| { |
| "paper_id": "O13-1000", |
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| "date_generated": "2023-01-19T08:03:27.754680Z" |
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| "title": "", |
| "authors": [ |
| { |
| "first": "Hung-Duen", |
| "middle": [], |
| "last": "Yang", |
| "suffix": "", |
| "affiliation": {}, |
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| }, |
| { |
| "first": "Bing", |
| "middle": [], |
| "last": "Liu", |
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| "email": "" |
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| { |
| "first": "Berlin", |
| "middle": [], |
| "last": "Chen", |
| "suffix": "", |
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| "email": "" |
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| { |
| "first": "Jiun-Shian", |
| "middle": [], |
| "last": "Liu", |
| "suffix": "", |
| "affiliation": {}, |
| "email": "" |
| }, |
| { |
| "first": "Wen-Hsiang", |
| "middle": [], |
| "last": "Lu", |
| "suffix": "", |
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| "email": "" |
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| { |
| "first": "", |
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| "abstract": "Social media analysis has become a major research direction in recent years due to numerous applications and challenging research problems. In this talk, I will present a sentiment and opinion centric framework for social media content analysis because in most applications of social media the most important information that one wants to mine are what people talk about and what their opinions are. These are exactly the tasks of sentiment", |
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| "text": "Social media analysis has become a major research direction in recent years due to numerous applications and challenging research problems. In this talk, I will present a sentiment and opinion centric framework for social media content analysis because in most applications of social media the most important information that one wants to mine are what people talk about and what their opinions are. These are exactly the tasks of sentiment", |
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| "text": "published a book titled \"Sentiment Analysis and Opinion Mining\" (Morgan and Claypool Publishers). Liu's earlier work was in the areas of data mining, Web mining, and machine learning, where he also published extensively in leading conferences and journals, and a textbook titled \"Web Data Mining: Exploring Hyperlinks, Contents and Usage Data\" (Springer). On professional services, Liu has served as program chairs of KDD, ICDM, CIKM, WSDM, SDM, and PAKDD, and as area/track chairs or senior PC members of many data mining, natural language processing, Web technology and AI conferences.", |
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| "text": "Additional information about him can be found at <http://www.cs.uic.edu/~liub/> ", |
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| "text": "Since speech is highly variable, even if we have a fairly large-scale database, we cannot avoid the data sparseness problem in constructing automatic speech recognition (ASR) systems. How to train and adapt statistical models using limited amounts of data is one of the most important research issues in ASR. This talk summarizes major techniques that have been proposed to solve the generalization problem in acoustic model training and adaptation, that is, how to achieve high recognition accuracy for new utterances. One of the common approaches is controlling the degree of freedom in model training and adaptation.", |
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| "text": "The techniques can be classified by whether a priori knowledge of speech obtained from a speech database such as those recorded using many speakers is used or not. Another approach is maximizing \"margins\" between training samples and the decision boundaries.", |
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| "text": "Many of these techniques have also been combined and extended to further improve performance.", |
