tailieunhanh - Báo cáo khoa học: "Multi-Class Composite N-gram Language Model for Spoken Language Processing Using Multiple Word Clusters"

In this paper, a new language model, the Multi-Class Composite N-gram, is proposed to avoid a data sparseness problem for spoken language in that it is difficult to collect training data. The Multi-Class Composite N-gram maintains an accurate word prediction capability and reliability for sparse data with a compact model size based on multiple word clusters, called MultiClasses. In the Multi-Class, the statistical connectivity at each position of the N-grams is regarded as word attributes, and one word cluster each is created to represent the positional attributes. . | Multi-Class Composite N-gram Language Model for Spoken Language Processing Using Multiple Word Clusters Hirofumi Yamamoto ATR SLT 2-2-2 Hikaridai Seika-cho Soraku-gun Kyoto-fu Japan yama@ Yoshinori Sagisaka GITI ATR SLT 1-3-10 Nishi-Waseda Shinjuku-ku Tokyo-to Japan sagisaka@ Shuntaro Isogai Waseda University 3-4-1 Okubo Shinjuku-ku Tokyo-to Japan isogai@ Abstract In this paper a new language model the Multi-Class Composite N-gram is proposed to avoid a data sparseness problem for spoken language in that it is difficult to collect training data. The Multi-Class Composite N-gram maintains an accurate word prediction capability and reliability for sparse data with a compact model size based on multiple word clusters called MultiClasses. In the Multi-Class the statistical connectivity at each position of the N-grams is regarded as word attributes and one word cluster each is created to represent the positional attributes. Furthermore by introducing higher order word N-grams through the grouping of frequent word successions Multi-Class N-grams are extended to Multi-Class Composite N-grams. In experiments the Multi-Class Composite N-grams result in lower perplexity and a 16 lower word error rate in speech recognition with a 40 smaller parameter size than conventional word 3-grams. 1 Introduction Word N-grams have been widely used as a statistical language model for language processing. Word N-grams are models that give the transition probability of the next word from the previous N - 1 word sequence based on a statistical analysis of the huge text corpus. Though word N-grams are more effective and flexible than rule-based grammatical constraints in many cases their performance strongly depends on the size of training data since they are statistical models. In word N-grams the accuracy of the word prediction capability will increase according to the number of the order N but also the number of word transition .

• Báo cáo khoa học
• Multi Class Composite
• N gram Language Model for Spoken Language Processing
• Multiple Word Clusters
• 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