Selection of the most suitable translation among all translation candidates returned by bilingual dictionary has always been quiet challenging task for any cross language query translation. Researchers have frequently tried to use word co-occurrence statistics to determine the most probable translation for user query. Algorithms using such statistics have certain shortcomings, which are focused in this paper. We propose a novel method for ambiguity resolution, named ‘two level disambiguation model’. At first level disambiguation, the model properly weighs the importance of translation alternatives of query terms obtained from the dictionary. The importance factor measures the probability of a translation candidate of being selected as the final translation of a query term. This removes the problem of taking binary decision for translation candidates. At second level disambiguation, the model targets the user query as a single concept and deduces the translation of all query terms simultaneously, taking into account the weights of translation alternatives also. This is contrary to previous researches which select translation for each word in source language query independently. The experimental result with English-Hindi cross language information retrieval shows that the proposed two level disambiguation model achieved 79.53% and 83.50% of monolingual translation and 21.11% and 17.36% improvement compared to greedy disambiguation strategies in terms of MAP for short and long queries respectively.

The abundance of multilingual content on internet other than English gives an urge to develop information retrieval system that can cross language boundaries. Such cross lingual information retrieval systems will bridge this language gap and allow user to ask a query in regional language and retrieve relevant documents in a different language. The problem of finding relevant document in language different from source language is the most challenging application of any cross lingual information retrieval. This paper discusses the development process of complete English to Hindi cross language information retrieval system along with the contribution of individual components to the system. The main focus of this paper is to discuss how optimization is done to our disambiguation approach, which we named as 'Two level Disambiguation method'. The experimental results obtained affirm that the addition of a component 'Analyzer' to our CLIR architecture increases the efficiency of our proposed disambiguation algorithm.

Multilingual information is overflowing on internet these days. This increasing diversity of web pages in almost every

No abstract

No abstract