NDLI: Twitter part-of-speech tagging using pre-classification Hidden Markov model

Content Provider	IEEE Xplore Digital Library
Author	Shichang Sun Hongbo Liu Hongfei Lin Abraham, A.
Copyright Year	2012
Description	Author affiliation: VSB - Technical University of Ostrava, Poruba, Czech Republic (Abraham, A.) \|\| Dalian University of Technology, 116024, China (Shichang Sun; Hongbo Liu; Hongfei Lin)
Abstract	Hidden Markov models (HMM) have been widely used in natural language processing (NLP), especially in syntactic level applications, which appears naturally as short-range-dependent sequence recognition problems. But the structure of HMM limits the usage of global knowledge including the sentiment analysis of the text, which has become an increasingly popular research topic in NLP now. In this paper, we propose a novel treatment of HMM model to use the result of sentimental subjectivity analysis in syntactic level task, i.e. part-of-speech (POS) tagging. The subjectivity information is introduced as a pre-classification procedure into the interval-type HMM. The subjectivity degree of the testing sentence is used as a combination factor to choose an appropriate value from the interval. Experiments results on public tagging data sets shows that the proposed approach enhanced the performance of POS tagging.
Starting Page	1118
Ending Page	1123
File Size	146894
Page Count	6
File Format	PDF
ISBN	9781467317139
e-ISBN	9781467317146
e-ISBN	9781467317122
DOI	10.1109/ICSMC.2012.6377881
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-14
Publisher Place	Korea (South)
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Tagging Training Natural language processing Data models Niobium Analytical models Naive Bayes model Subjectivity analysis Part-of-speech tagging
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
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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