AN IMPROVED JOINT MODEL: POS TAGGING AND DEPENDENCY PARSING

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PAKZAD A.; MINAEI BIDGOLI B.

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Journal: JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING Year:2016 | Volume:4 | Issue:1 Page(s): 1-8

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Information Journal Paper

Title

AN IMPROVED JOINT MODEL: POS TAGGING AND DEPENDENCY PARSING

Author(s)

PAKZAD A. | MINAEI BIDGOLI B. | Issue Writer Certificate

Keywords

JOINT MODELQ2

PART-OF-SPEECHQ2

DEPENDENCY PARSINGQ2

PERSIAN LANGUAGEQ2

Abstract

Dependency parsing is a way of syntactic parsing and a natural language that automatically analyzes the dependency structure of sentences, and the input for each sentence creates a dependency graph. Part-Of- Speech (POS) tagging is a prerequisite for DEPENDENCY PARSING. Generally, dependency parsers do the POS tagging task along with DEPENDENCY PARSING in a pipeline mode. Unfortunately, in pipeline models, a tagging error propagates, but the model is not able to apply useful syntactic information. The goal of JOINT MODELs simultaneously reduce errors of POS tagging and DEPENDENCY PARSING tasks. In this research, we attempted to utilize the JOINT MODEL on the Persian and English language using Corbit software. We optimized the model’s features and improved its accuracy concurrently. Corbit software is an implementation of a transition-based approach for word segmentation, POS tagging and DEPENDENCY PARSING. In this research, the joint accuracy of POS tagging and DEPENDENCY PARSING over the test data on Persian, reached 85.59% for coarse-grained and 84.24% for fine-grained POS. Also, we attained 76.01% for coarse-grained and 74.34% for fine-grained POS on English.

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APA: Copy

PAKZAD, A., & MINAEI BIDGOLI, B.. (2016). AN IMPROVED JOINT MODEL: POS TAGGING AND DEPENDENCY PARSING. JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING, 4(1), 1-8. SID. https://sid.ir/paper/255382/en

Vancouver: Copy

PAKZAD A., MINAEI BIDGOLI B.. AN IMPROVED JOINT MODEL: POS TAGGING AND DEPENDENCY PARSING. JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING[Internet]. 2016;4(1):1-8. Available from: https://sid.ir/paper/255382/en

IEEE: Copy

A. PAKZAD, and B. MINAEI BIDGOLI, “AN IMPROVED JOINT MODEL: POS TAGGING AND DEPENDENCY PARSING,” JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING, vol. 4, no. 1, pp. 1–8, 2016, [Online]. Available: https://sid.ir/paper/255382/en

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