An Algorithm for Automatic Recognition of Chinese Spatial Prepositional Phrases for Academic Texts

Abstract

Automatic recognition of prepositional phrases has always been pursued in English translation, and its accurate recognition is crucial for various activities and applications in the field of natural language processing. This paper proposes an automatic recognition algorithm for Chinese spatial prepositional phrases in academic texts, which can not only identify parallel prepositional phrases but also improve the recognition accuracy of nested prepositional phrases. First, the method employs a simple noun phrase recognition model to identify and fuse phrase information contained in the corpus, thereby simplifying the corpus and minimizing the internal complexity of prepositional phrases. Second, the method uses the CRF model to identify nested inner prepositional phrases; if there is nesting, it identifies the inner layer of nesting; if there is no nesting, it identifies prepositional phrases; finally, it fuses the inner prepositions identified in the initial corpus phrase, modifies its feature information, and retrains the foreign preposition phrase recognition model for recognition. After the internal and external prepositional phrases are automatically identified, the double error correction method is used to correct the identified prepositional phrases. The experimental analysis shows that the accuracy rate, recall rate, and $F$ -value of the method for identifying prepositional phrases are 95.33%, 94.32%, and 94.73%, respectively, which are higher than those of the other methods for identifying prepositional phrases, which effectively improves the recognition rate of prepositional phrases.