TAILIEUCHUNG - Báo cáo khoa học: "Improving Statistical Natural Language Translation with Categories and Rules"

This paper describes an all level approach on statistical natural language translation (SNLT). W i t h o u t any predefined knowledge the system learns a statistical translation lexicon (STL), word classes (WCs) and translation rules (TRs) from a parallel corpus thereby producing a generalized form of a word alignment (WA). The translation process itself is realized as a beam search. In our method example-based techniques enter an overall statistical approach leading to about 50 percent correctly translated sentences applied to the very difficult EnglishGerman V E R B M O B I L spontaneous speech corpus. . | Improving Statistical Natural Language Translation with Categories and Rules Franz Josef Och and Hans Weber FAU Erlangen - Computer Science Institute IMMD VIII - Artificial Intelligence Am Weichselgarten 9 91058 Erlangen - Tennenlohe Germany faoch weber @ Abstract This paper describes an all level approach on statistical natural language translation SNLT . Without any predefined knowledge the system learns a statistical translation lexicon STL word classes WCs and translation rules TRs from a parallel corpus thereby producing a generalized form of a word alignment WA . The translation process itself is realized as a beam search. In our method example-based techniques enter an overall statistical approach leading to about 50 percent correctly translated sentences applied to the very difficult English-German Verbmobil spontaneous speech corpus. 1 Introduction In SNLT the transfer itself is realized as a maximization process of the form Trans d argmaxe P e d 1 Here d is a given source language SL sentence which has to be translated into a target language TL sentence e. In order to model the distributions P e d all approaches in SNLT use a divide and conquer strategy of approximating P e d by a combination of simpler models. The problem is to reduce parameters in a sufficient way but end up with a model still able to describe the linguistic facts of natural language translation. The work presented here uses two approximations for P e d . One approximation is used for to gain the relevant parameters in training while a modified formula is subject of decoding translations. In detail we impose the following modifications with respect to approaches published in the last decade 1. A refined distance weight for the STL probabilities is used which allows for a good modeling of the effects caused by syntactic phrases. 2. In order to account for collocations a WA technique is used where one-to-n and n-to-one WAs are allowed. 3. For the .

• Báo cáo khoa học
• Improving Statistical
• Natural Language Translation
• Categories and Rules
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Improving Statistical Machine Translation
• Monolingual Collocation
• Zhanyi Liu
• báo cáo ngôn ngữ
• ngôn ngữ tự nhiên
• Improving Arabic to English Statistical Machine
• Translation by Reordering
• Post verbal Subjects for Alignment
• Phrase Linguistic Classification
• Generalization
• Improving Pronoun Translation
• Statistical Machine Translation
• Liane Guillou
• scientific reports
• model language
• process natural language
• Improving Grammaticality
• Statistical Sentence Generation Introducing a Dependency Spanning Tree Algorithm
• Argument Satisfaction Model
• BMC Bioinformatics
• Mass spectrometry
• Statistical modeling
• Molecule identification
• Silico fragmentation
• Enrichment analysis
• Gene set enrichment analysis
• Statistical power
• Molecular attribute
• Quantitative molecular methods
• Polymerase chain reaction
• Quantitative nucleic acid
• Epidemiological contexts
• Bài giảng Xử lý 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