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        <title>A Neural Transition-based Model for Nested Mention Recognition</title>
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        <namePart type="given">Bailin</namePart>
        <namePart type="family">Wang</namePart>
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    <name type="personal">
        <namePart type="given">Wei</namePart>
        <namePart type="family">Lu</namePart>
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    <name type="personal">
        <namePart type="given">Yu</namePart>
        <namePart type="family">Wang</namePart>
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    <name type="personal">
        <namePart type="given">Hongxia</namePart>
        <namePart type="family">Jin</namePart>
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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>
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        <name type="personal">
            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>It is common that entity mentions can contain other mentions recursively. This paper introduces a scalable transition-based method to model the nested structure of mentions. We first map a sentence with nested mentions to a designated forest where each mention corresponds to a constituent of the forest. Our shift-reduce based system then learns to construct the forest structure in a bottom-up manner through an action sequence whose maximal length is guaranteed to be three times of the sentence length. Based on Stack-LSTM which is employed to efficiently and effectively represent the states of the system in a continuous space, our system is further incorporated with a character-based component to capture letter-level patterns. Our model gets the state-of-the-art performances in ACE datasets, showing its effectiveness in detecting nested mentions.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1124</identifier>
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        <date>2018-oct-nov</date>
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            <start>1011</start>
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%0 Conference Proceedings
%T A Neural Transition-based Model for Nested Mention Recognition
%A Wang, Bailin
%A Lu, Wei
%A Wang, Yu
%A Jin, Hongxia
%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 wang-etal-2018-neural-transition
%X It is common that entity mentions can contain other mentions recursively. This paper introduces a scalable transition-based method to model the nested structure of mentions. We first map a sentence with nested mentions to a designated forest where each mention corresponds to a constituent of the forest. Our shift-reduce based system then learns to construct the forest structure in a bottom-up manner through an action sequence whose maximal length is guaranteed to be three times of the sentence length. Based on Stack-LSTM which is employed to efficiently and effectively represent the states of the system in a continuous space, our system is further incorporated with a character-based component to capture letter-level patterns. Our model gets the state-of-the-art performances in ACE datasets, showing its effectiveness in detecting nested mentions.
%R 10.18653/v1/D18-1124
%U https://aclanthology.org/D18-1124
%U https://doi.org/10.18653/v1/D18-1124
%P 1011-1017

Markdown (Informal)

[A Neural Transition-based Model for Nested Mention Recognition](https://aclanthology.org/D18-1124) (Wang et al., EMNLP 2018)

• A Neural Transition-based Model for Nested Mention Recognition (Wang et al., EMNLP 2018)

ACL

• Bailin Wang, Wei Lu, Yu Wang, and Hongxia Jin. 2018. A Neural Transition-based Model for Nested Mention Recognition. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1011–1017, Brussels, Belgium. Association for Computational Linguistics.