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بهبود استخراج ویژگی با استفاده از یک مدل یادگیری عمیق گروهی برای تشخیص موجودیت | ||
| مدل سازی در مهندسی | ||
| دوره 20، شماره 69، تیر 1401، صفحه 103-112 اصل مقاله (982.16 K) | ||
| نوع مقاله: مقاله کامپیوتر | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2022.23901.2115 | ||
| نویسندگان | ||
| الهام پارسایی مهر1؛ مهدی فرتاش* 2؛ جواد اکبری ترکستانی2 | ||
| 1گروه کامپیوتر، دانشکده مهندسی، دانشگاه آزاد اسلامی واحد اراک، | ||
| 2گروه مهندسی کامپیوتر، واحد اراک، دانشگاه آزاد اسلامی، اراک، ایران | ||
| تاریخ دریافت: 19 تیر 1400، تاریخ بازنگری: 28 آذر 1400، تاریخ پذیرش: 30 بهمن 1400 | ||
| چکیده | ||
| یکی از مراحل اولیه در بیشتر پردازش‌های زبان طبیعی، استخراج موجودیت نامدار از جمله است. در این زمینه تکنیک‌های مختلف مبتنی بر یادگیری ماشین ارائه شده است که بدون نیاز به پیچیدگی‌های استخراج ویژگی دستی، دقت بالاتری از خود نشان داده اند. لذا، دراین تحقیق ما برای گرفتن ویژگی‌های جمله ورودی از ترکیب دو مدل یادگیری عمیق شامل شبکه عصبی کانولوشن و همینطور حافظه کوتاه مدت طولانی استفاده می‌کنیم. با استخراج ویژگی‌های محلی کلمات توسط شبکه کانولوشن در کنار ویژگی‌های سراسری، اطلاعات بیشتری از جمله جهت کلاسبندی دقیقتر موجودیتها بدست میآوریم. ما معماری پیشنهادی‌مان را روی دو دیتاست CoNLL2003 و ACE05 ارزیابی مینماییم و نشان میدهیم که افزودن شبکه کانولوشن سطح کلمه باعث استخراج اطلاعات محلی مفیدی از کلمات موجود در جمله می‌شود که منجر به افزایش دقت سیستم می‌گردد. در نهایت، کارایی سیستم را با دیگر رقبا مقایسه مینماییم و برتری این معماری نسبت به دیگران گزارش داده می‌شود. | ||
| کلیدواژه‌ها | ||
| تشخیص موجودیت نامدار؛ تعبیه گذاری کلمه؛ LSTM؛ CNN؛ پردازش زبان طبیعی | ||
| عنوان مقاله [English] | ||
| An ensemble deep learning model to enhance feature representation for entity detection | ||
| نویسندگان [English] | ||
| Elham Parsaeimehr1؛ mehdi fartash2؛ Javad Akbari Torkestani2 | ||
| 1Department of Computer Engineering, َArak Branch, Islamic Azad University, Arak, Iran | ||
| 2Department of Computer Engineering, Arak Branch, Islamic Azad University, Arak, Iran | ||
| چکیده [English] | ||
| One of the main processes in most natural language processing (NLP), is named entity recognition (NER). In this regard, some machine learning techniques have been presented that traditionally use manual features. Also, in recent years, deep neural network-based models have been proposed that achieve higher accuracy without relying on huge computations for feature engineering. Thus, in this article, we employ a combination of two deep learning models to capture the properties of the input sentence, including: long short term memory (LSTM) and convolutional neural network (CNN). In this architecture, extracting local features along with global features, more information is acquired for more accurate classification. We evaluate the performance of this architecture on two datasets CoNLL2003 and ACE05; and demonstrate that by adding a word level CNN, useful local properties are extracted that enhance the accuracy of the performance. Finally, we compare the performance of our system with competitors and our superiority is reported. | ||
| کلیدواژه‌ها [English] | ||
| Named Entity Recognition, LSTM, CNN, Word Embedding, Natural Language Processing | ||
| مراجع | ||
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