Neural Text Segmentation and Its Application to Sentiment Analysis

Jing LI, Billy CHIU, Shuo SHANG, Ling SHAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

18 Citations (Scopus)


Text segmentation is a fundamental task in natural language processing. Depending on the levels of granularity, the task can be defined as segmenting a document into topical segments, or segmenting a sentence into elementary discourse units (EDUs). Traditional solutions to the two tasks heavily rely on carefully designed features. The recently proposed neural models do not need manual feature engineering, but they either suffer from sparse boundary tags or cannot efficiently handle the issue of variable size output vocabulary. In light of such limitations, we propose a generic end-to-end segmentation model, namely SEGBOT, which first uses a bidirectional recurrent neural network to encode an input text sequence. SEGBOT then uses another recurrent neural networks, together with a pointer network, to select text boundaries in the input sequence. In this way, SEGBOT does not require any hand-crafted features. More importantly, SEGBOT inherently handles the issue of variable size output vocabulary and the issue of sparse boundary tags. In our experiments, SEGBOT outperforms state-of-the-art models on two tasks: document-level topic segmentation and sentence-level EDU segmentation. As a downstream application, we further propose a hierarchical attention model for sentence-level sentiment analysis based on the outcomes of SEGBOT. The hierarchical model can make full use of both word-level and EDU-level information simultaneously for sentence-level sentiment analysis. In particular, it can effectively exploit EDU-level information, such as the inner properties of EDUs, which cannot be fully encoded in word-level features. Experimental results show that our hierarchical model achieves new state-of-the-art results on the Movie Review and Stanford Sentiment Treebank benchmarks.
Original languageEnglish
Pages (from-to)828-842
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number2
Early online date31 Mar 2020
Publication statusPublished - 1 Feb 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 1989-2012 IEEE.


  • Natural language processing
  • hierarchical attention
  • pointer networks
  • sentiment analysis
  • text segmentation


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