Alejandro Figueroa

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We present an evolutionary approach for the computation of exact answers to natural languages (NL) questions. Answers are extracted directly from the N-best snippets, which have been identified by a standard Web search engine using NL questions. The core idea of our evolutionary approach to Web question answering is to search for those substrings in the(More)
This work 1 presents Mdef-WQA, a system that searches for answers to definition questions in several languages on web snippets. For this purpose, Mdef-WQA biases the search engine in favour of some syntactic structures that often convey definitions. Once descriptive sentences are identified, Mdef-WQA clusters them by potential senses and presents the most(More)
We present a novel method for ranking query paraphrases for effective search in community question answering (cQA). The method uses query logs from Ya-hoo! Search and Yahoo! Answers for automatically extracting a corpus of paraphrases of queries and questions using the query-question click history. Elements of this corpus are automatically ranked according(More)
In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: a b s t r a c t In this work, a new evolutionary model is proposed for ranking answers(More)
Platforms for community-based Question Answering (cQA) are playing an increasing role in the synergy of information-seeking and social networks. Being able to categorize user questions is very important, since these categories are good predictors for the underlying question goal, viz. informational or subjective. Furthermore, an effective cQA platform(More)
This work presents a new approach to automatically answer definition questions from the Web. This approach learns n-gram language models from lexicalised dependency paths taken from abstracts provided by Wikipedia and uses context information to identify candidate descriptive sentences containing target answers. Results using a prototype of the model showed(More)