TAILIEUCHUNG - Báo cáo khoa học: "Jointly Labeling Multiple Sequences: A Factorial HMM Approach"

We present new statistical models for jointly labeling multiple sequences and apply them to the combined task of partof-speech tagging and noun phrase chunking. The model is based on the Factorial Hidden Markov Model (FHMM) with distributed hidden states representing partof-speech and noun phrase sequences. We demonstrate that this joint labeling approach, by enabling information sharing between tagging/chunking subtasks, outperforms the traditional method of tagging and chunking in succession. Further, we extend this into a novel model, Switching FHMM, to allow for explicit modeling of cross-sequence dependencies based on linguistic knowledge. We report tagging/chunking accuracies for varying dataset sizes. | Jointly Labeling Multiple Sequences A Factorial HMM Approach Kevin Duh Department of Electrical Engineering University of Washington USA duh@ Abstract We present new statistical models for jointly labeling multiple sequences and apply them to the combined task of part-of-speech tagging and noun phrase chunking. The model is based on the Factorial Hidden Markov Model FHMM with distributed hidden states representing part-of-speech and noun phrase sequences. We demonstrate that this joint labeling approach by enabling information sharing between tagging chunking subtasks outperforms the traditional method of tagging and chunking in succession. Further we extend this into a novel model Switching FHMM to allow for explicit modeling of cross-sequence dependencies based on linguistic knowledge. We report tagging chunking accuracies for varying dataset sizes and show that our approach is relatively robust to data sparsity. 1 Introduction Traditionally various sequence labeling problems in natural language processing are solved by the cascading of well-defined subtasks each extracting specific knowledge. For instance the problem of information extraction from sentences may be broken into several stages First part-of-speech POS tagging is performed on the sequence of word tokens. This result is then utilized in noun-phrase and verbphrase chunking. Finally a higher-level analyzer extracts relevant information based on knowledge gleaned in previous subtasks. The decomposition of problems into well-defined subtasks is useful but sometimes leads to unnecessary errors. The problem is that errors in earlier subtasks will propagate to downstream subtasks ultimately deteriorating overall performance. Therefore a method that allows the joint labeling of subtasks is desired. Two major advantages arise from simultaneous labeling First there is more robustness against error propagation. This is especially relevant if we use probabilities in our models. Cascading .

• Báo cáo khoa học
• Jointly Labeling Multiple Sequences
• A Factorial HMM Approach
• Kevin Duh
• 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