TAILIEUCHUNG - Báo cáo khoa học: "Lexicalized phonotactic word segmentation"

This paper presents a new unsupervised algorithm (WordEnds) for inferring word boundaries from transcribed adult conversations. Phone ngrams before and after observed pauses are used to bootstrap a simple discriminative model of boundary marking. This fast algorithm delivers high performance even on morphologically complex words in English and Arabic, and promising results on accurate phonetic transcriptions with extensive pronunciation variation. | Lexicalized phonotactic word segmentation Margaret M. Fleck Department of Computer Science University of Illinois Urbana IL 61801 USA mfleck@ Abstract This paper presents a new unsupervised algorithm WordEnds for inferring word boundaries from transcribed adult conversations. Phone ngrams before and after observed pauses are used to bootstrap a simple discriminative model of boundary marking. This fast algorithm delivers high performance even on morphologically complex words in English and Arabic and promising results on accurate phonetic transcriptions with extensive pronunciation variation. Expanding training data beyond the traditional miniature datasets pushes performance numbers well above those previously reported. This suggests that WordEnds is a viable model of child language acquisition and might be useful in speech understanding. 1 Introduction Words are essential to most models of language and speech understanding. Word boundaries define the places at which speakers can fluently pause and limit the application of most phonological rules. Words are a key constituent in structural analyses the output of morphological rules and the constituents in syntactic parsing. Most speech recognizers are word-based. And words are entrenched in the writing systems of many languages. Therefore it is generally accepted that children learning their first language must learn how to segment speech into a sequence of words. Similar but more limited learning occurs when adults hear speech containing unfamiliar words. These words must be accurately delimited so that they can be added to the lexicon and nearby familiar words recognized correctly. Current speech recognizers typically misinterpret such speech. This paper will consider algorithms which segment phonetically transcribed speech into words. For example Figure 1 shows a transcribed phrase from the Buckeye corpus Pitt et al. 2005 Pitt et al. 2007 and the automatically segmented output. Like almost all .

• Báo cáo khoa học
• Lexicalized phonotactic
• word segmentation
• Margaret M
• Fleck
• báo cáo khoa học
• báo cáo 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 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