Structure Modeling Approach for UD Parsing of Historical M odern J apanese (2025)

Abstract: This study shows the effectiveness of structure modeling for transfer ability in diachronic syntactic parsing. The syntactic parsing for historical languages is significant from a humanities and quantitative linguistics perspective to enable annotation support and analysis on unannotated documents.We compared the zero-shot transfer ability between Transformer-based Biaffine UD parsers and our structure modeling approach. The structure modeling approach is a pipeline method consisting with dictionary-based morphological analysis (MeCab), a deep learning-based phrase (bunsetsu) analysis (Monaka), SVM-based phrase dependency parsing (CaboCha) and a rule-based conversion from phrase dependencies to UD.This pipeline closely follows the methodology used in constructing Japanese UD corpora.Experimental results showed that the structure modeling approach outperformed zero-shot transfer from the contemporary to the modern Japanese. Moreover, the structure modeling approach outperformed several existing UD parsers in contemporary Japanese. To this end, the structure modeling approach outperformed in the diachronic transfer of Japanese by a wide margin and was useful to those applications for digital humanities and quantitative linguistics.

## introduction dependency parsing has long been studied as a core task in natural language processing. in recent years, dependency annotation corpora created under multilingual unified annotation frameworks such as universal dependencies (ud; zeman et al. 2018) have been published, enabling the development of deep learning-based dependency parsers that operate across multiple languages. on the other hand, syntactic structure analysis, including dependency parsing, is beneficial for humanities research as well as traditional nlp applications. this is because structure modeling, which consists of a layered pipeline of nlp tasks, can preserve low-level linguistic structures such as phrases required for humanities research. in contrast, the high zero-shot transfer performance of recent deep learning-based parsers @xcite would also be helpful to support annotation tasks and quantitative linguistic analysis on unannotated corpora such as historical literature, which no longer has native speakers. thus, this study focuses on the diachronic application of ud dependency parsing and compares structure modeling with end-to-end deep learning in the context of zero-shot transfer. specifically, japanese ud corpora exist for both contemporary and meiji-period (modern) japanese, allowing us to investigate the transfer performance from contemporary japanese, which has sufficient training resources, to modern japanese. the research questions regarding the structure modeling in this context are: (1) whether it demonstrates a performance advantage and (2) whether it is effective when considering practical annotation and application use cases. for our structure modeling approach to japanese ud parsing (see figure 1 ), we first applied a morphological analysis (mecab), and then, we applied deep learning-based phrase (bunsetsu) segmentation (monaka) and bunsetsu dependency parsing (cabocha). after this, we employed a rule-based transformation from bunsetsu dependencies to ud annotation, simulating actual japanese ud annotations. this structure modeling approach closely follows the standard workflow used for constructing japanese ud linguistic resources, involving morphological annotation, bunsetsu dependency annotation, and subsequent rule-based conversion into the ud format. as a comparison, we trained and used a graph-based biaffine parser @xcite , which is a representative ud parsing method. ## structure modeling approach for ud parsing in standard japanese ud annotation @xcite , bunsetsu dependency information is used as a basis for rulebased conversion into ud annotation, referencing the suws and luws contained within each bunsetsu. therefore, by estimating the suw, luw, and bunsetsu boundaries, along with the dependency relations between bunsetsu, it is possible to obtain ud parsing results by applying the same conversion rules. figure 1 shows the pipeline of the structure modeling approach. the pipeline starts from a crf-based suw analysis (mecab), and then the results of suw analysis are sent to deep learning-based luw and bunsetsu analysis (monaka). next, all suw, luw and bunsetsu information are sent to bunsetsu dependency parsing (cabocha), and finally, with all the information, rule-based ud conversion is performed. ## conclusion this study shows the effectiveness of structure modeling for transfer ability in diachronic syntactic parsing. we compared the zero-shot transfer ability between transformer-based biaffine ud parsers and our structure modeling approach. the structure modeling approach is a pipeline method consisting of dictionary-based morphological analysis (mecab), a deep learning-based phrase (bunsetsu) analysis (monaka), svm-based phrase dependency parsing (cabocha), and a rulebased conversion from phrase dependencies to ud, which closely follows the methodology used in constructing japanese ud corpora. experimental results showed that the structure modeling approach outperformed zero-shot transfer from the contemporary to the modern japanese by a wide margin. the structure modeling approach outperformed several existing ud parsers in contemporary japanese. moreover, for other languages as well, it may be beneficial to adopt an analysis approach based on an understanding of resource construction methods, such as how ud resources are created or how parsed trees are transformed into ud format using head rules. from a case study, the structure modeling approach can preserve lowlevel information such as morphology and phrases (bunsetsu). on the other hand, the biaffine parser has slightly better transfer performances of case relations. to this end, the structure modeling performed well on diachronic transfer in japanese.