A Novel Approach to Conditional Random Field-based Named Entity Recognition using Persian Specific Features | ||
| Journal of AI and Data Mining | ||
| مقاله 7، دوره 8، شماره 2، تیر 2020، صفحه 227-236 اصل مقاله (1.25 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2019.8430.1980 | ||
| نویسندگان | ||
| L. Jafar Tafreshi* 1؛ F. Soltanzadeh2 | ||
| 1Computer Research Center of Islamic Sciences (CRCIS), Tehran, Iran. | ||
| 2General Linguistics Department, Allameh Tabatabaei University, Tehran, Iran. | ||
| چکیده | ||
| 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 in the Persian language if they are trained with good features. To get good performance in Conditional Random Field-based Persian Named Entity Recognition, a several syntactic features based on dependency grammar along with some morphological and language-independent features have been designed in order to extract suitable features for the learning phase. In this implementation, designed features have been applied to Conditional Random Field to build our model. To evaluate our system, the Persian syntactic dependency Treebank with about 30,000 sentences, prepared in NOOR Islamic science computer research center, has been implemented. This Treebank has Named-Entity tags, such as Person, Organization and location. The result of this study showed that our approach achieved 86.86% precision, 80.29% recall and 83.44% F-measure which are relatively higher than those values reported for other Persian NER methods. | ||
| کلیدواژهها | ||
| Natural language processing؛ Name Entity Recognition؛ Conditional Random Field؛ Dependency Grammar | ||
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آمار تعداد مشاهده مقاله: 764 تعداد دریافت فایل اصل مقاله: 873 |
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