Informatics Publishing Limited

Rakesh Kumar ¹, Vishal Goyal ², Ravinder Khanna ³

Affiliations
1 Punjab Technical University, Kapurthala, IN
2 Dept. of Computer Science, Punjabi University, Patiala, IN
3 MM University, Sadopur, Ambala, IN

Abstract

India has many regional languages. Attempts have been made for developing machine translations between these languages, but little success has been reported so far. Analysis of Hindi to Punjabi machine translation system devised by Punjabi University, Patiala, India has found that Hindi post position से (sē) is translated inaccurately being its ambiguous nature, most of the times, as it has eighteen different senses in Punjabi. The overall translation success rate of this system reported as 87.60%, however the translation success rate in respect of this post position से (sē) is only about 2%. In this paper, N-gram approach (along with its smoothing variants) has been applied to improve the accuracy of translation of this post position से (sē) in already developed Hindi to Punjabi Machine Translation System. It has been concluded that bigram approach with Add-One smoothing algorithm gives the best results in improving the accuracy of translation of post position से (sē) from 2% to 85.49%, thus improving the overall machine translation accuracy of the system from 87.60% to 92.30% .

Keywords

Natural Language Processing (NLP), Word Sense Disambiguation (WSD), Machine Translation (MT).

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Harjeet Singh ¹, Vishal Goyal ², Ravinder Khanna ³

Affiliations
1 Punjab Technical University, Jalandhar, IN
2 Dept. of Computer Science, Punjabi University, Patiala, IN
3 MM University, Sadopur, Ambala, IN

Abstract

A lot of work is done on the standard varieties of Indian languages in the field of MT but dialectal variety of a language is still an unexplored area. Machine Translation between a Standard language and its dialect is easy as standard variety and its dialect are closely related to each other. When the Language pair is closely related then due to the common grammar and vocabulary, it become easy to develop a MT system.

In Punjab generally Malwai, Majhi, Doabi and Powadi dialect are used for the oral communication and no work is still done on any dialect in the field of Machine Translation. This Paper discusses the various phases in the development of Machine Translation system for Standard Punjabi - Malwai dialect pair.

Keywords

Machine Translation, Malwai Dialect.

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Rakesh Kumar ¹, Ravinder Khanna ², Vishal Goyal ³

Affiliations
1 Punjab Technical University, Kapurthala, IN
2 Sachdeva Engg. College for Girls, Gharuan, Mohali, IN
3 Dept. of Computer Science, Punjabi University, Patiala, IN

Abstract

Artificial intelligence (AI) has been a major research area in the later quarter of 20^th century and is likely to be even more so in the 21^st century. A key part of AI is Word Sense Disambiguation (WSD) which deals with choosing the correct sense of a word in the given text. All human languages have words with multiple meaning and selecting the intended sense is important. This paper briefly describes various methods presently used for WSD and their relative effectiveness. WSD applications currently find application in Information Retrieval, Information Extraction, Automated Answering Machine, Speech Reorganization, Machine Translation among many others. WSD has promise for the future in taking AI to the next higher level.

Keywords

Natural Language Processing (NLP), Artificial Intelligence (AI), Word Sense Disambiguation (WSD), Knowledge Based Methods, Supervised/Unsupervised Methods.

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Harjeet Singh ¹, Ravinder Khanna ², Vishal Goyal ³

Affiliations
1 Punjab Technical University, Jalandhar, IN
2 Department of Electronics, M.M. University, Ambala, IN
3 Department of Computer Science, Punjabi University, Patiala, IN

Abstract

Punjabi language is an modern Indo-Aryan language. It is 10th most spoken language in the world. Most of the speakers of Punjabi language lives in Punjab region of India. Punjabi has mainly four dialects viz Majhi, Doabi, Malwi and Pwadhi. Today, dialects represents the default variety of oral communication in the Punjab and Standard Punjabi is almost exclusively used for writing. The Malwai dialect is taken for the study because there are twenty two districts in Punjab state and the malwai dialect is spoken in more than half of the districts. Both Standard Punjabi and Malwai use the same script i.e. Gurukhi but the dialect is different. In this paper the difference between the Standard Punjabi and Malwai are explained.

Keywords

Machine Translation, Punjabi, Malwai Dialect.

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Rakesh Kumar ¹, Ravinder Khanna ¹

Affiliations
1 GGS Sachdeva Engg. College, Punjab, IN

Abstract

Word Sense Disambiguation (WSD) is an important part of Machine Readable Dictionary (MRD) which is extensively used in Expert System/Intelligent Systems. All languages have multiple meanings of words or phrases depending on the context of their usage. WSD draws the correct (intended) meaning using a database called Machine Readable Dictionary (MRD). Some rudimentary designs of MRD have been made for some European Languages. In this paper a preliminary attempt has been made towards the formulation and design of MRD in Punjabi Language using modified Lesk Algorithm which uses a simple method for relating the appropriate word sense relative to set of dictionary meanings of the word or phrase.

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