TAILIEUCHUNG - Báo cáo khoa học: "Deep dependencies from context-free statistical parsers: correcting the surface dependency approximation"

We present a linguistically-motivated algorithm for reconstructing nonlocal dependency in broad-coverage context-free parse trees derived from treebanks. We use an algorithm based on loglinear classifiers to augment and reshape context-free trees so as to reintroduce underlying nonlocal dependencies lost in the context-free approximation. We find that our algorithm compares favorably with prior work on English using an existing evaluation metric, and also introduce and argue for a new dependency-based evaluation metric. . | Deep dependencies from context-free statistical parsers correcting the surface dependency approximation Roger Levy Christopher D. Manning Department of Linguistics Departments of Computer Science and Linguistics Stanford University Stanford University rog@ manning@ Abstract We present a linguistically-motivated algorithm for reconstructing nonlocal dependency in broad-coverage context-free parse trees derived from treebanks. We use an algorithm based on loglinear classifiers to augment and reshape context-free trees so as to reintroduce underlying nonlocal dependencies lost in the context-free approximation. We find that our algorithm compares favorably with prior work on English using an existing evaluation metric and also introduce and argue for a new dependency-based evaluation metric. By this new evaluation metric our algorithm achieves 60 error reduction on gold-standard input trees and 5 error reduction on state-of-the-art machine-parsed input trees when compared with the best previous work. We also present the first results on nonlocal dependency reconstruction for a language other than English comparing performance on English and German. Our new evaluation metric quantitatively corroborates the intuition that in a language with freer word order the surface dependencies in context-free parse trees are a poorer approximation to underlying dependency structure. 1 Introduction While parsers are been used for other purposes the primary motivation for syntactic parsing is as an aid to semantic interpretation in pursuit of broader goals of natural language understanding. Proponents of traditional deep or precise approaches to syntax such as GB CCG HPSG LFG or TAG have argued that sophisticated grammatical formalisms are essential to resolving various hidden relationships such as the source phrase of moved wh-phrases in questions and relativizations or the controller of clauses without an overt subject. Knowledge of these hidden .

• Báo cáo khoa học
• Deep dependencies
• context free statistical parsers
• correcting the surface dependency approximation
• 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