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| "text": "Although many useful techniques have been developed, we still do not have a golden standard that can be applied to any kind of speech variation and any condition of the speech data available for training and adaptation. We need to focus on collecting rich and effective speech databases covering a wide range of variations, active learning for automatically selecting data for annotation, cheap, fast and good-enough transcription, and efficient supervised, semi-supervised, or unsupervised training/adaptation, based on advanced machine learning techniques. We also need to extend current efforts to understand more about human speech processing and the mechanism of natural speech variation.", |
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| "text": "Sadaoki Furui received the B.S., M.S., and Ph.D. degrees from the University of Tokyo, Japan in 1968 , 1970 , and 1978 . After joining the Nippon Telegraph and Telephone Corporation (NTT) Labs in 1970, he has worked on speech analysis, speech recognition, speaker recognition, speech synthesis, speech perception, and multimodal human-computer interaction. From 1978 to 1979, he was a visiting researcher at AT&T Bell Laboratories, Murray Hill, New Jersey. He was a Research Fellow and the Director of Furui Research Laboratory at NTT Labs. He became a Professor at Tokyo", |
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| "start": 80, |
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| "text": "Tokyo, Japan in 1968", |
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| "start": 101, |
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| "text": ", 1970", |
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| "start": 108, |
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| "text": ", and 1978", |
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| "section": "Biography", |
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| "text": "Proceedings of the Twenty-Fifth Conference on Computational Linguistics and Speech Processing(ROCLING 2013)", |
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| "ref_id": "b0", |
| "title": "He alsoreceived the Achievement Award from the Minister of Science and Technology and the Minister of Education", |
| "authors": [], |
| "year": 1975, |
| "venue": "1997, and was given the title of Professor Emeritus in 2011. He is now serving as President of Toyota Technological Institute at Chicago (TTI-C). He has authored or coauthored over 900 published papers and books including", |
| "volume": "88", |
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| "raw_text": "Institute of Technology in 1997, and was given the title of Professor Emeritus in 2011. He is now serving as President of Toyota Technological Institute at Chicago (TTI-C). He has authored or coauthored over 900 published papers and books including \"Digital Speech Processing, Synthesis and Recognition.\" He was elected a Fellow of the IEEE (1993), the Acoustical Society of America (ASA) (1996), the Institute of Electronics, Information and Communication Engineers of Japan (IEICE) (2001) and the International Speech Communication Association (ISCA) (2008). He received the Paper Award and the Achievement Award from the IEICE (1975, 88, 93, 2003, 2003, 2008), and the Paper Award from the Acoustical Society of Japan (ASJ) (1985, 87). He received the Senior Award and Society Award from the IEEE SP Society (1989, 2006), the ISCA Medal for Scientific Achievement (2009), and the IEEE James L. Flanagan Speech and Audio Processing Award (2010). He received the NHK (Nippon Hoso Kyokai: Japan Broadcasting Corporation) Broadcast Cultural Award (2012) and the Okawa Prize (2013). He alsoreceived the Achievement Award from the Minister of Science and Technology and the Minister of Education, Japan (1989, 2006), and the Purple Ribbon Medal from Japanese Emperor (2006) Proceedings of the Twenty-Fifth Conference on Computational Linguistics and Speech Processing (ROCLING 2013)", |
