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• W1493492660 abstract "Within a Question Answering (QA) framework, Question Context plays a vital role. We define Question Context to be background knowledge that can be used to represent the user’s information need more completely than the terms in the query alone. This paper proposes a novel approach that uses statistical language modeling techniques to develop a semantic Question Context which we then incorporate into the Information Retrieval (IR) stage of QA. Our approach proposes an Aspect-Based Relevance Language Model as basis of the Question Context Model. This model proposes that the sparse vocabulary of a query can be supplemented with semantic information from concepts (or aspects) related to query terms that already exist within the corpus. We incorporate the Aspect-Based Relevance Language Model into Question Context by first obtaining all of the latent concepts that exist in the corpus for a particular question topic. Then, we derive a likelihood of relevance that relates each Context Term (CT) associated with those aspects to the user’s query. Context Terms from the topics with the highest likelihood of relevance are then incorporated into the query language model based on their relevance score values. We use both query expansion and document model smoothing techniques and evaluate our approach using the traditional recall metric. Our results are promising and show significant improvements recall at low levels of precision using the query expansion method." @default.
• W1493492660 created "2016-06-24" @default.
• W1493492660 creator A5035917885 @default.
• W1493492660 creator A5054753371 @default.
• W1493492660 date "2009-03-16" @default.
• W1493492660 modified "2023-09-23" @default.
• W1493492660 title "Modeling Semantic Question Context for Question Answering" @default.
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• W1493492660 hasPublicationYear "2009" @default.
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