Representation Learning for Question Classification via Topic Sparse Autoencoder and Entity Embedding

Dingcheng Li, Jingyuan Zhang, Ping Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Deep learning models have achieved great successes these days. There are intensive studies of word representation learning for question classification. As questions are typically short texts, existing techniques are often not effective for extracting discriminative representations of questions just from a limited number of words. This motivates us to exploit additional information beyond words in order to improve the representation learning of questions. On one hand, topic modeling often captures meaningful semantic structures from the question corpus. Such global topical information should be helpful for question representations. On the other hand, entities extracted from question themselves provide more auxiliary information for short texts from a local viewpoint. Together with words, topics and entities, question representations can be substantially improved.In this paper, we propose a unified neural network framework by integrating Topic modeling, Word embedding and Entity Embedding (TWEE) for question representation learning. Concretely, we introduce a novel topic sparse autoencoder to incorporate discriminative topics into the representation learning of questions. In addition, both words and entity related information are embedded into the network to help learn a more comprehensive question representation. Empirical experiments show that the proposed TWEE framework outperforms the state-of-the-art methods on different datasets.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
EditorsYang Song, Bing Liu, Kisung Lee, Naoki Abe, Calton Pu, Mu Qiao, Nesreen Ahmed, Donald Kossmann, Jeffrey Saltz, Jiliang Tang, Jingrui He, Huan Liu, Xiaohua Hu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages126-133
Number of pages8
ISBN (Electronic)9781538650356
DOIs
StatePublished - Jan 22 2019
Externally publishedYes
Event2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States
Duration: Dec 10 2018Dec 13 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

Conference

Conference2018 IEEE International Conference on Big Data, Big Data 2018
Country/TerritoryUnited States
CitySeattle
Period12/10/1812/13/18

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems

Keywords

  • Entity Embedding
  • Question Classification
  • Representation Learning
  • Topic Sparse Autoen-coder

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