کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
11002884 1450007 2018 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Effective integration of morphological analysis and named entity recognition based on a recurrent neural network
ترجمه فارسی عنوان
یکپارچه سازی موثر تجزیه و تحلیل مورفولوژیکی و شناسایی نام نهاد بر اساس یک شبکه عصبی مکرر
کلمات کلیدی
تجزیه و تحلیل مورفولوژیکی، شناسایی نام خطا در انتشار پرونده، مدل شبکه یکپارچه،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
Morphological analysis (MA) and named entity recognition (NER) are essential steps in natural language processing. Because NER is generally considered the next step after MA, many previous studies have adopted a pipeline architecture in which results of MA are used as inputs of NER. However, under this kind of pipeline architecture, MA errors lead to decreasing performance in NER models. To alleviate this error propagation problem, we propose an integrated neural network model that performs MA and NER simultaneously. The proposed model consists of two layers of bidirectional gated recurrent unit models with conditional random field layers: a lower layer for MA and an upper layer for NER. To optimize weighting parameters of the proposed model, we use a two-phase training scheme. The first phase trains all layers for NER, and the second trains the lower layer for MA. In our experiments, the proposed model outperforms both an independent MA model and independent NER model. Based on the experimental results, we conclude that the proposed model can effectively alleviate the error propagation problem that frequently occurs in the pipeline architecture.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition Letters - Volume 112, 1 September 2018, Pages 361-365
نویسندگان
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