Classical Chinese poems of Tang and Song dynasties are an important part for the studies of Chinese literature. To thoroughly understand the poems, properly segmenting the verses is an important step for human readers and software agents. Yet, due to the availability of data and the costs of annotation, there are still no known large and useful sources that offer classical Chinese poems with annotated word boundaries. In this project, annotators with Chinese literature background labeled 32399 poems. We analyzed the annotated patterns and conducted inter-rater agreement studies about the annotations. The distributions of the annotated patterns for poem lines are very close to some well-known professional heuristics, i.e., that the 2-2-1, 2-1-2, 2-2-1-2, and 2-2-2-1 patterns are very frequent. The annotators agreed well at the line level, but agreed on the segmentations of a whole poem only 43% of the time. We applied a traditional machine-learning approach to segment the poems, and achieved promising results at the line level as well. Using the annotated data as the ground truth, these methods could segment only about 18% of the poems completely right under favorable conditions. Switching to deep-learning methods helped us achieved better than 30%.

Providing structural information about civil cases for judgement prediction systems or recommendation systems can enhance the efficiency of the inference procedures and the justifiability of produced results. In this research, we focus on the civil cases about alimony, which is a relatively uncommon choice in current applications of artificial intelligence in law. We attempt to identify the statements for four types of legal functions in judgement documents, i.e., the pleadings of the applicants, the responses of the opposite parties, the opinions of the courts, and uses of laws to reach the final decisions. In addition, we also try to identify the conflicting issues between the plaintiffs and the defendants in the judgement documents.

The needs for mediation are increasing rapidly along with the increasing number of cases of the alimony for the elderly in recent years. Offering a prediction mechanism for predicting the outcomes of some prospective lawsuits may alleviate the workload of the mediation courts. This research aims to offer the predictions for the judgments and the granted alimony for the plaintiffs of such civil cases in Chinese, based on our analysis of results of the past lawsuits. We hope that the results can be helpful for both the involved parties and the courts. To build the current system, we segment and vectorize the texts of the judgement documents, and apply the logistic regression and model tree models for predicting the judgments and for estimating the granted alimony of the cases, respectively.

Similar judgments search is an important task in legal practice, from which valuable legal insights can be obtained. Issues are disputes between both parties in civil litigation, which represents the core topics to be considered in the trials. Many studies calculate the similarity between judgments from different perspectives and methods. We first cluster the issues in the judgments, and then encode the judgments with vectors for whether or not the judgments contain issues in the corresponding clusters. The similarity between the judgments are evaluated based on the encoded messages. We verify the effectiveness of the system with a human scoring process by a legal background assistant, while comparing the effects of several combinations of preprocessing steps and selections of clustering strategies.

Named Entity Recognition (NER) tools have been in development for years, yet few have been aimed at medical documents. The increasing needs for analyzing medical data makes it crucial to build a sophisticated NER model for this missing area. In this paper, W2NER, the state-of-the-art NER model, which has excelled in English and Chinese tasks, is run through selected inputs, several pretrained language models, and training strategies. The objective was to build an NER model suitable for healthcare corpora in Chinese. The best model managed to achieve an F1 score at 81.93%, which ranked first in the ROCLING 2022 shared task.

(This is the abstract for the submission.) Large-scale comparisons between the poetry of Tang and Song dynasties shed light on how words and expressions were used and shared among the poets. That some words were used only in the Tang poetry and some only in the Song poetry could lead to interesting research in linguistics. That the most frequent colors are different in the Tang and Song poetry provides a trace of the changing social circumstances in the dynasties. Results of the current work link to research topics of lexicography, semantics, and social transitions. We discuss our findings and present our algorithms for efficient comparisons among the poems, which are crucial for completing billion times of comparisons within acceptable time.