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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Pratibha Bajpai
Amity University
Computer Science & IT Electrical & Electronics Engineering

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Two Level Disambiguation Model for Query Translation Pratibha Bajpai; Parul Verma; Syed Q. Abbas
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 5: October 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (647.677 KB) | DOI: 10.11591/ijece.v8i5.pp3923-3932

Abstract

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.
Notice of Retraction Improved Query Translation for English to Hindi Cross Language Information Retrieval Pratibha Bajpai; Parul Verma
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 4, No 2: June 2016
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v4i2.211

Abstract

Notice of Retraction-----------------------------------------------------------------------After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IAES's Publication Principles.We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.The presenting author of this paper has the option to appeal this decision by contacting ijeei.iaes@gmail.com.-----------------------------------------------------------------------Bilingual dictionaries have always been an important source of query translation in Cross Language Information Retrieval. Besides other issues bilingual translation suffers from ambiguity problem. To resolve this issue, several recent works have recommended the use of term co occurrence statistics. Same concept with a major modification is the focus of our work described here. Our work is based on the fact that all terms do not have same discriminating power in a query. To overcome such problem, our algorithm provides more weight to discriminating terms in the query and treats co occurrences of useful terms as more valuable than those of frequent terms. The paper also takes into account the concept of local context in formulating formula for co-occurrences statistics. In the experiments, our method achieved 85% of monolingual translation in terms of the mean average precision (MAP). The results are quiet encouraging as compared to other methods used for cross language information retrieval for Indian languages. 
Co-Authors Parul Verma Syed Q. Abbas