Papers by Behnam Sabeti
Optimizing Annotation Effort Using Active Learning Strategies: A Sentiment Analysis Case Study in Persian (2020.lrec-1)
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Seyed Arad Ashrafi Asli, Behnam Sabeti, Zahra Majdabadi, Preni Golazizian, Reza Fahmi, Omid Momenzadeh
| Challenge: | Existing deep learning approaches require huge amounts of data to be trained properly. |
| Approach: | They propose to use Persian as a model to choose the samples for annotation instead of labeling the whole dataset. |
| Outcome: | The proposed models achieve the baseline performance with a significantly lower amount of labeled data. |
MirasText: An Automatically Generated Text Corpus for Persian (L18-1)
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Behnam Sabeti, Hossein Abedi Firouzjaee, Ali Janalizadeh Choobbasti, S.H.E. Mortazavi Najafabadi, Amir Vaheb
| Challenge: | Natural language processing is one of the most important fields of artificial intelligence. |
| Approach: | They propose to use MirasText to generate Persian text corpus from Persian websites . MiraSText has over 2.8 million documents and over 1.4 billion tokens . |
| Outcome: | The generated corpus has over 2.8 million documents and over 1.4 billion tokens . MirasText has over 800 billion token tokens and more than 300 thousand articles . |
Irony Detection in Persian Language: A Transfer Learning Approach Using Emoji Prediction (2020.lrec-1)
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Preni Golazizian, Behnam Sabeti, Seyed Arad Ashrafi Asli, Zahra Majdabadi, Omid Momenzadeh, Reza Fahmi
| Challenge: | Existing methods for emotion extraction and sentiment analysis produce invalid results due to the use of irony. |
| Approach: | They propose to use emoji prediction to fine tune a model using hand labeled tweets with irony tags. |
| Outcome: | The proposed method outperforms the state-of-the-art method on Persian dataset with an accuracy of 83.1% and offers strong baseline for further research in Persian language. |
MirasVoice: A bilingual (English-Persian) speech corpus (L18-1)
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| Challenge: | Existing research and development areas in speech recognition are focused on the language of speakers. |
| Approach: | They propose to use a bilingual (English-Farsi) speech corpus to validate and explore speaker verification systems. |
| Outcome: | The proposed corpus can be used in a variety of language dependent and independent applications. |
Twitter Trend Extraction: A Graph-based Approach for Tweet and Hashtag Ranking, Utilizing No-Hashtag Tweets (2020.lrec-1)
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Zahra Majdabadi, Behnam Sabeti, Preni Golazizian, Seyed Arad Ashrafi Asli, Omid Momenzadeh, Reza Fahmi
| Challenge: | Twitter has become a major platform for users to express their opinions on any topic and engage in debates. |
| Approach: | They propose to use tweets as graph nodes to extract trends from tweets graph . they propose to employ RankClus algorithm to rank tweets, words and hashtags in each trend . |
| Outcome: | The proposed algorithm can extract trends from tweets and rank tweets, words and hashtags based on their importance and relevance to the topic. |