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      Semantic Models for Re-ranking in Question Answering

      Fifth BCS-IRSG Symposium on Future Directions in Information Access (FDIA 2013) (FDIA)

      Future Directions in Information Access (FDIA 2013)

      3 September 2013

      Question Answering, Semantics, Learning to Rank

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          This paper describes a research aimed at unveiling the role of Semantic Models in Question Answering. In these systems questions and answers are often expressed in quite different languages, so our objective is to bridge this “lexical chasm” adopting semantic representations. The aim of the research is to find out if Semantc Models are useful for this task and if they can improve the answer re-ranking performance. We have carried out an initial evaluation of a subset of the semantic models on the CLEF2010 QA dataset, showing their effectiveness. We also did a first attempt in combining them by means of Learning to Rank algorithms.

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          Most cited references 8

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          Composition in distributional models of semantics.

          Vector-based models of word meaning have become increasingly popular in cognitive science. The appeal of these models lies in their ability to represent meaning simply by using distributional information under the assumption that words occurring within similar contexts are semantically similar. Despite their widespread use, vector-based models are typically directed at representing words in isolation, and methods for constructing representations for phrases or sentences have received little attention in the literature. This is in marked contrast to experimental evidence (e.g., in sentential priming) suggesting that semantic similarity is more complex than simply a relation between isolated words. This article proposes a framework for representing the meaning of word combinations in vector space. Central to our approach is vector composition, which we operationalize in terms of additive and multiplicative functions. Under this framework, we introduce a wide range of composition models that we evaluate empirically on a phrase similarity task.
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            A solution to Plato's problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge.

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              Representing word meaning and order information in a composite holographic lexicon.

              The authors present a computational model that builds a holographic lexicon representing both word meaning and word order from unsupervised experience with natural language. The model uses simple convolution and superposition mechanisms (cf. B. B. Murdock, 1982) to learn distributed holographic representations for words. The structure of the resulting lexicon can account for empirical data from classic experiments studying semantic typicality, categorization, priming, and semantic constraint in sentence completions. Furthermore, order information can be retrieved from the holographic representations, allowing the model to account for limited word transitions without the need for built-in transition rules. The model demonstrates that a broad range of psychological data can be accounted for directly from the structure of lexical representations learned in this way, without the need for complexity to be built into either the processing mechanisms or the representations. The holographic representations are an appropriate knowledge representation to be used by higher order models of language comprehension, relieving the complexity required at the higher level. ((c) 2007 APA, all rights reserved).

                Author and article information

                September 2013
                September 2013
                : 20-25
                Department of Computer Science

                University of Bari Aldo Moro

                Via Orabona 4, I-70125

                Bari, Italy
                © Piero Molino. Published by BCS Learning and Development Ltd. Fifth BCS-IRSG Symposium on Future Directions in Information Access (FDIA 2013), Granada, Spain

                This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit

                Fifth BCS-IRSG Symposium on Future Directions in Information Access (FDIA 2013)
                Granada, Spain
                3 September 2013
                Electronic Workshops in Computing (eWiC)
                Future Directions in Information Access (FDIA 2013)
                Product Information: 1477-9358BCS Learning & Development
                Self URI (journal page):
                Electronic Workshops in Computing


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