نوع مقاله : مقاله کامپیوتر
نویسندگان
1 گروه کامپیوتر، دانشکده مهندسی، دانشگاه آزاد اسلامی واحد اراک،
2 گروه مهندسی کامپیوتر، واحد اراک، دانشگاه آزاد اسلامی، اراک، ایران
3 دانشگاه آزاد اسلامی واحد اراک. دانشکده مهندسی کامپیوتر.
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [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]