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An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation

, and . (2016)cite arxiv:1607.05368Comment: 1st Workshop on Representation Learning for NLP.

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Automatic Evaluation of Topic Coherence., , , and . HLT-NAACL, page 100-108. The Association for Computational Linguistics, (2010)ITTC @ TREC 2021 Clinical Trials Track., , , , , , , , and . TREC, volume 500-335 of NIST Special Publication, National Institute of Standards and Technology (NIST), (2021)On collocations and topic models., , and . ACM Trans. Speech Lang. Process., 10 (3): 10:1-10:14 (2013)Word Sense Induction for Novel Sense Detection., , , , and . EACL, page 591-601. The Association for Computer Linguistics, (2012)LED down the rabbit hole: exploring the potential of global attention for biomedical multi-document summarisation., , , , , and . SDP@COLING, page 181-187. Association for Computational Linguistics, (2022)M3: Multi-level dataset for Multi-document summarisation of Medical studies., , , , and . EMNLP (Findings), page 3887-3901. Association for Computational Linguistics, (2022)Topic Intrusion for Automatic Topic Model Evaluation., , and . EMNLP, page 844-849. Association for Computational Linguistics, (2018)An Automatic Approach for Document-level Topic Model Evaluation., , and . CoNLL, page 206-215. Association for Computational Linguistics, (2017)#DemocratsAreDestroyingAmerica: Rumour Analysis on Twitter During COVID-19., , and . CIKM (Workshops), volume 2699 of CEUR Workshop Proceedings, CEUR-WS.org, (2020)Discovering Relevant Reviews for Answering Product-Related Queries., , , , and . ICDM, page 1468-1473. IEEE, (2019)