{"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/coherence-aware-neural-topic-modeling","title":"Coherence-Aware Neural Topic Modeling","arxiv_id":"1809.02687","date":"2018-09-07","proceeding":"EMNLP 2018 10","authors":["Ran Ding","Ramesh Nallapati","Bing Xiang"],"abstract":"Topic models are evaluated based on their ability to describe documents well\n(i.e. low perplexity) and to produce topics that carry coherent semantic\nmeaning. In topic modeling so far, perplexity is a direct optimization target.\nHowever, topic coherence, owing to its challenging computation, is not\noptimized for and is only evaluated after training. In this work, under a\nneural variational inference framework, we propose methods to incorporate a\ntopic coherence objective into the training process. We demonstrate that such a\ncoherence-aware topic model exhibits a similar level of perplexity as baseline\nmodels but achieves substantially higher topic coherence.","url_abs":"http://arxiv.org/abs/1809.02687v1","url_pdf":"http://arxiv.org/pdf/1809.02687v1.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":"coherence-aware-neural-topic-modeling","repo_url":"https://github.com/YongfeiYan/Neural-Document-Modeling","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"coherence-aware-neural-topic-modeling","repo_url":"https://github.com/gonsoomoon-ml/topic-modeling","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"topic-models","task_name":"Topic Models"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.02687","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.02687"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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