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        <title>Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs</title>
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    <name type="personal">
        <namePart type="given">Dimitri</namePart>
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        <namePart type="given">Mohammad</namePart>
        <namePart type="given">Taher</namePart>
        <namePart type="family">Pilehvar</namePart>
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    <name type="personal">
        <namePart type="given">Nigel</namePart>
        <namePart type="family">Collier</namePart>
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        <dateIssued>2018-oct-nov</dateIssued>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Chiang</namePart>
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            <namePart type="given">Julia</namePart>
            <namePart type="family">Hockenmaier</namePart>
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            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>This paper addresses the problem of mapping natural language text to knowledge base entities. The mapping process is approached as a composition of a phrase or a sentence into a point in a multi-dimensional entity space obtained from a knowledge graph. The compositional model is an LSTM equipped with a dynamic disambiguation mechanism on the input word embeddings (a Multi-Sense LSTM), addressing polysemy issues. Further, the knowledge base space is prepared by collecting random walks from a graph enhanced with textual features, which act as a set of semantic bridges between text and knowledge base entities. The ideas of this work are demonstrated on large-scale text-to-entity mapping and entity classification tasks, with state of the art results.</abstract>
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        <date>2018-oct-nov</date>
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            <start>1959</start>
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%0 Conference Proceedings
%T Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs
%A Kartsaklis, Dimitri
%A Pilehvar, Mohammad Taher
%A Collier, Nigel
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F kartsaklis-etal-2018-mapping
%X This paper addresses the problem of mapping natural language text to knowledge base entities. The mapping process is approached as a composition of a phrase or a sentence into a point in a multi-dimensional entity space obtained from a knowledge graph. The compositional model is an LSTM equipped with a dynamic disambiguation mechanism on the input word embeddings (a Multi-Sense LSTM), addressing polysemy issues. Further, the knowledge base space is prepared by collecting random walks from a graph enhanced with textual features, which act as a set of semantic bridges between text and knowledge base entities. The ideas of this work are demonstrated on large-scale text-to-entity mapping and entity classification tasks, with state of the art results.
%R 10.18653/v1/D18-1221
%U https://aclanthology.org/D18-1221
%U https://doi.org/10.18653/v1/D18-1221
%P 1959-1970

Markdown (Informal)

[Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs](https://aclanthology.org/D18-1221) (Kartsaklis et al., EMNLP 2018)

• Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs (Kartsaklis et al., EMNLP 2018)

ACL

• Dimitri Kartsaklis, Mohammad Taher Pilehvar, and Nigel Collier. 2018. Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1959–1970, Brussels, Belgium. Association for Computational Linguistics.