International Journal of Computational Linguistics & Chinese Language Processing, Volume 24, Number 1, June 2019

International Journal of Computational Linguistics & Chinese Language Processing Aims and Scope International Journal of Computational Linguistics and Chinese Language Processing (IJCLCLP) is an international journal published by the Association for Computational Linguistics and Chinese Language Processing (ACLCLP). This journal was founded in August 1996 and is published four issues per year since 2005. This journal covers all aspects related to computational linguistics and speech/text processing of all natural languages. Possible topics for manuscript submitted to the journal include, but are not limited to: • Computational Linguistics • Natural Language Processing • Machine Translation • Language Generation • Language Learning • Speech Analysis/Synthesis • Speech Recognition/Understanding • Spoken Dialog Systems • Information Retrieval and Extraction • Web Information Extraction/Mining • Corpus Linguistics • Multilingual/Cross-lingual Language Processing Membership & Subscriptions If you are interested in joining ACLCLP, please see appendix for further information. Copyright © The Association for Computational Linguistics and Chinese Language Processing International Journal of Computational Linguistics and Chinese Language Processing is published four issues per volume by the Association for Computational Linguistics and Chinese Language Processing. Responsibility for the contents rests upon the authors and not upon ACLCLP, or its members. Copyright by the Association for Computational Linguistics and Chinese Language Processing. All rights reserved. No part of this journal may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical photocopying, recording or otherwise, without prior permission in writing form from the Editor-in Chief. Cover Calligraphy by Professor Ching-Chun Hsieh, founding president of ACLCLP Text excerpted and compiled from ancient Chinese classics, dating back to 700 B.C. This calligraphy honors the interaction and influence between text and language International Journal of Computational Linguistics and Chinese Language Processing vol. 24, no. 1, June 2019 Contents Papers 應用記憶增強條件隨機場域與之深度學習及自動化詞彙特徵 於中文命名實體辨識之研究 [Leveraging Memory Enhanced Conditional Random Fields with Gated CNN and Automatic BAPS Features for Chinese Named Entity Recognition]……….... 簡國峻(Kuo-Chun Chien),張嘉惠(Chia-Hui Chang) 1 Discovering the Latent Writing Style from Articles: A Contextualized Feature Extraction Approach…...………………... Yen-Hao Huang, Ting-Wei Liu, Ssu-Rui Lee, Ya-Wen Yu, Wan-Hsuan Lee, Fernando Henrique Calderon Alvarado and Yi-Shin Chen 15 探究端對端混合模型架構於華語語音辨識 [An Investigation of Hybrid CTC-Attention Modeling in Mandarin Speech Recognition]…………………………………………………..…... 張修瑞(Hsiu-Jui Chang),趙偉成(Wei-Cheng Chao), 羅天宏(Tien-Hong Lo),陳柏琳(Berlin Chen) 39 The Association for Computational Linguistics and Chinese Language Processing Computational Linguistics and Chinese Language Processing Vol. 24, No. 1, June 2019, pp. 1-14 1  The Association for Computational Linguistics and Chinese Language Processing 應用記憶增強條件隨機場域與之深度學習及 自動化詞彙特徵於中文命名實體辨識之研究 Leveraging Memory Enhanced Conditional Random Fields with Gated CNN and Automatic BAPS Features for Chinese Named Entity Recognition 簡國峻、張嘉惠 Kuo-Chun Chien and Chia-Hui Chang 摘要 命名實體辨識是在自然語言處理當中一個重要的任務。現今基礎深度學習模型 應用於資料品質較為優良的命名實體擷取，雖有不錯的效果，但在社群媒體資 料集中卻未能達到傳統條件隨機場域之基準值。由於一個命名實體有能可多次 在文中提及，因此藉由上下文資訊來改進命名實體的擷取也是近年來的研究方 向。在本研究中，我們延伸記憶增強條件隨機場域 MECRF 於中文的命名實體 擷取，利用門控卷積網路及雙向 GRU 網路來增強記憶條件隨機場域，以利模 型抓取長距離的文章資訊。此外，也藉由特徵探勘擷取命名實體前後詞彙以及 前綴後綴詞彙特徵（簡稱為 BAPS），並使用模型可自動訓練的參數，自動調 整詞向量及 BAPS 詞彙特徵。最後我們同時採用字元及詞彙向量來增進模型的 效能。本研究所提出之模型，在網路社群媒體的人名辨識資料中可以達到的 91.67％準確率，在 SIGHAN-MSRA 中也得到最高的 92.45％地名實體辨識效 果及 90.95％整體召回率。 關鍵詞：機器學習、命名實體辨識、神經網路、特徵探勘  國立中央大學資訊工程學系 Department of Computer Science & Information Engineering, National Central University E-mail: qk0614@gmail.com; chia@csie.ncu.edu.tw 2 簡國峻與張嘉惠 Abstract Named Entity Recognition (NER) is an essential task in Natural Language Processing. Memory Enhanced CRF (MECRF) integrates external memory to extend Conditional Random Field (CRF) to capture long-range dependencies …

Content truncated to 500 words