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

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

Content Provider	ACM Digital Library
Author	Myaeng, Sung-Hyon Wicaksono, Alfan Farizki
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.
Starting Page	2039
Ending Page	2048
Page Count	10
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505520
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Conditional random field Sequence labeling Advice mining
Content Type	Text
Resource Type	Article

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