Sandesh Singh

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Sandesh Singh is an author.

Publications

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Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Language of vandalism: Improving Wikipedia vandalism detection via stylometric analysis ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies English 2011 Community-based knowledge forums, such as Wikipedia, are susceptible to vandalism, i.e., ill-intentioned contributions that are detrimental to the quality of collective intelligence. Most previous work to date relies on shallow lexico-syntactic patterns and metadata to automatically detect vandalism in Wikipedia. In this paper, we explore more linguistically motivated approaches to vandalism detection. In particular, we hypothesize that textual vandalism constitutes a unique genre where a group of people share a similar linguistic behavior. Experimental results suggest that (1) statistical models give evidence to unique language styles in vandalism, and that (2) deep syntactic patterns based on probabilistic context free grammars (PCFG) discriminate vandalism more effectively than shallow lexicosyntactic patterns based on n-grams. 0 0
Language of vandalism: improving Wikipedia vandalism detection via stylometric analysis HLT English 2011 0 0
Question classification - A semantic approach using wordnet and wikipedia And wikipedia
Question answering systems
Question classification
Wordnet
Proceedings of the 4th Indian International Conference on Artificial Intelligence, IICAI 2009 English 2009 Question Answering Systems are providing answers to the users' questions in succinct form where Question classification module of a Question Answering System plays a very important role in pinpointing the exact answer of the question. In literature, incorrect question classification lias been cited as one of the major causes of poor performance of the Question Answering Systems and this emphasizes on the importance of question classification module designing. In this paper, we have proposed a question classification method that combines the powerful semantic features of the WordNet and the vast knowledge repository of the Wikipedia to describe informative terms explicitly. We have trained our method over a standard set of 5500 questions (by UIUC) and then tested over 5 TREC question collections and compared our results. Our system's average question classification accuracy is 89.55% in comparison of 80.2% by Zhang and Lee [17], 84.2% by Li and Roth [7], 89.2% by Huang [6]. The question classification accuracy suggests the effectiveness of the method which is promising in the field of open domain question classification. Copyright 0 0
World wide web based question answering system - A relevance feedback framework for automatic answer validation Answer validation
Question answering system
Web validation
2nd International Conference on the Applications of Digital Information and Web Technologies, ICADIWT 2009 English 2009 An open domain question answering system is one of the emerging information retrieval systems available on the World Wide Web that is becoming popular day by day to get succinct and relevant answers in response of users' questions. The validation of the correctness of the answer is an important issue in the field of question answering. In this paper, we are proposing a World Wide Web based solution for answer validation where answers returned by open domain Question Answering Systems can be validated using online resources such as Wikipedia and Google. We have applied several heuristics for answer validation task and tested them against some popular World Wide Web based open domain Question Answering Systems over a collection of 500 questions collected from standard sources such as TREC, the Worldbook, and the Worldfactbook. We found that the proposed method is yielding promising results for automatic answer validation task. 0 0