TAILIEUCHUNG - Báo cáo khoa học: "Unsupervised Coreference Resolution in a Nonparametric Bayesian Model"

We present an unsupervised, nonparametric Bayesian approach to coreference resolution which models both global entity identity across a corpus as well as the sequential anaphoric structure within each document. While most existing coreference work is driven by pairwise decisions, our model is fully generative, producing each mention from a combination of global entity properties and local attentional state. Despite being unsupervised, our system achieves a MUC F1 measure on the MUC-6 test set, broadly in the range of some recent supervised results. . | Unsupervised Coreference Resolution in a Nonparametric Bayesian Model Aria Haghighi and Dan Klein Computer Science Division UC Berkeley aria42 klein @ Abstract We present an unsupervised nonparametric Bayesian approach to coreference resolution which models both global entity identity across a corpus as well as the sequential anaphoric structure within each document. While most existing coreference work is driven by pairwise decisions our model is fully generative producing each mention from a combination of global entity properties and local attentional state. Despite being unsupervised our system achieves a MUC Fl measure on the MUC-6 test set broadly in the range of some recent supervised results. 1 Introduction Referring to an entity in natural language can broadly be decomposed into two processes. First speakers directly introduce new entities into discourse entities which may be shared across discourses. This initial reference is typically accomplished with proper or nominal expressions. Second speakers refer back to entities already introduced. This anaphoric reference is canonically though of course not always accomplished with pronouns and is governed by linguistic and cognitive constraints. In this paper we present a nonparametric generative model of a document corpus which naturally connects these two processes. Most recent coreference resolution work has focused on the task of deciding which mentions noun phrases in a document are coreferent. The dominant approach is to decompose the task into a collection of pairwise coreference decisions. One then 848 applies discriminative learning methods to pairs of mentions using features which encode properties such as distance syntactic environment and so on Soon et al. 2001 Ng and Cardie 2002 . Although such approaches have been successful they have several liabilities. First rich features require plentiful labeled data which we do not have for coreference tasks in most domains and .

• Báo cáo khoa học
• Unsupervised Coreference Resolution
• Nonparametric Bayesian Model
• Aria Haghighi and Dan Klein
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Unsupervised Event Coreference Resolution
• Rich Linguistic Features
• Long Papers
• báo cáo ngôn ngữ
• ngôn ngữ tự nhiên
• 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