Research Cell: An International Journal of Engineering Sciences

Niladri Sekhar Dash ¹, Arpita Chakraborty ²

Affiliations
1 Linguistic Research Unit, Indian Statistical Institute, Kolkata, IN
2 ISI, Kolkata, IN

Abstract

It will be a nice learning experience for the Bangla language learners if an on-line Bangla education system is supported with a Digital Bangla Pronunciation Dictionary (DBPD), which is accessed in classroom and at home, as the case may be, as one of the most useful reference guides for learning standard and acceptable pronunciation of Bangla words. Keeping this idea at background, in this paper, we have made an attempt to report the design architecture of the proposed digital Bangla pronunciation dictionary, which is being developed with a large lexical database of nearly hundred thousand words that are directly obtained from a digital corpus of Bangla written texts as well as from other digital lexical sources available in the language. This is perhaps the first attempt ever made for any of the Indian languages with a mission for serving the Bangla speakers as well as Bangla language learners with better learning resources and devices for the language across the world. The immediate application of the resource is visualized as a tool for e-governance and on-line language teaching where the learners can access this device to address various linguistic purposes including spelling, pronunciation, part-of-speech, meaning, and usage of words.

Keywords

Pronunciation, Part-of-Speech, Transliteration, Orthography, IPA, Meaning.

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Niladri Sekhar Dash ¹

Affiliations
1 Linguistic Research Unit, Indian Statistical Institute, Kolkata, IN

Abstract

In this paper I describe a few steps that we adopt to develop a digital TermBank after culling the Scientific and Technical Terms (STTs) from a text corpus of Bangla. Following the stages and methods of processing and analysis of corpus we are successful to develop a TermBank which now contains nearly 10,000 terms to be used in various works of linguistics and language technology. The strategy we use can be effectively applied on corpora of other Indian languages for same purposes. This confirms its utility and relevance in NLP works for Indian languages.

Keywords

Scientific and Technical Terms, Corpus, POS Tagging, Collocation, Lemmatization, Treebank, Terminology, Frequency.

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Bishwa Ranjan Das ¹, Hima Bindu Maringanti ¹, Niladri Sekhar Dash ²

Affiliations
1 Department of Computer Application. Maharaja Sriram Chandra Bhanjadeo University, Baripada, India., IN
2 Linguistic Research Unit, Indian Statistical Institute, Kolkata, India., IN

Abstract

This paper presents a novel approach to recognize named entities for Odia newspaper text. The development of a NER system for Odia newspaper text using Support Vector Machine is a challenging task in the field of intelligent computing. Named Entity Recognition aims at classifying each word in a piece of document into predefined target named entity classes in a linear as well as non-linear fashion. Starting with named entity annotated corpora and a set of features it requires to develop a base-line NER System. Some language specific rules are added to the system to recognize some specific NE classes. Moreover, some gazetteers and context patterns are added to the system to increase its performance level as it is observed that identification of rules and context patterns requires language-based knowledge to make the system work better. A lexical database is used to prepare the rules as well as to identify the context patterns for Odia text. A very large corpus including one lakhs sentences both training and test set is taken for experimental test and results show that our approach achieves much higher accuracy than previous approaches.

Keywords

Support Vector Machine, Name Entity Recognition, Part of Speech Tagging, Root Word.

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