Persian Ezafeh Recognition using Transformer-Based Models

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ANSARI ALI; Ebrahimian Zahra; Toosi Ramin; Akhaee Mohammad Ali

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2023 | Issue:9

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Title

Persian Ezafeh Recognition using Transformer-Based Models

Author(s)

ANSARI ALI | Ebrahimian Zahra | Toosi Ramin | Akhaee Mohammad Ali

Keywords

Ezafe recognition

Natural Language Processing

BERT

Abstract

In Persian, the grammatical particle ezafe connects two words. Ezafe is one of the salient factors in Persian phonology and morphology to understand the meaning of a sentence completely and truly, whereas it is not usually written in sentences, resulting in mistakes in reading complex sentences and errors in Natural Language Processing tasks. Therefore, recognizing words that need Ezafe at the end of themselves, is a major factor to improve the performance of a variety of NLP-based systems such as a Text TTSsystem. Because in Persian TTS systems without an Ezafe recognition module cannot make Ezafe constructions to read the text correctly and does not recognize the relations between the words. As Transformer-based methods shows state-of-the-art results in lots of NLP tasks, in this paper, we experiment ParsBERT in the task of Ezafe recognition. The latter earning 2. 68% better F1-score than the prior state-of-the-art, we obtain the most advantageous outcomes.

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APA: Copy

ANSARI, ALI, Ebrahimian, Zahra, Toosi, Ramin, & Akhaee, Mohammad Ali. (). . . SID. https://sid.ir/paper/1046839/en

Vancouver: Copy

ANSARI ALI, Ebrahimian Zahra, Toosi Ramin, Akhaee Mohammad Ali. . . Available from: https://sid.ir/paper/1046839/en

IEEE: Copy

ALI ANSARI, Zahra Ebrahimian, Ramin Toosi, and Mohammad Ali Akhaee, “,” presented at the . , [Online]. Available: https://sid.ir/paper/1046839/en

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