Automatic Recognition and Extraction of English Verb Types Based on Index Line Clustering

Abstract

Languages are not uniform and certain words are used differently by speakers of different languages more or less often, or with distinct meanings. In both linguistics and natural language processing (NLP) problems, the classification that groups together verbs and a collection of similar syntactic and semantic features are of great interest. In the modern era of science and technology, NLP technology is developing rapidly. However, the interpretation of index lines still needs to be realized manually. This method takes a long time, especially after entering the era of big data, the number of corpora has increased rapidly and it is normal to have a corpus with hundreds of millions of words. The quantity of text generated every day is increasing intensely and the word index based on search words is as high as tens of thousands of lines, so it is very difficult to analyze index lines manually. Automatic lexical knowledge acquisition is essential for a variety of NLP activities. Particularly knowledge about verbs is critical, which are the major source of relationship information in a sentence. Due to this issue, this study attempts to automatically identify and extract English verbs by index line clustering. Each index behavior can be regarded as microtext automatic clustering to realize the automatic identification and extraction of English verb forms. This study first focuses on the clustering index algorithm including the C-means clustering algorithm and fuzzy C-means clustering algorithm, then describes in detail the automatic recognition and extraction process of English verbs based on index line clustering, and creates a verification set and completes the index line clustering of English verbs. Finally, the effect of index line algorithm is analyzed from two aspects: automatic recognition of English verb types and recall rate. At the same time, the verbs are selected to analyze their types and judge the probability of each type. The experimental results show that the average recognition rate of English verbs in the manual classification is 91.01%, and the average accuracy of automatic recognition and extraction of English verb patterns based on index row clustering is 95.99%.