AIRCC's International Journal of Computer Science and Information Technology

D. Muhammad Noorul Mubarak ¹, Sareesh Madhu ², S. A. Shanavas ²

Affiliations
1 Department of Computer Science, University of Kerala, IN
2 Department of Linguistics, University of Kerala, IN

Abstract

Parts-of-speech tagging is the process of labeling each word in a sentence. A tag mentions the word's usage in the sentence. Usually, these tags indicate syntactic classification like noun or verb, and sometimes include additional information, with case markers (number, gender etc) and tense markers. A large number of current language processing systems use a parts-of-speech tagger for pre-processing.

There are mainly two approaches usually followed in Parts of Speech Tagging. Those are Rule based Approach and Stochastic Approach. Rule based Approach use predefined handwritten rules. This is the oldest approach and it use lexicon or dictionary for reference. Stochastic Approach use probabilistic and statistical information to assign tag to words. It use large corpus, so that Time complexity and Space complexity is high whereas Rule base approach has less complexity for both Time and Space. Stochastic Approach is the widely used one nowadays because of its accuracy.

Malayalam is a Dravidian family of languages, inflectional with suffixes with the ischolar_main word forms. The currently used Algorithms are efficient Machine Learning Algorithms but these are not built for Malayalam. So it affects the accuracy of the result of Malayalam POS Tagging.

My proposed Approach use Dictionary entries along with adjacent tag information. This algorithm use Multithreaded Technology. Here tagging done with the probability of the occurrence of the sentence structure along with the dictionary entry.

Keywords

NLP, POS Tagger, Rule Based Approach, Stochastic Approach, Multithreading, Dictionary Entry, Malayalam.

Full Text

   

Meera Subhash ¹, M. Wilscy ¹, S. A. Shanavas ²

Affiliations
1 Department of Computer Science, University of Kerala, Kerala, IN
2 Department of Linguistics, University of Kerala, Kerala, IN

Abstract

Words are tools of life which is omnipresent in every language. All words in a language are unique having their own function and meaning. The syntactic and semantic knowledge about individual words can be encapsulated in a highly structured repository known as computational lexicon which is very essential for Machine Translation. For designing a computational lexicon, the first and foremost task is to identify the head words or ischolar_main words in the language. The Root Word Identifier proposed in this work is a rule based approach which automatically removes the inflected part and derive the ischolar_main words using morphophonemic rules. The system is tested with 2400 words from a Malayalam corpus to generate the linguistic information such as the ischolar_main form, their inflected forms and grammatical category. The performance is evaluated using the statistical measures like Precision, Recall and F-measure. The values obtained for these measures are more than 90%.

Keywords

Corpus, Computational Lexicon, Morphophonemic Rules, Root Word, Root Word Identifier.

Full Text