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        <title>From Entity Linking to Question Answering – Recent Progress on Semantic Grounding Tasks</title>
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        <namePart type="given">Ming-Wei</namePart>
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            <title>Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)</title>
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        <name type="personal">
            <namePart type="given">Bo</namePart>
            <namePart type="family">Han</namePart>
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        <name type="personal">
            <namePart type="given">Alan</namePart>
            <namePart type="family">Ritter</namePart>
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        <name type="personal">
            <namePart type="given">Leon</namePart>
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        <name type="personal">
            <namePart type="given">Wei</namePart>
            <namePart type="family">Xu</namePart>
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        <name type="personal">
            <namePart type="given">Tim</namePart>
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    <abstract>Entity linking and semantic parsing have been shown to be crucial to important applications such as question answering and document understanding. These tasks often require structured learning models, which make predictions on multiple interdependent variables. In this talk, I argue that carefully designed structured learning algorithms play a central role in entity linking and semantic parsing tasks. In particular, I will present several new structured learning models for entity linking, which jointly detect mentions and disambiguate entities as well as capture non-textual information. I will then show how to use a staged search procedure to building a state-of-the-art knowledge base question answering system. Finally, if time permits, I will discuss different supervision protocols for training semantic parsers and the value of labeling semantic parses.</abstract>
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%0 Conference Proceedings
%T From Entity Linking to Question Answering – Recent Progress on Semantic Grounding Tasks
%A Chang, Ming-Wei
%Y Han, Bo
%Y Ritter, Alan
%Y Derczynski, Leon
%Y Xu, Wei
%Y Baldwin, Tim
%S Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F chang-2016-entity
%X Entity linking and semantic parsing have been shown to be crucial to important applications such as question answering and document understanding. These tasks often require structured learning models, which make predictions on multiple interdependent variables. In this talk, I argue that carefully designed structured learning algorithms play a central role in entity linking and semantic parsing tasks. In particular, I will present several new structured learning models for entity linking, which jointly detect mentions and disambiguate entities as well as capture non-textual information. I will then show how to use a staged search procedure to building a state-of-the-art knowledge base question answering system. Finally, if time permits, I will discuss different supervision protocols for training semantic parsers and the value of labeling semantic parses.
%U https://aclanthology.org/W16-3902
%P 2

Markdown (Informal)

[From Entity Linking to Question Answering – Recent Progress on Semantic Grounding Tasks](https://aclanthology.org/W16-3902) (Chang, WNUT 2016)

• From Entity Linking to Question Answering – Recent Progress on Semantic Grounding Tasks (Chang, WNUT 2016)

ACL

• Ming-Wei Chang. 2016. From Entity Linking to Question Answering – Recent Progress on Semantic Grounding Tasks. In Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), page 2, Osaka, Japan. The COLING 2016 Organizing Committee.