Bootstrapping polarity classifiers with rule-based classification.
In this article, we examine the effectiveness of bootstrapping supervised machine-learning polarity classifiers with the help of a domain-independent rule-based classifier that relies on a lexical resource, i.e., a polarity lexicon and a set of linguistic rules. The benefit of this method is that th...
| Published in: | Language Resources & Evaluation Vol. 47; no. 4; pp. 1049 - 1089 |
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| Main Authors: | , , |
| Format: | Article |
| Published: |
Springer Nature
Dec2013
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=92719604&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 92719604 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Dec2013 vid: 47 iid: 4 pid: 237 pub: Springer Nature artinfo: ui: 92719604 10.1007/s10579-013-9218-3 ppf: 1049 ppct: 40 formats: fmt: @attributes: type: P size: 612KB tig: atl: Bootstrapping polarity classifiers with rule-based classification. aug: au: Wiegand, Michael Klenner, Manfred Klakow, Dietrich affil: Spoken Language Systems, Saarland University, Building C7.1, 66123, Saarbrücken, Germany Institute of Computational Linguistics, Zürich University, Binzmühlestrasse 14, 8050, Zürich, Switzerland su: Polarity (Linguistics) Statistical bootstrapping Rule-based programming Feature selection Sentiment analysis sug: subj: Polarity (Linguistics) Statistical bootstrapping Rule-based programming Feature selection Sentiment analysis keyword: Bootstrapping methods Feature engineering Polarity classification Text classification ab: In this article, we examine the effectiveness of bootstrapping supervised machine-learning polarity classifiers with the help of a domain-independent rule-based classifier that relies on a lexical resource, i.e., a polarity lexicon and a set of linguistic rules. The benefit of this method is that though no labeled training data are required, it allows a classifier to capture in-domain knowledge by training a supervised classifier with in-domain features, such as bag of words, on instances labeled by a rule-based classifier. Thus, this approach can be considered as a simple and effective method for domain adaptation. Among the list of components of this approach, we investigate how important the quality of the rule-based classifier is and what features are useful for the supervised classifier. In particular, the former addresses the issue in how far linguistic modeling is relevant for this task. We not only examine how this method performs under more difficult settings in which classes are not balanced and mixed reviews are included in the data set but also compare how this linguistically-driven method relates to state-of-the-art statistical domain adaptation. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2013. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2013 holdings: @attributes: islocal: N |
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