كليدواژه :
تشخيص موجوديت‌هاي نامدار پردازش زبان طبيعي , مبتني بر قاعده , ويكي‌پديا , كسره اضافه
چكيده فارسي :
تشخيص موجوديت‌هاي نامدار فرآيندي است كه در آن اسامي اشخاص، مكان‌ها(شهرها، كشورها، درياها و غيره)، سازمان‌ها(شركت‌هاي خصوصي و دولتي، نهادهاي بين‌المللي و غيره)، تاريخ، واحدهاي پولي و درصدها در يك متن شناسايي مي‌شوند. تشخيص موجوديت‌هاي نامدار نقشي اساسي در سامانه‌هاي پرسش و پاسخ، خلاصه‌سازي، ترجمه ماشيني، برچسب‌زن نقش معنايي، جستجوي معنايي، استخراج رابطه و شناسايي نقل قول دارند. در اين مقاله ابتدا فرهنگ واژگان موجوديت‌هاي سازمان، مكان و اشخاص با استفاده از محتواي ويكي‌پدياي فارسي استخراج شد؛ سپس با استفاده از قواعد، سامانه پيشنهادي توسعه يافت. در ادامه دقت شناسايي موجوديت‌هاي نامدار با استفاده از كسره اضافه كه يكي از ويژگي‌هاي مهم زبان فارسي است، بهبود داده شد. جهت ارزيابي سامانه تعداد 42 هزار كلمه از پيكره بي جن‌خان به‌صورت دستي برچسب زده شدند و معيار F 81/92 درصد به‌دست آمد. نتايج حاكي از آن است كه با استفاده از كسره اضافه در سامانه‌هاي تشخيص موجوديت دقت آن‌ها به‌طور قابل ملاحظه‌اي افزايش مي‌يابد.
چكيده لاتين :
Named entity recognition is a process in which the people’s names, name of places (cities, countries, seas, etc.) and organizations (public and private companies, international institutions, etc.), date, currency and percentages in a text are identified. Named entity recognition plays an important role in many NLP tasks such as semantic role labeling, question answering, summarization, machine translation, semantic search, and relation extraction and quotation recognition systems. Named entity recognition in the Persian language is far more complex and more difficult than English. In English texts usually proper nouns begin with capital letters and this feature makes it easy to identify named entities, but this feature is absent in Persian language texts. To create a named entity recognition system, generally three methods are being used which include rule-based, machine-learning-based and hybrid methods. Each of these methods has its own advantages and disadvantages. Lack of named entity labeled data is the greatest challenge in Persian text. Because of this problem usually rule-based methods used to extract entities.
In this paper firstly, the dictionary of organizations, places and people were extracted from Wikipedia. Wikipedia is one of the best sources for extracting entities in which more than 200000 Farsi-named entities are known to exist. The proposed algorithm classify each Wikipedia article title by using its categories. Each of Wikipedia titles has several categories that can be used to partially identify the named entity type. Then named entity recognition accuracy (precision) was increased using the rules. These rules can be divided into 3 categories that include morphological rules, adjacency and text patterns. The most important rules are adjacency rules. By using these rules the type of entity with the word nearby each entity (like Mr, Mrs , …) can be identified. To evaluate the system, 42000 tokens of BijanKhan corpus were manually annotated (labeled). Early F-measure was calculated 78.79 percent. In continue, named entity recognition accuracy (precision) improved using izāfe which is one of the important Persian language features and 81.94 percent for F-measure was achieved. The results showed that using izāfe in named entity recognition systems significantly increases their accuracy.