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| "text": "Illinois at Chicago (UIC)\u7684Bing Liu\u6559\u6388\u4ee5\u53ca\uf92d\u81ea\u65bc\u65e5 \u672cTokyo Institute of Technology\u7684Sadaoki Furui\u6559\u6388\uff0c\u5206\u5225\u5c31Sentiment and Opinion Centric Analysis of Social Media Content\u4ee5\u53caData-intensive Automatic Speech Recognition Based on Machine Learning\u7d66\u4e88\ufa1d\u5f69\u7684\u6f14\u8b1b\u3002\u975e\u5e38\u611f\u8b1d\u4ed6\u5011\u9060\u9053\u800c\uf92d\u70ba\u5927\u6703\u589e\u8272\uf967\u5c11\u3002 Variability in vowel formant frequencies of children with cerebral palsy \u2026 Li-mei Chen, Yung-Chieh Lin, Wei-Chen Hsu, Fang-Hsin Liao 29. \u57fa\u65bc\u7279\u5fb5\u70ba\u672c\u53ca\u4f7f\u7528 SVM \u7684\u6587\u672c\u5c0d\u860a\u6db5\u95dc\u4fc2\u7684\u81ea\u52d5\u63a8\uf941\u65b9\u6cd5 \u2026 Bing Liu is a professor of Computer Science at the University of Illinois at Chicago (UIC). He received his PhD in Artificial Intelligence (AI) from University of Edinburgh. Before joining UIC, he was with the National University of Singapore. His current research interests include sentiment analysis and opinion mining, opinion spam detection, and social media modeling.", |
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| "content": "<table><tr><td colspan=\"3\">\u5e8f\u8a00 \u672c\u5c46ROCLING\u5171\u6536\u5230\u6295\u7a3f\uf969\u70ba41\u7bc7\uff0c\u6bcf\u7bc7\uf941\u6587\ufa26\u5747\u9080\u8acb\u81f3\u5c113\u4f4d\u8a72\uf9b4\u57df\u7684\u5c08\u5bb6\u5b78\u8005\u9032\ufa08\u5be9\u67e5\uff0c \u76ee\uf93f \u5e8f\u8a00\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026 i \u8b70\u7a0b\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026 ii 13. \u4e2d\u82f1\u6587\u7684\u6587\u5b57\u860a\u6db5\u8207\u95b1\uf95a\u6e2c\u9a57\u7684\u521d\u6b65\u63a2\uf96a \u2026 Wei-Jie Huang, Po-Cheng Lin, Chao-Lin Liu 14. \u860a\u6db5\uf906\u578b\u5206\u6790\u65bc\u6539\u9032\u4e2d\u6587\u6587\u5b57\u860a\u6db5\uf9fc\u5225\u7cfb\u7d71 \u2026 Shan-Shun Yang, Shih-Hung Wu, Liang-Pu Chen, Hung-Sheng Chiu, Ren-Dar Yang 28. Tao-Hsing Chang</td></tr><tr><td colspan=\"3\">\u6700\u5f8c\u8b70\u7a0b\u59d4\u54e1\u6703\u5171\u63a5\u53d717\u7bc7oral presentation\uf941\u6587\u548c12\u7bc7poster presentation\uf941\u6587\uff0c\u5305\u542b\uf9ba\u8a9e \u76ee\uf93f\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026 iii 15. A Semantic-Based Approach to Noun-Noun Compound Interpretation \u2026 30. Constructing Social Intentional Corpora to Predict Click-Through Rate for \u97f3\u8fa8\u8a8d\u8207\u5408\u6210\u3001\u6a5f\u5668\u7ffb\u8b6f\u3001\u8a9e\u97f3\u5b78\u8207\u97f3\u97fb\u5b78\u4e4b\u5206\u6790\u53ca\u61c9\u7528\u3001\u81ea\u7136\u8a9e\u8a00\u8655\uf9e4\u4e4b\u61c9\u7528\u3001\u5de5\u5177\u8207\u8cc7 You-shan Chung, Keh-Jiann Chen Search Advertising \u2026 \u6e90\u3001\u53ca\u8a9e\u97f3\uf9fc\u5225\u548c\uf9e4\u89e3\u7b49\uf9b4\u57df\uff0c\u6b64\u5be9\u67e5\u7d50\u679c\u7dad\u6301\uf9baROCLING\uf98c\u5c46\u4ee5\uf92d\u4e00\u8cab\u7684\uf941\u6587\u54c1\u8cea\uff0c\u4e26\u517c Keynote Speech 16. \u6539\uf97c\u8abf\u8b8a\u983b\u8b5c\u7d71\u8a08\u5716\u7b49\u5316\u6cd5\u65bc\u5f37\u5065\u6027\u8a9e\u97f3\u8fa8\uf9fc\u4e4b\u7814\u7a76 \u2026 Yi-Ting Chen, Hung-Yu Kao \u9867\u591a\u5c64\u9762\u7814\u7a76\u4eba\u54e1\u7684\uf96b\u8207\uff0c\u5728\u6b64\u975e\u5e38\u611f\u8b1d\uf941\u6587\u5be9\u67e5\u59d4\u54e1\u7684\u628a\u95dc\u3002 Yu-chen Kao, Berlin Chen 31. Location and Activity Recommendation by Using Consecutive Itinerary</td></tr><tr><td colspan=\"3\">\u4eca\uf98e\u7684\u8b70\u7a0b\u5b89\u6392\uff0c\u9664\uf9ba\u6700\u65b0\u7684\u5b78\u8853\uf941\u6587\u7684\u767c\u8868\u5916\uff0c\u4e5f\u9080\u8acb\uf978\u4f4d\u8a9e\u97f3\u53ca\u81ea\u7136\u8a9e\u8a00\u8655\uf9e4\uf9b4\u57df\u5c08\u5bb6 1. Sentiment and Opinion Centric Analysis of Social Media Content \u2026 1 17. Employing Linear Prediction Coding in Feature Time Sequences for Robust Matching Model \u2026</td></tr><tr><td colspan=\"3\">Bing Liu 2. Data-intensive Automatic Speech Recognition Based on Machine Learning \u2026 3 ROCLING 2013 Paper Speech Recognition in Noisy Environments \u2026 Hao-teng Fan, Jeih-weih Hung \u7d66\u4e88\u5c08\u984c\u6f14\u8b1b\uff0c\u5305\u62ec\u7f8e\u570bUniversity