Paper Info
Title
Automatic extraction of advice-revealing sentences foradvice mining from online forums
Abstract
Web forums often contain explicit key learnings gleaned from people's experiences since they are platforms for personal communications on sharing information with others. One of the key learnings contained inWeb forums is often expressed in the form of advice. As part of human experience mining from Web resources, we aim to provide a methodology to extract advice-revealing sentences from Web forums due to its usefulness, especially in travel domain. Instead of viewing the problem as a simple classification, we define it as a sequence labeling problem using various features. We identify three different types of features (i.e., syntactic features, context features, and sentence informativeness) and propose a new way of using Hidden Markov Model (HMM) for labeling sequential sentences, which in our experiment gave the best performance for our task. Moreover, the sentence informativeness score serves as an important feature for this task. It is worth noting that this work is the first attempt to extract advice-revealing sentences from Web forums.
Year
DOI
Venue
2013
10.1145/2479832.2479857
K-CAP
Keywords
Field
DocType
sequential sentence,explicit key,best performance,online forum,advice-revealing sentence,automatic extraction,web resource,web forum,hidden markov model,key learning,sentence informativeness score,sentence informativeness,foradvice mining,sequence labeling
Web resource,Data mining,Sequence labeling,Information retrieval,Computer science,Hidden Markov model,Syntax,Sentence
Conference
Citations 
PageRank 
References 
5
0.49
16
Authors
2
Name
Order
Citations
PageRank
Alfan Farizki Wicaksono1296.36
Sung-hyon Myaeng280289.18