{"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/generative-topic-embedding-a-continuous-1","title":"Generative Topic Embedding: a Continuous Representation of Documents (Extended Version with Proofs)","arxiv_id":"1606.02979","date":"2016-06-09","proceeding":null,"authors":["Shaohua Li","Tat-Seng Chua","Jun Zhu","Chunyan Miao"],"abstract":"Word embedding maps words into a low-dimensional continuous embedding space\nby exploiting the local word collocation patterns in a small context window. On\nthe other hand, topic modeling maps documents onto a low-dimensional topic\nspace, by utilizing the global word collocation patterns in the same document.\nThese two types of patterns are complementary. In this paper, we propose a\ngenerative topic embedding model to combine the two types of patterns. In our\nmodel, topics are represented by embedding vectors, and are shared across\ndocuments. The probability of each word is influenced by both its local context\nand its topic. A variational inference method yields the topic embeddings as\nwell as the topic mixing proportions for each document. Jointly they represent\nthe document in a low-dimensional continuous space. In two document\nclassification tasks, our method performs better than eight existing methods,\nwith fewer features. In addition, we illustrate with an example that our method\ncan generate coherent topics even based on only one document.","url_abs":"http://arxiv.org/abs/1606.02979v2","url_pdf":"http://arxiv.org/pdf/1606.02979v2.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":"generative-topic-embedding-a-continuous-1","repo_url":"https://github.com/askerlee/topicvec","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"document-classification","task_name":"Document Classification"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.02979","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}