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  1. Transactions on Asian and Low-Resource Language Information Processing (TALLIP)
  2. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) : Volume 15
  3. Issue 3, March 2016
  4. BenLem (A Bengali Lemmatizer) and Its Role in WSD
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ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) : Volume 16
ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) : Volume 15
Issue 4, June 2016
Issue 3, March 2016
Converting Continuous-Space Language Models into N-gram Language Models with Efficient Bilingual Pruning for Statistical Machine Translation
BenLem (A Bengali Lemmatizer) and Its Role in WSD
Enhancing Shift-Reduce Constituent Parsing with Action N-Gram Model
Extracting Arabic Causal Relations Using Linguistic Patterns
Bilingual Semantic Role Labeling Inference via Dual Decomposition
Modeling Monolingual Character Alignment for Automatic Evaluation of Chinese Translation
Using Bisect K-Means Clustering Technique in the Analysis of Arabic Documents
Arabic Cross-Language Information Retrieval: A Review
Adaptation of Language Models for SMT Using Neural Networks with Topic Information
Inter-, Intra-, and Extra-Chunk Pre-Ordering for Statistical Japanese-to-English Machine Translation
Issue 2, February 2016
Issue 1, January 2016
ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) : Volume 14

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BenLem (A Bengali Lemmatizer) and Its Role in WSD

Content Provider ACM Digital Library
Author Garain, Utpal Chakrabarty, Abhisek
Copyright Year 2016
Abstract A lemmatization algorithm for Bengali has been developed and evaluated. Its effectiveness for word sense disambiguation (WSD) is also investigated. One of the key challenges for computer processing of highly inflected languages is to deal with the frequent morphological variations of the root words appearing in the text. Therefore, a lemmatizer is essential for developing natural language processing (NLP) tools for such languages. In this experiment, Bengali, which is the national language of Bangladesh and the second most popular language in the Indian subcontinent, has been taken as a reference. In order to design the Bengali lemmatizer (named as BenLem), possible transformations through which surface words are formed from lemmas are studied so that appropriate reverse transformations can be applied on a surface word to get the corresponding lemma back. BenLem is found to be capable of handling both inflectional and derivational morphology in Bengali. It is evaluated on a set of 18 news articles taken from the FIRE Bengali News Corpus consisting of 3,342 surface words (excluding proper nouns) and found to be 81.95% accurate. The role of the lemmatizer is then investigated for Bengali WSD. Ten highly polysemous Bengali words are considered for sense disambiguation. The FIRE corpus and a collection of Tagore’s short stories are considered for creating the WSD dataset. Different WSD systems are considered for this experiment, and it is noticed that BenLem improves the performance of all the WSD systems and the improvements are statistically significant.
Starting Page 1
Ending Page 18
Page Count 18
File Format PDF
ISSN 23754699
e-ISSN 23754702
DOI 10.1145/2835494
Volume Number 15
Issue Number 3
Journal ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP)
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-02-26
Publisher Place New York
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Bengali Indic languages Evaluation Lemmatizer Word sense disambiguation (WSD)
Content Type Text
Resource Type Article
Subject Computer Science
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