Character-Level C hinese Dependency Parsing via Modeling Latent Intra-Word Structure (2024)

Abstract: Revealing the syntactic structure of sentences in Chinese poses significant challenges for word-level parsers due to the absence of clear word boundaries. To facilitate a transition from word-level to character-level Chinese dependency parsing, this paper proposes modeling latent internal structures within words. In this way, each word-level dependency tree is interpreted as a forest of character-level trees. A constrained Eisner algorithm is implemented to ensure the compatibility of character-level trees, guaranteeing a single root for intra-word structures and establishing inter-word dependencies between these roots. Experiments on Chinese treebanks demonstrate the superiority of our method over both the pipeline framework and previous joint models. A detailed analysis reveals that a coarse-to-fine parsing strategy empowers the model to predict more linguistically plausible intra-word structures.

## introduction in the field of natural language processing, dependency parsing plays a crucial role in revealing the syntactic structure of sentences, thereby forming the foundation for numerous downstream applications such as machine translation @xcite , information extraction @xcite , and sentiment analysis @xcite . this task, although straightforward in spacedelimited languages, encounters significant challenges in languages like chinese, where explicit word boundaries are absent. traditional chinese parsing methods rely heavily on word-level treebanks, necessitating the segmentation of text into distinct words before parsing. this prerequisite not only adds an additional layer of complexity * corresponding author but also makes the parsing outcome vulnerable to inaccuracies in segmentation. the need to address these issues has prompted a transition from word-level to character-level chinese dependency parsing. however, the lack of character-level chinese treebanks presents a challenge. as a workaround, researchers have endeavored to derive character-level dependency trees from word-level ones @xcite @xcite @xcite @xcite . @xcite pioneered the integration of character-and word-level annotations. figure 1 demonstrates a fully depicted character-level dependency tree (figure 1b ) by combining the word-level tree (figure 1a ) with annotated intra-word structures. nonetheless, the application of this method is constrained by the non-trivial task of deriving linguistically coherent intra-word structures. some researchers have opted for a simpler approach by defining pseudo intra-word structures @xcite . as illustrated in figure 1c , these structures utilize a left-wavy pattern, with the rightmost character acting as the root and other characters headed by their right-adjacent characters. although this method circumvents the labor-intensive annotation process, it may not accurately represent the syntactic roles of characters. this paper proposes a new approach to characterlevel chinese dependency parsing via modeling latent intra-word structure. as illustrated in figure 1d , our approach allows for the implicit representation of all potential internal structures within words. for example, for the word "发 展 (develop)", both "发 (grow)→展 (expand)" and "发 (grow)←展 (expand)" are acceptable structures. in this way, each word-level dependency tree is interpreted as a forest of character-level trees. central to our approach is a constrained eisner algorithm @xcite , crafted to maintain the compatibility of character-level trees it generates. ## model notations. given a sentence @xmath0. . . c n , where c i represents the ith character of x and c 0 denotes an artificial root token, a labeled depen-link(wi,@xmath1: link(wi,@xmath2: link(wi,@xmath3: we present only r-rules, omitting the symmetric l-rules and initial conditions for brevity. ## experiments data. we conduct experiments on three versions of the penn chinese treebank (ctb): ctb5, ctb6, and ctb7. 4 the split of train, development, and test sets follows established practices @xcite @xcite . evaluation metrics. for chinese word segmentation (cws), we employ standard f1 measures (f1 seg ). for dependency parsing, evaluation is conducted at the word level, using word-level f1 scores (uf dep and lf dep ) as the evaluation metrics @xcite . a dependency arc is considered correct only if the head-modifier word pair is correctly segmented. punctuation is excluded during the evaluation of dependency parsing. baseline and proposed models.