Word and phrase segmentation is one of the main activities in natural languages processing (NLP). Many programs in NLP need to be preprocessed for extraction of text’s words and distinction phrases. Getting meaningful words with their prefix and suffix is the main and t More
Word and phrase segmentation is one of the main activities in natural languages processing (NLP). Many programs in NLP need to be preprocessed for extraction of text’s words and distinction phrases. Getting meaningful words with their prefix and suffix is the main and the final goal of segmentation. This activity depends on various natural languages can be easy or hard. Persian is among the languages with complex preprocessing tasks. One of the complexity sources is handling different writing scripts. In written Persian texts, we have two kinds of spaces: short space and white space. Also there are various scripts for writing Persian texts, differing in the style of writing words, using or elimination of spaces within or between words, using various forms of characters and so on.
In this paper, we want to suggest a statistical method for phrase segmentation on Persian texts using neural networks due to using in search engines. For this purpose, we use occurrence likelihood of uniwords and biwords in corpus. The suggested algorithm includes four steps and could detect about 89.6% of correct tokens. Experimental results show this method can improve the performance of the usual methods
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