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

Published: 27 October 2013 Publication History

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.

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    cover image ACM Conferences
    CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management
    October 2013
    2612 pages
    ISBN:9781450322638
    DOI:10.1145/2505515
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    Published: 27 October 2013

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    Author Tags

    1. advice mining
    2. conditional random field
    3. sequence labeling

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    October 27 - November 1, 2013
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    • (2021)Generating Tips from Product ReviewsProceedings of the 14th ACM International Conference on Web Search and Data Mining10.1145/3437963.3441755(310-318)Online publication date: 8-Mar-2021
    • (2021)A Comprehensive Review of Markov Random Field and Conditional Random Field Approaches in Pathology Image AnalysisArchives of Computational Methods in Engineering10.1007/s11831-021-09591-w29:1(609-639)Online publication date: 27-Apr-2021
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    • (2017)A review on conditional random fields as a sequential classifier in machine learning2017 International Conference on Electrical Engineering and Computer Science (ICECOS)10.1109/ICECOS.2017.8167121(143-148)Online publication date: Aug-2017

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