Toward advice mining: Conditional random fields for extracting advice-revealing text units

Alfan Farizki Wicaksono, Sung Hyon Myaeng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

Web forums are platforms for personal communications on sharing information with others. Such information is often expressed in the form of advice. In this paper, we address the problem of advice-revealing text unit (ATU) extraction from online forums due to its usefulness in travel domain. We represent advice as a two-tuple comprising an advice-revealing sentence and its context sentences. To extract the advice-revealing sentences, we propose to define the task as a sequence labeling problem, using three different types of features: syntactic, contextual, and semantic features. To extract the context sentences, we propose to use a 2 Dimensional CRF (2D-CRF) model, which gives the best performance compared to traditional machine learning models. Finally, we present a solution to the integrated problem of extracting both advice-revealing sentences and their respective context sentences at the same time using our proposed models, i.e., Multiple Linear CRF (ML-CRF) and 2 Dimensional CRF Plus (2D-CRF+). The experimental results show that ML-CRF performs better than any other models studied in this paper for extracting advice-revealing sentences and context sentences.

Original languageEnglish
Title of host publicationCIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management
Pages2039-2048
Number of pages10
DOIs
Publication statusPublished - 11 Dec 2013
Event22nd ACM International Conference on Information and Knowledge Management, CIKM 2013 - San Francisco, CA, United States
Duration: 27 Oct 20131 Nov 2013

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference22nd ACM International Conference on Information and Knowledge Management, CIKM 2013
Country/TerritoryUnited States
CitySan Francisco, CA
Period27/10/131/11/13

Keywords

  • Advice mining
  • Conditional random field
  • Sequence labeling

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