T ibetan Dependency Parsing with Graph Convolutional Neural Networks (2023)

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.

## introduction in recent years, the explosive growth of tibetan text data, fueled by the popularization of information technology in tibetan areas, has made the processing and deeper understanding of tibetan information a hot research topic in tibetan natural language processing (nlp) @xcite . dependency analysis is an essential task for the semantic modeling of texts, as it provides a basis for deep semantic analysis and has significant research and practical value. the results of dependency analysis can be directly applied to numerous basic natural language processing tasks, such as question answering @xcite , sentiment analysis @xcite , and named entity recognition @xcite . traditional tibetan dependency analysis methods can be mainly classified into two categories: (1) statistical learning-based methods @xcite , which usually require experts to design corresponding rules and features and then use statistical learning models to model and predict dependency syntax. this type of method heavily relies on tibetan linguistic experts. (2) deep learning-based methods @xcite , which have been widely applied in tibetan information processing, such as word segmentation, text classification, and dependency analysis, with the rapid development of deep learning. the major advantage of deep learning-based methods is that they do not require expert features. tibetan dependency analysis can be achieved through a fixed network structure and annotated data. however, the methods above model tibetan dependency analysis as a classification or transition problem, ignoring the features of graph data in dependency analysis. graph data features can better model the relationships between different words and ignore the distance between words in the text, i.e., they can model the dependency information between words that are far apart. they can also model higher-order relationships through indirect relationships between words, which significantly impacts modeling word relationships, such as amr (abstract meaning representation) @xcite . this paper presents a method for tibetan dependency analysis based on graph convolutional neural networks. tibetan word representations are modeled using bert @xcite and bilstm, followed by graph neural networks(gnn) @xcite for modeling dependency relationships between words. mlp is then employed for relationship classification and determining the dependency relationship types. the results on tibetan dependency analysis data indicate that gnn can significantly enhance the performance of tibetan dependency analysis, thus affirming the value of graph information. the main contributions of our work are as follows: (1) we propose using graph convolutional neural networks (gcn) to model the dependency relationships in tibetan sentences. (2) experimental results show that the proposed method outperforms other methods, such as r-gcn @xcite , in tibetan dependency analysis, which may be due to insufficient training data. the main contributions are twofold: the rest of the paper is organized as follows: section 2 introduces some of the most related work, including tibetan dependency parsing models and graph neural networks. our proposed model is detailed described in section 3. section 4 shows our experimental results on the introduced tibetan dependency analysis dataset and presents the effects of different modules. we conclude our work in section 5. ## related work this section briefly reviews related work, including tibetan dependency parsing methods and neural-based methods for dependency parsing. ## experiments in this section, we conduct experiments of tibetan dependency analysis task on the dataset from @xcite . ## conclusion to address the issue of inadequate modeling of dependency graph information in current tibetan dependency analysis methods, this paper proposes a graph neural networkbased approach for tibetan dependency analysis. furthermore, a tibetan pre-trained language model is employed to improve the performance further. the experimental results demonstrate the effectiveness of the graph neural network and the tibetan pre-trained language model for enhancing tibetan dependency analysis. large models such as chat-gpt have recently achieved significant results in natural language processing tasks such as dialogue and knowledge extraction. in the future, we aim to explore large models for lowresource languages and their potential applications in low-resource scenarios.