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State-of-the-art NLP, for example, dependency parsing, can cope fairly well with a wide variety of questions as long as the user avoids unnecessarily convoluted formulations. We thus disregard this aspect in this paper (although it remains an issue for NLP research for very sophisticated questions). 2 Challenge: Knowledge Harvesting Knowledge search needs knowledge to start with. This could be given in the form of an explicit knowledge base, distilled from Web pages and compiled into an integrated collection of facts, or it could be in the form of semantically rich annotations of entities and relationships in the Web pages themselves.

A botanics student may be interested in succulents that grow in both America and Africa. While perhaps resembling quiz questions, this type of queries arises in the daily work of millions of university students. 2. As many people like watching TV game shows, true quiz questions may indeed be a use case as well. Which king was married to Eleanor of Aquitaine? Who was the last wife of Idi Amin? Who was the wife of the French president when Nicolas Sarkozy was born? ”. S. Ceri and M. ): Search Computing, LNCS 5950, pp.

As long as questions are phrased in a relatively straightforward way, modern NLP is able to parse them and understand the logical structure of the questions. Moreover, a full-fledged question contains better cues about the relations that should connect the entities or entity classes of interest, using verbal phrases and prepositions. For example, the question Against which German clubs did Real Madrid play in one of the European leagues or cups? makes it easier to map it to query conditions on the hasPlayedAgainst relation.

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