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        <title>Conical Classification For Efficient One-Class Topic Determination</title>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2021</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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        <name type="personal">
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            <namePart type="family">Yih</namePart>
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    <abstract>As the Internet grows in size, so does the amount of text based information that exists. For many application spaces it is paramount to isolate and identify texts that relate to a particular topic. While one-class classification would be ideal for such analysis, there is a relative lack of research regarding efficient approaches with high predictive power. By noting that the range of documents we wish to identify can be represented as positive linear combinations of the Vector Space Model representing our text, we propose Conical classification, an approach that allows us to identify if a document is of a particular topic in a computationally efficient manner. We also propose Normal Exclusion, a modified version of Bi-Normal Separation that makes it more suitable within the one-class classification context. We show in our analysis that our approach not only has higher predictive power on our datasets, but is also faster to compute.</abstract>
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    <identifier type="doi">10.18653/v1/2021.findings-emnlp.143</identifier>
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        <date>2021-11</date>
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            <start>1662</start>
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%0 Conference Proceedings
%T Conical Classification For Efficient One-Class Topic Determination
%A Khanna, Sameer
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F khanna-2021-conical-classification
%X As the Internet grows in size, so does the amount of text based information that exists. For many application spaces it is paramount to isolate and identify texts that relate to a particular topic. While one-class classification would be ideal for such analysis, there is a relative lack of research regarding efficient approaches with high predictive power. By noting that the range of documents we wish to identify can be represented as positive linear combinations of the Vector Space Model representing our text, we propose Conical classification, an approach that allows us to identify if a document is of a particular topic in a computationally efficient manner. We also propose Normal Exclusion, a modified version of Bi-Normal Separation that makes it more suitable within the one-class classification context. We show in our analysis that our approach not only has higher predictive power on our datasets, but is also faster to compute.
%R 10.18653/v1/2021.findings-emnlp.143
%U https://aclanthology.org/2021.findings-emnlp.143
%U https://doi.org/10.18653/v1/2021.findings-emnlp.143
%P 1662-1673

Markdown (Informal)

[Conical Classification For Efficient One-Class Topic Determination](https://aclanthology.org/2021.findings-emnlp.143) (Khanna, Findings 2021)

• Conical Classification For Efficient One-Class Topic Determination (Khanna, Findings 2021)

ACL

• Sameer Khanna. 2021. Conical Classification For Efficient One-Class Topic Determination. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1662–1673, Punta Cana, Dominican Republic. Association for Computational Linguistics.