Pinaki Bhaskar

Language independent query focused snippet generation

The present paper describes the development of a language independent query focused snippet generation module. This module takes the query and content of each retrieved document and generates a query dependent snippet for each retrieved document. The algorithm of this module based on the sentence extraction, sentence scoring and sentence ranking. Subjective evaluation has been. English snippet got the best evaluation score, i.e. 1 and overall average evaluation score of 0.83 has been achieved in the scale of 0 to 1.

A Hybrid QA System with Focused IR and Automatic Summarization for INEX 2011

The article presents the experiments carried out as part of the participation in the QA track of INEX 2011. We have submitted two runs. The INEX QA task has two main sub tasks, Focused IR and Automatic Summarization. In the Focused IR system, we first preprocess the Wikipedia documents and then index them using Nutch. Stop words are removed from each query tweet and all the remaining tweet words are stemmed using Porter stemmer. The stemmed tweet words form the query for retrieving the most relevant document using the index. The automatic summarization system takes as input the query tweet along with the tweet’s text and the title from the most relevant text document. Most relevant sentences are retrieved from the associated document based on the TF-IDF of the matching query tweet, tweet’s text and title words. Each retrieved sentence is assigned a ranking score in the Automatic Summarization system. The answer passage includes the top ranked retrieved sentences with a limit of 500 words. The two unique runs differ in the way in which the relevant sentences are retrieved from the associated document. Our first run got the highest score of 432.2 in Relaxed metric of Readability evaluation among all the participants.

Cross Lingual Snippet Generation Using Snippet Translation System

Multi Lingual Snippet Generation MLSG systems provide the users with snippets in multiple languages. But collecting and managing documents in multiple languages in an efficient way is a difficult task and thereby makes this process more complicated. Fortunately, this requirement can be fulfilled in another way by translating the snippets from one language to another with the help of Machine Translation MT systems. The resulting system is called Cross Lingual Snippet Generation CLSG system. This paper presents the development of a CLSG system by Snippet Translation when documents are available only in one language. We consider the English-Bengali language pair for snippet translation in one direction English to Bengali. In this work, a major concentration is given towards translating snippets with simpler but excluding deeper MT concepts. In experimental results, an average BLEU score of 14.26 and NIST score of 4.93 are obtained.