Paper Info
Title
Toward advice mining: conditional random fields for extracting advice-revealing text units
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.
Year
DOI
Venue
2013
10.1145/2505515.2505520
CIKM
Keywords
Field
DocType
conditional random field,integrated problem,multiple linear crf,dimensional crf,advice-revealing sentence,advice mining,dimensional crf plus,different type,best performance,respective context sentence,advice-revealing text unit,context sentence,sequence labeling
Conditional random field,Data mining,Sequence labeling,Information retrieval,Computer science,Natural language processing,Artificial intelligence,Sentence,Syntax
Conference
Citations 
PageRank 
References 
1
0.35
15
Authors
2
Name
Order
Citations
PageRank
Alfan Farizki Wicaksono1296.36
Sung-hyon Myaeng280289.18