Abstract: In order to solve the problems of the relevant personnel in the case, such as miscellaneous handling of litigation cases, scattered information collection, and long time for handling the case, a model for extracting key elements of traffic accident litigation cases is proposed based on Conditional Random Fields (CRF). The model uses information extraction technology to design different feature templates by constructing key element tagging set and building corpus. Fully combining the text characteristics of litigation cases in the field of traffic accidents, considering the window length and the selection and combination of different features, the traffic accident litigation case is trained and tested based on the PyCharm platform. The experimental results show that the optimal feature template can extract the key elements in traffic accident litigation cases with an F1 value of 80.15%, and different word segmentation tools have an impact on the key element identification results. The proposed model is an effective exploration and attempt to give a fair and just judgment result quickly and correctly.

Key words: litigation cases, CRF, key elements, feature templates