1,124 research outputs found
An automatic part-of-speech tagger for Middle Low German
Syntactically annotated corpora are highly important for enabling large-scale diachronic and diatopic language research. Such corpora have recently been developed for a variety of historical languages, or are still under development. One of those under development is the fully tagged and parsed Corpus of Historical Low German (CHLG), which is aimed at facilitating research into the highly under-researched diachronic syntax of Low German. The present paper reports on a crucial step in creating the corpus, viz. the creation of a part-of-speech tagger for Middle Low German (MLG). Having been transmitted in several non-standardised written varieties, MLG poses a challenge to standard POS taggers, which usually rely on normalized spelling. We outline the major issues faced in the creation of the tagger and present our solutions to them
Parts of Speech Tagging: Rule-Based
Parts of speech (POS) tagging is the process of assigning a word in a text as corresponding to a part of speech based on its definition and its relationship with adjacent and related words in a phrase, sentence, or paragraph. POS tagging falls into two distinctive groups: rule-based and stochastic. In this paper, a rule-based POS tagger is developed for the English language using Lex and Yacc. The tagger utilizes a small set of simple rules along with a small dictionary to generate sequences of tokens
Voted Approach for Part of Speech Tagging in Bengali
PACLIC 23 / City University of Hong Kong / 3-5 December 200
Part of Speech Tagging for Text Clustering in Swedish
Proceedings of the 17th Nordic Conference of Computational Linguistics
NODALIDA 2009.
Editors: Kristiina Jokinen and Eckhard Bick.
NEALT Proceedings Series, Vol. 4 (2009), 150-157.
© 2009 The editors and contributors.
Published by
Northern European Association for Language
Technology (NEALT)
http://omilia.uio.no/nealt .
Electronically published at
Tartu University Library (Estonia)
http://hdl.handle.net/10062/9206
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