Statement map: reducing web information credibility noise through opinion classification

Authors:
Koji Murakami;Eric Nichols;Junta Mizuno;Yotaro Watanabe;Shouko Masuda;Hayato Goto;Megumi Ohki;Chitose Sao;Suguru Matsuyoshi;Kentaro Inui;Yuji Matsumoto
Affiliations:
Nara Institute Science and Technology, Ikoma, Nara, Japan;Tohoku University, Sendai, Miyagi, Japan;Nara Institute Science and Technology, Ikoma, Nara, Japan;Tohoku University, Sendai, Miyagi, Japan;Osaka Prefecture University, Osaka, Osaka, Japan;Nara Institute Science and Technology, Ikoma, Nara, Japan;Nara Institute Science and Technology, Ikoma, Nara, Japan;Nara Institute Science and Technology, Ikoma, Nara, Japan;Nara Institute Science and Technology, Ikoma, Nara, Japan;Tohoku University, Sendai, Miyagi, Japan;Nara Institute Science and Technology, Ikoma, Nara, Japan
Venue:
AND '10 Proceedings of the fourth workshop on Analytics for noisy unstructured text data
Year:
2010

Citing 14
Cited 3

A common theory of information fusion from multiple text sources step one: cross-document structure

SIGDIAL '00 Proceedings of the 1st SIGdial workshop on Discourse and dialogue - Volume 10
Japanese dependency analysis using cascaded chunking

COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Learning to recognize features of valid textual entailments

HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Making sense of credibility on the Web: Models for evaluating online information and recommendations for future research

Journal of the American Society for Information Science and Technology
A Database of Relations between Predicate Argument Structures for Recognizing Textual Entailment and Contradiction

ISUC '08 Proceedings of the 2008 Second International Symposium on Universal Communication
Statement map: assisting information crediblity analysis by visualizing arguments

Proceedings of the 3rd workshop on Information credibility on the web
A survey of types of text noise and techniques to handle noisy text

Proceedings of The Third Workshop on Analytics for Noisy Unstructured Text Data
Modeling semantic containment and exclusion in natural language inference

COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Enhancing the Japanese WordNet

ALR7 Proceedings of the 7th Workshop on Asian Language Resources
Large-scale verb entailment acquisition from the web

EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Highlighting disputed claims on the web

Proceedings of the 19th international conference on World wide web
Assigning trust to Wikipedia content

WikiSym '08 Proceedings of the 4th International Symposium on Wikis
A structured model for joint learning of argument roles and predicate senses

ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Combining labeled and unlabeled data for learning cross-document structural relationships

IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing

Mining web query logs to analyze political issues

Proceedings of the 3rd Annual ACM Web Science Conference
The promise and peril of real-time corrections to political misperceptions

Proceedings of the 2013 conference on Computer supported cooperative work
Bursting your (filter) bubble: strategies for promoting diverse exposure

Proceedings of the 2013 conference on Computer supported cooperative work companion

Quantified Score

Hi-index	0.00

Visualization

Abstract

On the Internet, users often encounter noise in the form of spelling errors or unknown words, however, dishonest, unreliable, or biased information also acts as noise that makes it difficult to find credible sources of information. As people come to rely on the Internet for more and more information, reducing this credibility noise grows ever more urgent. The STATEMENT MAP project's goal is to help Internet users evaluate the credibility of information sources by mining the Web for a variety of viewpoints on their topics of interest and presenting them to users together with supporting evidence in a way that makes it clear how they are related. In this paper, we show how a STATEMENT MAP system can be constructed by combining Information Retrieval (IR) and Natural Language Processing (NLP) technologies, focusing on the task of organizing statements retrieved from the Web by viewpoints. We frame this as a semantic relation classification task, and identify 4 semantic relations: [AGREEMENT], [CONFLICT], [CONFINEMENT], and [EVIDENCE]. The former two relations are identified by measuring semantic similarity through sentence alignment, while the latter two are identified through sentence-internal discourse processing. As a prelude to end-to-end user evaluation of STATEMENT MAP, we present a large-scale evaluation of semantic relation classification between user queries and Internet texts in Japanese and conduct detailed error analysis to identify the remaining areas of improvement.