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        <title>Learning Features from Co-occurrences: A Theoretical Analysis</title>
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        <namePart type="given">Yanpeng</namePart>
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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            <namePart type="given">Emily</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Bender</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Leon</namePart>
            <namePart type="family">Derczynski</namePart>
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            <namePart type="given">Pierre</namePart>
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    <abstract>Representing a word by its co-occurrences with other words in context is an effective way to capture the meaning of the word. However, the theory behind remains a challenge. In this work, taking the example of a word classification task, we give a theoretical analysis of the approaches that represent a word X by a function f(P(C|X)), where C is a context feature, P(C|X) is the conditional probability estimated from a text corpus, and the function f maps the co-occurrence measure to a prediction score. We investigate the impact of context feature C and the function f . We also explain the reasons why using the co-occurrences with multiple context features may be better than just using a single one. In addition, based on the analysis, we propose a hypothesis about the conditional probability on zero probability events.</abstract>
    <identifier type="citekey">li-2018-learning</identifier>
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        <url>https://aclanthology.org/C18-1241</url>
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        <date>2018-08</date>
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            <start>2846</start>
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%0 Conference Proceedings
%T Learning Features from Co-occurrences: A Theoretical Analysis
%A Li, Yanpeng
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F li-2018-learning
%X Representing a word by its co-occurrences with other words in context is an effective way to capture the meaning of the word. However, the theory behind remains a challenge. In this work, taking the example of a word classification task, we give a theoretical analysis of the approaches that represent a word X by a function f(P(C|X)), where C is a context feature, P(C|X) is the conditional probability estimated from a text corpus, and the function f maps the co-occurrence measure to a prediction score. We investigate the impact of context feature C and the function f . We also explain the reasons why using the co-occurrences with multiple context features may be better than just using a single one. In addition, based on the analysis, we propose a hypothesis about the conditional probability on zero probability events.
%U https://aclanthology.org/C18-1241
%P 2846-2854

Markdown (Informal)

[Learning Features from Co-occurrences: A Theoretical Analysis](https://aclanthology.org/C18-1241) (Li, COLING 2018)

• Learning Features from Co-occurrences: A Theoretical Analysis (Li, COLING 2018)

ACL

• Yanpeng Li. 2018. Learning Features from Co-occurrences: A Theoretical Analysis. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2846–2854, Santa Fe, New Mexico, USA. Association for Computational Linguistics.