM),\nto learn latent topics from short texts. ETM not only solves the problem of\nvery limited word co-occurrence information by aggregating short texts into\nlong pseudo- texts, but also utilizes a Markov Random Field regularized model\nthat gives correlated words a better chance to be put into the same topic. The\nexperiments on real-world datasets validate the effectiveness of our model\ncomparing with the state-of-the-art models.","url_abs":"http://arxiv.org/abs/1609.08496v1","url_pdf":"http://arxiv.org/pdf/1609.08496v1.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":"topic-modeling-over-short-texts-by","repo_url":"https://github.com/williamscott701/Embedding-LJST","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}