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Language independent sequence labelling for Opinion Target Extraction.

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Title: Language independent sequence labelling for Opinion Target Extraction.
Authors: Agerri, Rodrigo1 rodrigo.agerri@ehu.eus, Rigau, German1 german.rigau@ehu.eus
Source: Artificial Intelligence. Mar2019, Vol. 268, p85-95. 11p.
Subjects: Natural language processing, Sentiment analysis, Data mining, Tasks, Public use
Abstract: Abstract In this research note we present a language independent system to model Opinion Target Extraction (OTE) as a sequence labelling task. The system consists of a combination of clustering features implemented on top of a simple set of shallow local features. Experiments on the well known Aspect Based Sentiment Analysis (ABSA) benchmarks show that our approach is very competitive across languages, obtaining best results for six languages in seven different datasets. Furthermore, the results provide further insights into the behaviour of clustering features for sequence labelling tasks. The system and models generated in this work are available for public use and to facilitate reproducibility of results. [ABSTRACT FROM AUTHOR]
Copyright of Artificial Intelligence is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 134087888
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Language independent sequence labelling for Opinion Target Extraction.
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  Data: <searchLink fieldCode="JN" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink>. Mar2019, Vol. 268, p85-95. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Sentiment+analysis%22">Sentiment analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Tasks%22">Tasks</searchLink><br /><searchLink fieldCode="DE" term="%22Public+use%22">Public use</searchLink>
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  Data: Abstract In this research note we present a language independent system to model Opinion Target Extraction (OTE) as a sequence labelling task. The system consists of a combination of clustering features implemented on top of a simple set of shallow local features. Experiments on the well known Aspect Based Sentiment Analysis (ABSA) benchmarks show that our approach is very competitive across languages, obtaining best results for six languages in seven different datasets. Furthermore, the results provide further insights into the behaviour of clustering features for sequence labelling tasks. The system and models generated in this work are available for public use and to facilitate reproducibility of results. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Artificial Intelligence is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.artint.2018.12.002
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      – Code: eng
        Text: English
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        PageCount: 11
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      – SubjectFull: Natural language processing
        Type: general
      – SubjectFull: Sentiment analysis
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      – SubjectFull: Data mining
        Type: general
      – SubjectFull: Tasks
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      – SubjectFull: Public use
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      – TitleFull: Language independent sequence labelling for Opinion Target Extraction.
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              Text: Mar2019
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              Value: 268
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