Twitter Part-Of-Speech Tagging Using Pre-classification Hidden Markov Model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F12%3A86084785" target="_blank" >RIV/61989100:27240/12:86084785 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ICSMC.2012.6377881" target="_blank" >http://dx.doi.org/10.1109/ICSMC.2012.6377881</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICSMC.2012.6377881" target="_blank" >10.1109/ICSMC.2012.6377881</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Twitter Part-Of-Speech Tagging Using Pre-classification Hidden Markov Model

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Twitter Part-Of-Speech Tagging Using Pre-classification Hidden Markov Model

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/EE.2.3.20.0073" target="_blank" >EE.2.3.20.0073: Bio-inspirované metody: věda, vzdělávání a transfer znalostí</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics 2012

ISBN

978-1-4673-1714-6

ISSN

1062-922X

e-ISSN

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Počet stran výsledku

6

Strana od-do

1118-1123

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Soul

Datum konání akce

14. 10. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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