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Role of Java in Natural Language Processing for Indian Regional Languages
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Hindi and Punjabi are closely related languages with lots of similarities in syntax and vocabulary Both Punjabi and Hindi languages have originated from Sanskrit which is one of the oldest language. In terms of speakers, Hindi is third most widely spoken language and Punjabi is twelfth most widely spoken language. Punjabi language is mostly used in the Northern India and in some areas of Pakistan as well as in UK, Canada and USA. Hindi is the national language of India and is spoken and used by the people all over the country. In the present research, Basic Hindi to Punjabi machine translation system using direct translation approach has been developed. The results of this translation system are surprisingly good. The system includes lexicon based translation, transliteration and continuously improving the system through machine learning module. It also takes care of basic word sense disambiguation.
In this paper, we present the evaluation of our CLIR system performed as part of our participation in FIRE 2008. We participated in Hindi to English, Marathi to English, English to Hindi bilingual task and English, Hindi, Marathi mono-lingual task. We take a query translation based approach using bi-lingual dictionaries. Query words not found in the bi-lingual dictionary are transliterated. Since Devanagari is a phonetic script, for transliteration from Hindi/Marathi to English, we use a rule-based approach and for translitera-tion from English to Hindi, we use a segment based translit-eration approach. The resultant transliteration/translation candidates for each query word are disambiguated using an iterative page-rank style algorithm which, based on term-term co-occurrence statistics, produces the most probable translation of the query. In many Indian language documents the actual Indian word is used as-is without translation. For example, to describe Kashmir Travel, it is quite common to also use Kashmir Yatra in the documents i.e, the actual Hindi word transliterated in English. So, this motivated us to try a full transliteration of the source query without translation. We report the results of this experiment.
Language is a hallmark of intelligence, and endowing computers with the ability to analyze and generate language-a field of research known as Natural Language Processing (NLP)-has been the dream of Artificial Intelligence (AI). In this paper we give a perspective of NLP from the point of view of ambiguity processing and computing under resource constraint. Language is fraught with ambiguity at all levels, be they morphemes, words, phrases, sentences or paragraphs. We first discuss these ambiguities with examples. Then we take a particular case of disambiguation-word sense disambiguation (WSD)-and discuss its solution in the face of multilinguality and resource constraint, namely, scarcity of annotated data. Multilinguality is one of the powerful instruments of leveraging shared resource.
I present a review of machine translation strategies and examine the infrastructure that needs to be created to facilitate R&D efforts in an Indian context. In particular, I present work on ANGABHARTI and ANUBHARTI approaches and their hybridization.
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