Search Results for author: Behrouz Minaei-Bidgoli

Found 17 papers, 3 papers with code

Evaluating Persian Tokenizers

no code implementations22 Feb 2022 Danial Kamali, Behrooz Janfada, Mohammad Ebrahim Shenasa, Behrouz Minaei-Bidgoli

Natural Language Processing in Persian is challenging due to Persian's exceptional cases, such as half-spaces.

Language Modelling Lexical Analysis +1

ParsFEVER: a Dataset for Farsi Fact Extraction and Verification

1 code implementation Joint Conference on Lexical and Computational Semantics 2021 Majid Zarharan, Mahsa Ghaderan, Amin Pourdabiri, Zahra Sayedi, Behrouz Minaei-Bidgoli, Sauleh Eetemadi, Mohammad Taher Pilehvar

Training and evaluation of automatic fact extraction and verification techniques require large amounts of annotated data which might not be available for low-resource languages.

Interval Probabilistic Fuzzy WordNet

no code implementations4 Apr 2021 Yousef Alizadeh-Q, Behrouz Minaei-Bidgoli, Sayyed-Ali Hossayni, Mohammad-R Akbarzadeh-T, Diego Reforgiato Recupero, Mohammad-Reza Rajati, Aldo Gangemi

Utilizing our algorithm and the open-American-online-corpus (OANC) and UKB word-sense-disambiguation, we constructed and published the IPF synsets of WordNet for English language.

Word Sense Disambiguation

IUST at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text using Deep Neural Networks and Linear Baselines

no code implementations SEMEVAL 2020 Soroush Javdan, Taha Shangipour ataei, Behrouz Minaei-Bidgoli

Our group, with the name of IUST(username: TAHA), participated at the SemEval-2020 shared task 9 on Sentiment Analysis for Code-Mixed Social Media Text, and we have attempted to develop a system to predict the sentiment of a given code-mixed tweet.

Sentiment Analysis

PERLEX: A Bilingual Persian-English Gold Dataset for Relation Extraction

no code implementations13 May 2020 Majid Asgari-Bidhendi, Mehrdad Nasser, Behrooz Janfada, Behrouz Minaei-Bidgoli

The main motivations of this research stem from a lack of a dataset for relation extraction in the Persian language as well as the necessity of extracting knowledge from the growing big-data in the Persian language for different applications.

Knowledge Base Population Relation Extraction

FarsBase-KBP: A Knowledge Base Population System for the Persian Knowledge Graph

no code implementations4 May 2020 Majid Asgari-Bidhendi, Behrooz Janfada, Behrouz Minaei-Bidgoli

While most of the knowledge bases already support the English language, there is only one knowledge base for the Persian language, known as FarsBase, which is automatically created via semi-structured web information.

Entity Linking Knowledge Base Population +1

ParsEL 1.0: Unsupervised Entity Linking in Persian Social Media Texts

no code implementations22 Apr 2020 Majid Asgari-Bidhendi, Farzane Fakhrian, Behrouz Minaei-Bidgoli

Entity linking is the task of linking the entity mentions in the text to their corresponding entities in a knowledge base.

Entity Linking

Pars-ABSA: an Aspect-based Sentiment Analysis dataset for Persian

1 code implementation26 Jul 2019 Taha Shangipour Ataei, Kamyar Darvishi, Soroush Javdan, Behrouz Minaei-Bidgoli, Sauleh Eetemadi

Due to the increased availability of online reviews, sentiment analysis had been witnessed a booming interest from the researchers.

Aspect-Based Sentiment Analysis

A new selection strategy for selective cluster ensemble based on Diversity and Independency

no code implementations9 Oct 2016 Muhammad Yousefnezhad, Ali Reihanian, Daoqiang Zhang, Behrouz Minaei-Bidgoli

In recent years, Diversity and Quality, which are two metrics in evaluation procedure, have been used for selecting basic clustering results in the cluster ensemble selection.

Multi-View Learning for Web Spam Detection

no code implementations16 May 2013 Ali Hadian, Behrouz Minaei-Bidgoli

In order to design a multi-view classification system, we used state-of-the-art spam classification methods with distinct feature sets (views) as the base classifiers.

Classification General Classification +2

A Framework for Spelling Correction in Persian Language Using Noisy Channel Model

no code implementations LREC 2012 Mohammad Hoseyn Sheykholeslam, Behrouz Minaei-Bidgoli, Hossein Juzi

Our evaluation results show that Noisy Channel Model using our corpus and training set in this framework works more accurately and improves efficiently in comparison with other methods.

Spelling Correction

Cannot find the paper you are looking for? You can Submit a new open access paper.