Volume 12 (2024)
Volume 11 (2023)
Volume 10 (2022)
Volume 9 (2021)
Volume 8 (2020)
Volume 7 (2019)
Volume 6 (2018)
Volume 5 (2017)
Volume 4 (2016)
Volume 3 (2015)
Volume 2 (2014)
Volume 1 (2013)
H.3.8. Natural Language Processing
A Transformer-based Approach for Persian Text Chunking

P. Kavehzadeh; M. M. Abdollah Pour; S. Momtazi

Volume 10, Issue 3 , July 2022, , Pages 373-383


  Over the last few years, text chunking has taken a significant part in sequence labeling tasks. Although a large variety of methods have been proposed for shallow parsing in English, most proposed approaches for text chunking in Persian language are based on simple and traditional concepts. In this paper, ...  Read More

H.3.8. Natural Language Processing
A Novel Approach to Conditional Random Field-based Named Entity Recognition using Persian Specific Features

L. Jafar Tafreshi; F. Soltanzadeh

Volume 8, Issue 2 , April 2020, , Pages 227-236


  Named Entity Recognition is an information extraction technique that identifies name entities in a text. Three popular methods have been conventionally used namely: rule-based, machine-learning-based and hybrid of them to extract named entities from a text. Machine-learning-based methods have good performance ...  Read More

H.3.8. Natural Language Processing
Feature Engineering in Persian Dependency Parser

S. Lazemi; H. Ebrahimpour-komleh

Volume 7, Issue 3 , July 2019, , Pages 467-474


  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, ...  Read More

H.3.8. Natural Language Processing
Improvement of Chemical Named Entity Recognition through Sentence-based Random Under-sampling and Classifier Combination

A. Akkasi; E. Varoglu

Volume 7, Issue 2 , April 2019, , Pages 311-319


  Chemical Named Entity Recognition (NER) is the basic step for consequent information extraction tasks such as named entity resolution, drug-drug interaction discovery, extraction of the names of the molecules and their properties. Improvement in the performance of such systems may affects the quality ...  Read More

H.3.8. Natural Language Processing
Extraction of Drug-Drug Interaction from Literature through Detecting Linguistic-based Negation and Clause Dependency

B. Bokharaeian; A. Diaz

Volume 4, Issue 2 , July 2016, , Pages 203-212


  Extracting biomedical relations such as drug-drug interaction (DDI) from text is an important task in biomedical NLP. Due to the large number of complex sentences in biomedical literature, researchers have employed some sentence simplification techniques to improve the performance of the relation extraction ...  Read More

H.3.8. Natural Language Processing
An improved joint model: POS tagging and dependency parsing

A. Pakzad; B. Minaei Bidgoli

Volume 4, Issue 1 , March 2016, , Pages 1-8


  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 ...  Read More

H.3.8. Natural Language Processing
Comparing k-means clusters on parallel Persian-English corpus

A. Khazaei; M. Ghasemzadeh

Volume 3, Issue 2 , July 2015, , Pages 203-208


  This paper compares clusters of aligned Persian and English texts obtained from k-means method. Text clustering has many applications in various fields of natural language processing. So far, much English documents clustering research has been accomplished. Now this question arises, are the results of ...  Read More