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International Journal of Information and Communication Technology Research (IJICT، جلد ۱۳، شماره ۲، صفحات ۳۹-۴۸
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| عنوان فارسی |
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| چکیده فارسی مقاله |
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| کلیدواژه‌های فارسی مقاله |
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| عنوان انگلیسی |
Improving Persian Named Entity Recognition Through Multi Task Learning |
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| چکیده انگلیسی مقاله |
Named Entity Recognition is a challenging task, specially for low resource languages, such as Persian, due to the lack of massive gold data. As developing manually-annotated datasets is time consuming and expensive, we use a multitask learning (MTL) framework to exploit different datasets to enrich the extracted features and improve the accuracy of recognizing named entities in Persian news articles. Highly motivated auxiliary tasks are chosen to be included in a deep learning based structure. Additionally, we investigate the effect of chosen datasets on performance of the model. Our best model significantly outperformed the state of the art model by , according to F1 score in the phrase level. |
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| کلیدواژه‌های انگلیسی مقاله |
Named-Entity Recognition, Deep Learning, Multi-Task Learning, Persian Language, Low-recourse Languages |
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| نویسندگان مقاله |
| Mohammad Hadi Bokaei
Information Technology Institute Telecommunication research Center, Tehran, Iran
| Abdolah Sepahvand
Information Technology Institut Telecommunication Research Center, Tehran, Iran
| Mohammad Nouri
Information Technology Institute Telecommunication Research Center, Tehran, Iran
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| نشانی اینترنتی |
http://ijict.itrc.ac.ir/browse.php?a_code=A-10-4254-2&slc_lang=other&sid=1 |
| فایل مقاله |
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| کد مقاله (doi) |
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| زبان مقاله منتشر شده |
other |
| موضوعات مقاله منتشر شده |
فناوری اطلاعات |
| نوع مقاله منتشر شده |
پژوهشی |
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