TAILIEUCHUNG - Báo cáo khoa học: "Tree-based Analysis of Simple Recurrent Network Learning"

Using this approach, an SRN. trained to the phonotactics of a Dutch monosyllabic corpus containing 4500 words, was reported to distinguish words from non-words with 7 % error, Since the phonotactics of a given language is represented by the constraints allowing a given sequence to be a word or not, and the SRN managed to distinguish words from random strings with tolerable error, the authors claim that SRNs are able to learn the phonotactics of Dutch language. | Tree-based Analysis of Simple Recurrent Network Learning Ivelin Stoianov Dept. Alfa-Informatica Faculty of Arts Groningen University POBox 716 9700 AS Groningen The Netherlands Email stoianov@ 1 Simple recurrent networks for natural language phonotactics analysis. In searching for a connectionist paradigm capable of natural language processing many researchers have explored the Simple Recurrent Network SRN such as Elman 1990 Cleermance 1993 Reilly 1995 and Lawrence I996 . SRNs have a context layer that keeps track of the past hidden neuron activations and enables them to deal with sequential data. The events in Natural Language span time so SRNs are needed to deal with them. Among the various levels of language processing a phonological level can be distinguished. The Phonology deals with phonemes or graphemes - the latter in the case when one works with orthographic word representations. The principles governing the combinations of these symbols is called phonotactics Laver 1994 . It is a good starting point for connectionist language analysis because there are not too many basic entities. The number of the symbols varies between 26 for the Latin graphemes and 50 for the phonemes . Recently some experiments considering phonotactics modelling with SRNs have been carried out by Stoianov 1997 Rodd 1997 . The neural network in Stoianov 1997 was trained to study the phonotactics of a large Dutch word corpus. This problem was implemented as an SRN learning task -to predict the symbol following the left context given to the input layer so far. Words were applied to the network symbol by symbol which in turn were encoded orthogonally that is one node standing for one symbol Fig. 1 . An extra symbol was used as a delimiter. After the ưaining the network responded to the input with different neuron activations at the output layer. The more active a given output neuron is the higher the probability is that it is a successor. The authors used a so-called optimal .

• Báo cáo khoa học
• Tree based Analysis
• Simple Recurrent Network Learning
• Ivetin Stoianov
• 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