of \u6211\u5011\u540c\u6642\u8981\u611f\u8b1d\u570b\u79d1\u6703\u5de5\u7a0b\u79d1\u6280\u63a8\u5c55\u4e2d\u5fc3\u3001\u4e2d\u592e\u7814\u7a76\u9662\u8cc7\u8a0a\u79d1\u5b78\u7814\u7a76\u6240\u3001\u4e2d\u83ef\u96fb\u4fe1\u7814\u7a76\u6240\u3001 Sadaoki Furui 18. \u7d50\u5408 I-Vector \u53ca\u6df1\u5c64\ufa19\u7d93\u7db2\uf937\u4e4b\u8a9e\u8005\u9a57\u8b49\u7cfb\u7d71 \u2026</td></tr><tr><td colspan=\"3\">\u8cc7\u8a0a\u5de5\u696d\u7b56\u9032\u6703\u3001\u5de5\u696d\u6280\u8853\u7814\u7a76\u9662\u3001\u8cfd\u5fae\u79d1\u6280\u8207\u81f4\u9060\u79d1\u6280\u7684\u5354\u8fa6\u8207\u8d0a\u52a9\u3002\u64f4\u5927\u63a5\u89f8\u5c64\u9762\uff0c\u5c07</td></tr><tr><td colspan=\"3\">\u7522\u696d\u754c\u8207\u5b78\u8853\u754c\u7d50\u5408\u505a\u70ba\u8a9e\u8a00\u8655\uf9e4\u8207\u8a9e\u97f3\u6280\u8853\u4e4b\u5171\u540c\u5925\u4f34\u3002 3. Improved Sentence Modeling Techniques for Extractive Speech Summarization 5</td></tr><tr><td colspan=\"2\">\u6700\u5f8c\uff0c\u611f\u6fc0\u5404\u4f4d\u8207\u6703\u5148\u9032\u7684\u7a4d\u6975\uf96b\u8207\u548c\u652f\u6301\uff0c\u4f7f\u672c\u6b21\u7814\u8a0e\u6703\u5f97\u4ee5\u9806\uf9dd\u8209\ufa08\u3002</td><td/></tr><tr><td>Jenq-Haur Wang, Ting-Wei Ye</td><td/><td/></tr><tr><td colspan=\"3\">22. Primary Chinese Semantic-Phonetic Compounds Pronunciation Rules Mining</td></tr><tr><td>and Visualization</td><td/><td>\u2026</td></tr><tr><td colspan=\"3\">Tsung Peng Yen, Chia-Ping Chen Meng-Feng Tsai, Chien-Hui Hsu, Chia-Hui Chang, Hsiang-Mei Liao, Shu-Ping Li, Denise H. Wu</td></tr><tr><td colspan=\"3\">7. Chinese Spelling Checker Based on Statistical Machine Translation 23. A Corpus-driven Pattern Analysis in Locative Phrases: A Statistical Comparison \u2026</td></tr><tr><td>Hsun-wen Chiu, Jian-cheng Wu, Jason S. Chang of Co-appearing Concepts in Fixed Frames</td><td/><td>\u2026</td></tr><tr><td colspan=\"2\">8. Detecting English Grammatical Errors based on Machine Translation CHAO F.Y. AUGUST, Siaw-Fong Chung</td><td>\u2026</td></tr><tr><td colspan=\"3\">Jim Chang, Jian-cheng Wu, Jason S. Chang 24. A simple real-word error detection and correction using local word bigram and</td></tr><tr><td>9. Selecting Proper Lexical Paraphrase for Children trigram</td><td/><td>\u2026 \u2026</td></tr><tr><td colspan=\"2\">Tomoyuki Kajiwara, Hiroshi Matsumoto, Kazuhide Yamamoto Pratip Samanta, Bidyut Baran Chaudhuri Biography</td><td/></tr><tr><td colspan=\"3\">10. Synthesis Unit and Question Set Definition for Mandarin HMM-based Singing 25. \u7d50\u5408\u95dc\u9375\u8a5e\u9a57\u8b49\u53ca\u8a9e\u8005\u9a57\u8b49\u4e4b\u96f2\u7aef\u8eab\u4efd\u9a57\u8b49\u7cfb\u7d71 \u2026</td></tr><tr><td>Voice Synthesis Yi-Chin Chiu, Bor-Shen Lin, Chuan-Yen Fan</td><td/><td>\u2026</td></tr><tr><td colspan=\"2\">Ju-Yun Cheng, Yi-Chin Huang, Chung-Hsien Wu 26. Causing Emotion in Collocation: An Exploratory Data Analysis</td><td>\u2026</td></tr><tr><td>11. \u57fa\u65bc\u6642\u57df\u4e0a\u57fa\u9031\u540c\u6b65\u758a\u52a0\u6cd5\u4e4b\u6b4c\u8072\u5408\u6210\u7cfb\u7d71 Pei-Yu Lu, Yu-Yun Chang, Shu-kai Hsieh</td><td colspan=\"2\">\u5927\u6703\u4e3b\u5e2d \u694a\u5f18\u6566\u3001\u9673\u5609\u5e73\u3001\u8a31\u805e\uf9a2 \u2026</td></tr><tr><td colspan=\"2\">\u8b70\u7a0b\u4e3b\u5e2d \u5f35\u5609\u60e0\u3001\u738b\u5bb6\u6176 27. Observing Features of PTT Neologisms: A Corpus-driven Study with N-gram Wu Ming Kuan, Chia-Ping Chen</td><td/></tr><tr><td>12. \u57fa\u65bc\u97f3\u6bb5\u5f0f LMR \u5c0d\u6620\u4e4b\u8a9e\u97f3\u8f49\u63db\u65b9\u6cd5\u7684\u6539\u9032 Model</td><td>2013 \uf98e 10 \u6708 04 \u65e5</td><td>\u2026 \u2026</td></tr><tr><td>Tsun-Jui Liu, Shu-Kai Hsieh, Laurent PREVOT</td><td/><td/></tr><tr><td>v</td><td/><td/></tr></table>", |
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| "text": "Proceedings of the Twenty-Fifth Conference on Computational Linguistics and Speech Processing(ROCLING 2013)", |
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| "content": "<table><tr><td>Keynote B</td></tr><tr><td>Data-intensive Automatic Speech Recognition Based on Machine Learning</td></tr></table>", |
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