NDLI: Subjectivity classification and analysis of the ASRS corpus

Content Provider	IEEE Xplore Digital Library
Author	Switzer, J. Khan, L. Muhaya, F.B.
Copyright Year	2011
Description	Author affiliation: University of Texas at Dallas, USA (Switzer, J.; Khan, L.) \|\| Prince Muqrin Chair (PMC) for IT Security, King Saud University, Saudi Arabia (Muhaya, F.B.)
Abstract	Semantic analysis of corpora containing heavy usage of jargon words and phrases introduces problems not commonly addressed by Natural Language Processing methods. Modern semantic analysis relies on data from unedited websites or other expertly written sources, which lack similar usage of jargon words and phrases. This paper presents a system of semi-supervised lexicon learning algorithms that collate several manually labeled and clustered data sources, such as thesauri. In addition, this paper demonstrates an improvement in performance of these subjectivity classifiers by applying a boosting method. This paper presents a method of automatic Aviation Safety Reporting System (ASRS) shaping factor classification based on the most relevant words from a subjectivity lexicon.
Starting Page	160
Ending Page	165
File Size	150249
Page Count	6
File Format	PDF
ISBN	9781457709647
e-ISBN	9781457709661
e-ISBN	9781457709654
DOI	10.1109/IRI.2011.6009539
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech recognition Thesauri Boosting Humans Natural language processing Classification algorithms Equations Annotation Classification Sentiment Text mining
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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