Research on Semantic Feature Disambiguation Based on Improved Collaborative Filtering Algorithm

Abstract

Artificial intelligence is developing rapidly, and the amount of information data is also increasing. Human-computer interaction technology can use computers to understand and process massive amounts of large data texts and accurately locate useful information. Moreover, it is very difficult to achieve smooth human-computer interaction. The main reason for the difficulty is that there are various ambiguities in natural language. Therefore, a noun phrase referential disambiguation model is established by improving the convolutional collaborative filtering algorithm in the paper, where the hand-crafted features are applied to clean up data to remove redundancy, and the remaining correct data is used as the input of the model. After experiments, the MACSSR algorithm and the CSSR algorithm are used for comparative testing under different M directs. It proves that the research content of this paper is superior to the comparative test method, and can extract semantic dependencies more quickly and effectively, which contributes to the intelligent semantic disambiguation technology.