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    <titleInfo>
        <title>Tibetan Dependency Parsing with Graph Convolutional Neural Networks</title>
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            <title>Proceedings of the Ancient Language Processing Workshop</title>
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            <namePart type="given">Adam</namePart>
            <namePart type="family">Anderson</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Shai</namePart>
            <namePart type="family">Gordin</namePart>
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        <name type="personal">
            <namePart type="given">Bin</namePart>
            <namePart type="family">Li</namePart>
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        <name type="personal">
            <namePart type="given">Yudong</namePart>
            <namePart type="family">Liu</namePart>
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            <publisher>INCOMA Ltd., Shoumen, Bulgaria</publisher>
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    <abstract>Dependency parsing is a syntactic analysis method to analyze the dependency relationships between words in a sentence. The interconnection between words through dependency relationships is typical graph data. Traditional Tibetan dependency parsing methods typically model dependency analysis as a transition-based or sequence-labeling task, ignoring the graph information between words. To address this issue, this paper proposes a graph neural network (GNN)-based Tibetan dependency parsing method. This method treats Tibetan words as nodes and the dependency relationships between words as edges, thereby constructing the graph data of Tibetan sentences. Specifically, we use BiLSTM to learn the word representations of Tibetan, utilize GNN to model the relationships between words and employ MLP to predict the types of relationships between words. We conduct experiments on a Tibetan dependency database, and the results show that the proposed method can achieve high-quality Tibetan dependency parsing results.</abstract>
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        <url>https://aclanthology.org/2023.alp-1.24</url>
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        <date>2023-09</date>
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            <start>213</start>
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%0 Conference Proceedings
%T Tibetan Dependency Parsing with Graph Convolutional Neural Networks
%A An, Bo
%Y Anderson, Adam
%Y Gordin, Shai
%Y Li, Bin
%Y Liu, Yudong
%Y Passarotti, Marco C.
%S Proceedings of the Ancient Language Processing Workshop
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F an-2023-tibetan
%X Dependency parsing is a syntactic analysis method to analyze the dependency relationships between words in a sentence. The interconnection between words through dependency relationships is typical graph data. Traditional Tibetan dependency parsing methods typically model dependency analysis as a transition-based or sequence-labeling task, ignoring the graph information between words. To address this issue, this paper proposes a graph neural network (GNN)-based Tibetan dependency parsing method. This method treats Tibetan words as nodes and the dependency relationships between words as edges, thereby constructing the graph data of Tibetan sentences. Specifically, we use BiLSTM to learn the word representations of Tibetan, utilize GNN to model the relationships between words and employ MLP to predict the types of relationships between words. We conduct experiments on a Tibetan dependency database, and the results show that the proposed method can achieve high-quality Tibetan dependency parsing results.
%U https://aclanthology.org/2023.alp-1.24
%P 213-221

Markdown (Informal)

[Tibetan Dependency Parsing with Graph Convolutional Neural Networks](https://aclanthology.org/2023.alp-1.24) (An, ALP-WS 2023)

• Tibetan Dependency Parsing with Graph Convolutional Neural Networks (An, ALP-WS 2023)

ACL

• Bo An. 2023. Tibetan Dependency Parsing with Graph Convolutional Neural Networks. In Proceedings of the Ancient Language Processing Workshop, pages 213–221, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.