TY - JOUR ID - 520 TI - An improved joint model: POS tagging and dependency parsing JO - Journal of AI and Data Mining JA - JADM LA - en SN - 2322-5211 AU - Pakzad, A. AU - Minaei Bidgoli, B. AD - Department of Computer Engineering, Iran University of Science & Technology, Tehran, Iran. Y1 - 2016 PY - 2016 VL - 4 IS - 1 SP - 1 EP - 8 KW - Joint model KW - Part-Of-Speech KW - Dependency Parsing KW - Persian Language DO - 10.5829/idosi.JAIDM.2016.04.01.01 N2 - 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. UR - https://jad.shahroodut.ac.ir/article_520.html L1 - https://jad.shahroodut.ac.ir/article_520_230b4a2f3d31d53d426520ccb2cbed98.pdf ER -