ViraScience.
Article

Feature Engineering in Persian Dependency Parser

S. Lazemi, H. Ebrahimpour-komleh

Abstract

Dependency parser is one of the most important fundamental tools in the natural language processing, which extracts structure of sentences and determines the relations between words based on the dependency grammar. The dependency parser is proper for free order languages, such as Persian. In this paper, data-driven dependency parser has been developed with the help of phrase-structure parser for Persian. The defined feature space in each parser is one of the important factors in its success. Our goal is to generate and extract appropriate features to dependency parsing of Persian sentences. To achieve this goal, new semantic and syntactic features have been defined and added to the MSTParser by stacking method. Semantic features are obtained by using word clustering algorithms based on syntagmatic analysis and syntactic features are obtained by using the Persian phrase-structure parser and have been used as bit-string. Experiments have been done on the Persian Dependency Treebank (PerDT) and the Uppsala Persian Dependency Treebank (UPDT). The results indicate that the definition of new features improves the performance of the dependency parser for the Persian. The achieved unlabeled attachment score for PerDT and UPDT are 89.17% and 88.96% respectively.

Similar items

  • An Empirical Study on the Effect of Morphological and Lexical Features in Persian Dependency Parsing

    Scholarly record2013SPMRL@EMNLPMojtaba Khallash, Ali Hadian, Behrouz Minaei-Bidgoli

    This paper investigates the impact of different morphological and lexical information on data-driven dependency parsing of Persian, a morphologically rich language. We explore two state-of-the-art parsers, namely MSTParser andMaltParser, on the recently released Persian dependenc

  • Improving Persian Dependency-Based Parser Using Deep Learning

    Article2022Computer and Knowledge Engineeringsoghra lazemi, hossein Ebrahimpour-komleh, nasser Noroozi

    One of the most important problems in computational linguistics is the grammar and, consequently, syntactic structures and structural parsing. The structural parser tries to analyze the relationships between words and to extract the syntactic structure of the sentence. The depend

  • ParsPer: A Dependency Parser for Persian

    Scholarly record2015DepLingMojgan Seraji, Bernd Bohnet, Joakim Nivre

    We present a dependency parser for Persian, called ParsPer, developed using the graph-based parser in the Mate Tools. The parser is trained on the entire Uppsala Persian Dependency Treebank with a specific configuration that was selected by MaltParser as the best performing parsi

  • Dependency Parsers for Persian

    Scholarly record2012ALR@COLINGMojgan Seraji, Beáta Megyesi, Joakim Nivre

    We present two dependency parsers for Persian, MaltParser and MSTParser, trained on the Uppsala PErsian Dependency Treebank. The treebank consists of 1,000 sentences today. Its annotation scheme is based on Stanford Typed Dependencies (STD) extended for Persian with regard to obj

  • Feature Engineering in Maximum Spanning Tree Dependency Parser

    Scholarly record2007TSDVáclav Novák, Zdenek Zabokrtský

    In this paper we present the results of our experiments with modifications of the feature set used in the Czech mutation of the Maximum Spanning Tree parser. First we show how new feature templates improve the parsing accuracy and second we decrease the dimensionality of the feat

  • LSTM Feature Representation in Projective and Non-Projective Transition-Based Dependency Parsers

    Scholarly record2017Lena Reisinger

    The objective of the first part of my project was to investigate how allowing for nonprojectivity influences the attachment accuracy of transition-based dependency parsers. I implemented four parsers, two of which were based on novel transition systems. Since the novel non-projec