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Information Journal Paper

Title

PHRASE CHUNKING IN PERSIAN TEXTS

Pages

  69-86

Abstract

 Text tokenization is the process of tokenizing text to meaningful tokens such as words, phrases, sentences, etc. Tokenization of syntactical phrases named as chunking is an important preprocessing needed in many applications such as MACHINE TRANSLATION information retrieval, TEXT TO SPEECH, etc. In this paper chunking of Farsi texts is done using statistical and learning methods and the grammatical characteristics of Farsi texts. Many features and labeling methods are examined one by one and the best features and labeling techniques are used for the detection of syntactic phrases and their boundaries. Several machine learning techniques including SUPPORT VECTOR MACHINE and CONDITIONAL RANDOM FIELDS are used as classifier in our experiments. The impact of the size of training texts on chunking performance was studied as well. Using the proposed methods in this paper, a performance of 84.02% was obtained for detection of phrase boundaries and 78.04% for detection of both phrase boundaries and phrase type.

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    APA: Copy

    HOMAYOUNPOUR, MOHAMMAD MAHDI, & SALIMI BADR, ARMIN. (2013). PHRASE CHUNKING IN PERSIAN TEXTS. SIGNAL AND DATA PROCESSING, -(2 (SERIAL 20)), 69-86. SID. https://sid.ir/paper/160821/en

    Vancouver: Copy

    HOMAYOUNPOUR MOHAMMAD MAHDI, SALIMI BADR ARMIN. PHRASE CHUNKING IN PERSIAN TEXTS. SIGNAL AND DATA PROCESSING[Internet]. 2013;-(2 (SERIAL 20)):69-86. Available from: https://sid.ir/paper/160821/en

    IEEE: Copy

    MOHAMMAD MAHDI HOMAYOUNPOUR, and ARMIN SALIMI BADR, “PHRASE CHUNKING IN PERSIAN TEXTS,” SIGNAL AND DATA PROCESSING, vol. -, no. 2 (SERIAL 20), pp. 69–86, 2013, [Online]. Available: https://sid.ir/paper/160821/en

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