A Study of Key Elements of Computer Linguistics Extraction Based on Artificial Intelligence NLP

Key element extraction is an important research field in computational linguistics. Based on the Hidden Markov Model in natural language processing technology, this paper utilizes the Viterbi decoding algorithm, along with its optimization and improvement algorithms, to construct a key element extraction model. This model then extracts the key legal elements from the original corpus of traffic collision litigation cases. To further validate the performance of this paper’s model, a public newspaper dataset released by a university was selected to deeply explore its effectiveness. This paper’s model significantly improves the accuracy and F1 values of the key elements of legal text extraction, reaching 93.28% and 90.83%, respectively, compared to all other models. The model’s extraction effect on the six key elements in the legal text reaches an ideal state, where the F1 value of the extracted element’s sentence results reaches 100%. In comparison to the HMM model in the public dataset, the model in this paper has improved by 10.93%, 8.78%, and 10.29% in the three indexes, indicating its superior performance in key element extraction.