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        <title>Scalable graph-based method for individual named entity identification</title>
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            <title>Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)</title>
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        <name type="personal">
            <namePart type="given">Dmitry</namePart>
            <namePart type="family">Ustalov</namePart>
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        <name type="personal">
            <namePart type="given">Swapna</namePart>
            <namePart type="family">Somasundaran</namePart>
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        <name type="personal">
            <namePart type="given">Peter</namePart>
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        <name type="personal">
            <namePart type="given">Mihai</namePart>
            <namePart type="family">Surdeanu</namePart>
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    <abstract>In this paper, we consider the named entity linking (NEL) problem. We assume a set of queries, named entities, that have to be identified within a knowledge base. This knowledge base is represented by a text database paired with a semantic graph, endowed with a classification of entities (ontology). We present state-of-the-art methods in NEL, and propose a new method for individual identification requiring few annotated data samples. We demonstrate its scalability and performance over standard datasets, for several ontology configurations. Our approach is well-motivated for integration in real systems. Indeed, recent deep learning methods, despite their capacity to improve experimental precision, require lots of parameter tuning along with large volume of annotated data.</abstract>
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    <identifier type="doi">10.18653/v1/D19-5303</identifier>
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%0 Conference Proceedings
%T Scalable graph-based method for individual named entity identification
%A Khalife, Sammy
%A Vazirgiannis, Michalis
%Y Ustalov, Dmitry
%Y Somasundaran, Swapna
%Y Jansen, Peter
%Y Glavaš, Goran
%Y Riedl, Martin
%Y Surdeanu, Mihai
%Y Vazirgiannis, Michalis
%S Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong
%F khalife-vazirgiannis-2019-scalable
%X In this paper, we consider the named entity linking (NEL) problem. We assume a set of queries, named entities, that have to be identified within a knowledge base. This knowledge base is represented by a text database paired with a semantic graph, endowed with a classification of entities (ontology). We present state-of-the-art methods in NEL, and propose a new method for individual identification requiring few annotated data samples. We demonstrate its scalability and performance over standard datasets, for several ontology configurations. Our approach is well-motivated for integration in real systems. Indeed, recent deep learning methods, despite their capacity to improve experimental precision, require lots of parameter tuning along with large volume of annotated data.
%R 10.18653/v1/D19-5303
%U https://aclanthology.org/D19-5303
%U https://doi.org/10.18653/v1/D19-5303
%P 17-25

Markdown (Informal)

[Scalable graph-based method for individual named entity identification](https://aclanthology.org/D19-5303) (Khalife & Vazirgiannis, TextGraphs 2019)

• Scalable graph-based method for individual named entity identification (Khalife & Vazirgiannis, TextGraphs 2019)

ACL

• Sammy Khalife and Michalis Vazirgiannis. 2019. Scalable graph-based method for individual named entity identification. In Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13), pages 17–25, Hong Kong. Association for Computational Linguistics.