TAILIEUCHUNG - Báo cáo khoa học: "Incorporating speech recognition confidence into discriminative named entity recognition of speech data"

This paper proposes a named entity recognition (NER) method for speech recognition results that uses confidence on automatic speech recognition (ASR) as a feature. The ASR confidence feature indicates whether each word has been correctly recognized. The NER model is trained using ASR results with named entity (NE) labels as well as the corresponding transcriptions with NE labels. In experiments using support vector machines (SVMs) and speech data from Japanese newspaper articles, the proposed method outperformed a simple application of textbased NER to ASR results in NER Fmeasure by improving precision. . | Incorporating speech recognition confidence into discriminative named entity recognition of speech data Katsuhito Sudoh Hajime Tsukada Hideki Isozaki NTT Communication Science Laboratories Nippon Telegraph and Telephone Corporation 2-4 Hikaridai Seika-cho Keihanna Science City Kyoto 619-0237 Japan sudoh tsukada isozaki @ Abstract This paper proposes a named entity recognition NER method for speech recognition results that uses confidence on automatic speech recognition ASR as a feature. The ASR confidence feature indicates whether each word has been correctly recognized. The NER model is trained using ASR results with named entity NE labels as well as the corresponding transcriptions with NE labels. In experiments using support vector machines SVMs and speech data from Japanese newspaper articles the proposed method outperformed a simple application of textbased NER to ASR results in NER F-measure by improving precision. These results show that the proposed method is effective in NER for noisy inputs. 1 Introduction As network bandwidths and storage capacities continue to grow a large volume of speech data including broadcast news and PodCasts is becoming available. These data are important information sources as well as such text data as newspaper articles and WWW pages. Speech data as information sources are attracting a great deal of interest such as DARPA s global autonomous language exploitation GALE program. We also aim to use them for information extraction IE question answering and indexing. Named entity recognition NER is a key technique for IE and other natural language processing tasks. Named entities NEs are the proper expressions for things such as peoples names locations names and dates and NER identifies those expressions and their categories. Unlike text data speech data introduce automatic speech recognition ASR error problems to NER. Although improvements to ASR are needed developing a robust NER for noisy word sequences is .

• Báo cáo khoa học
• Incorporating speech recognition confidence
• discriminative named entity recognition
• speech data
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Bài giảng Xử lý ngôn ngữ tự nhiên
• Ngôn ngữ tự nhiên
• Kỹ thuật lập trình
• Xử lý nhập nhằng ngữ nghĩa
• Xử lý ngữ nghĩa
• Xử lý ngôn ngữ
• Natural Language Processing
• Ngôn ngữ lập trình
• Nhập nhằng cấu trúc
• Tri thức về ngôn ngữ
• Mô hình n gram
• Dịch máy
• Phương pháp dịch máy
• Hiểu ngôn ngữ
• Phân tích ngữ nghĩa
• Phân loại tin tự động
• Biểu diễn vị từ
• Thuộc tính về sự kiện
• Thuộc tính về sự kiện
• Mô hình ngôn ngữ Google Book N grams
• Mô hình ngôn ngữ KenLM
• Phương pháp làm mịn
• Tách từ tiếng Việt
• Gán nhãn từ loại
• Phân tích cú pháp
• Nghĩa từ vựng
• Phân giải nhập nhằng từ
• Phân loại văn bản
• Lexical chain
• Phản hồi thông tin
• Bài toán PTCP
• Cấu trúc ngữ pháp
• Phân tích cú pháp xác suất
• CKY kết hợp xác suất
• Văn phạm phi ngữ cảnh xác suất
• Hình thái học