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    <titleInfo>
        <title>Knowledge Acquisition for Web Search</title>
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    <name type="personal">
        <namePart type="given">Marius</namePart>
        <namePart type="family">Pasca</namePart>
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            <title>Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts</title>
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            <namePart type="given">Wenjie</namePart>
            <namePart type="family">Li</namePart>
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        <name type="personal">
            <namePart type="given">Khalil</namePart>
            <namePart type="family">Sima’an</namePart>
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    <abstract>The identification of textual items, or documents, that best match a user’s information need, as expressed in search queries, forms the core functionality of information retrieval systems. Well-known challenges are associated with understanding the intent behind user queries; and, more importantly, with matching inherently-ambiguous queries to documents that may employ lexically different phrases to convey the same meaning. The conversion of semi-structured content from Wikipedia and other resources into structured data produces knowledge potentially more suitable to database-style queries and, ideally, to use in information retrieval. In parallel, the availability of textual documents on the Web enables an aggressive push towards the automatic acquisition of various types of knowledge from text. Methods developed under the umbrella of open-domain information extraction acquire open-domain classes of instances and relations from Web text. The methods operate over unstructured or semi-structured text available within collections of Web documents, or over relatively more intriguing streams of anonymized search queries. Some of the methods import the automatically-extracted data into human-generated resources, or otherwise exploit existing human-generated resources. In both cases, the goal is to expand the coverage of the initial resources, thus providing information about more of the topics that people in general, and Web search users in particular, may be interested in.</abstract>
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%0 Conference Proceedings
%T Knowledge Acquisition for Web Search
%A Pasca, Marius
%Y Li, Wenjie
%Y Sima’an, Khalil
%S Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts
%D 2015
%8 September
%I Association for Computational Linguistics
%C Lisbon, Portugal
%F pasca-2015-knowledge
%X The identification of textual items, or documents, that best match a user’s information need, as expressed in search queries, forms the core functionality of information retrieval systems. Well-known challenges are associated with understanding the intent behind user queries; and, more importantly, with matching inherently-ambiguous queries to documents that may employ lexically different phrases to convey the same meaning. The conversion of semi-structured content from Wikipedia and other resources into structured data produces knowledge potentially more suitable to database-style queries and, ideally, to use in information retrieval. In parallel, the availability of textual documents on the Web enables an aggressive push towards the automatic acquisition of various types of knowledge from text. Methods developed under the umbrella of open-domain information extraction acquire open-domain classes of instances and relations from Web text. The methods operate over unstructured or semi-structured text available within collections of Web documents, or over relatively more intriguing streams of anonymized search queries. Some of the methods import the automatically-extracted data into human-generated resources, or otherwise exploit existing human-generated resources. In both cases, the goal is to expand the coverage of the initial resources, thus providing information about more of the topics that people in general, and Web search users in particular, may be interested in.
%U https://aclanthology.org/D15-2004

Markdown (Informal)

[Knowledge Acquisition for Web Search](https://aclanthology.org/D15-2004) (Pasca, EMNLP 2015)

• Knowledge Acquisition for Web Search (Pasca, EMNLP 2015)

ACL

• Marius Pasca. 2015. Knowledge Acquisition for Web Search. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts, Lisbon, Portugal. Association for Computational Linguistics.