Reinforcing the topic of embeddings with Theta Pure Dependence for text classification

Ning Xing, Yuexian Hou, Peng Zhang, Wenjie Li, Dawei Song

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


For sentiment classification, it is often recognized that embedding based on distributional hypothesis is weak in capturing sentiment contrast-contrasting words may have similar local context. Based on broader context, we propose to incorporate Theta Pure Dependence (TPD) into the Paragraph Vector method to reinforce topical and sentimental information. TPD has a theoretical guarantee that the word dependency is pure, i.e., the dependence pattern has the integral meaning whose underlying distribution can not be conditionally factorized. Our method outperforms the state-of-the-art performance on text classification tasks.
Original languageEnglish
Title of host publicationConference Proceedings - EMNLP 2015
Subtitle of host publicationConference on Empirical Methods in Natural Language Processing
PublisherAssociation for Computational Linguistics (ACL)
Number of pages6
ISBN (Electronic)9781941643327
Publication statusPublished - 1 Jan 2015
EventConference on Empirical Methods in Natural Language Processing, EMNLP 2015 - Lisbon, Portugal
Duration: 17 Sep 201521 Sep 2015


ConferenceConference on Empirical Methods in Natural Language Processing, EMNLP 2015

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

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