TAILIEUCHUNG - Báo cáo khoa học: "Combining Lexical, Syntactic, and Semantic Features with Maximum Entropy Models for Extracting Relations"

Extracting semantic relationships between entities is challenging because of a paucity of annotated data and the errors induced by entity detection modules. We employ Maximum Entropy models to combine diverse lexical, syntactic and semantic features derived from the text. Our system obtained competitive results in the Automatic Content Extraction (ACE) evaluation. Here we present our general approach and describe our ACE results. | Combining Lexical Syntactic and Semantic Features with Maximum Entropy Models for Extracting Relations Nanda Kambhatla IBM T. J. Watson Research Center 1101 Kitchawan Road Route 134 Yorktown Heights NY 10598 nanda@ Abstract Extracting semantic relationships between entities is challenging because of a paucity of annotated data and the errors induced by entity detection modules. We employ Maximum Entropy models to combine diverse lexical syntactic and semantic features derived from the text. Our system obtained competitive results in the Automatic Content Extraction ACE evaluation. Here we present our general approach and describe our ACE results. 1 Introduction Extraction of semantic relationships between entities can be very useful for applications such as biography extraction and question answering . to answer queries such as Where is the Taj Mahal . Several prior approaches to relation extraction have focused on using syntactic parse trees. For the Template Relations task of MUC-7 BBN researchers Miller et al. 2000 augmented syntactic parse trees with semantic information corresponding to entities and relations and built generative models for the augmented trees. More recently Zelenko et al. 2003 have proposed extracting relations by computing kernel functions between parse trees and Culotta and Sorensen 2004 have extended this work to estimate kernel functions between augmented dependency trees. We build Maximum Entropy models for extracting relations that combine diverse lexical syntactic and semantic features. Our results indicate that using a variety of information sources can result in improved recall and overall F measure. Our approach can easily scale to include more features from a multitude of . WordNet gazat-teers output of other semantic taggers can be brought to bear on this task. In this paper we present our general approach describe the features we currently use and show the results of our participation in the ACE

• Báo cáo khoa học
• Combining Lexical
• Syntactic
• Semantic Features
• Maximum Entropy Models
• Extracting Relations
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Combining Lexical Semantic Resources
• Question & Answer Archives
• Translation Based Answer Finding
• báo cáo ngôn ngữ
• A High Accurate Chinese English NE Backward Translation System
• Combining Both Lexical Information
• Web Statistics
• The Development of Lexical Resources
• Information Extraction from Text Combining Word Net
• Dewey Decimal Classification
• scientific reports
• model language
• process natural language
• Combining Unsupervised
• Lexical Knowledge Methods
• Word Sense Disambiguation
• Combining a Statistical Language Model
• Logistic Regression to Predict the Lexical
• Syntactic Difficulty of Texts for FFL
• Improved Lexical Alignment
• Combining Multiple Reified Alignments
• Dan Tufi
• 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