TAILIEUCHUNG - Báo cáo khoa học: "Relation Extraction Using Label Propagation Based Semi-supervised Learning"

Shortage of manually labeled data is an obstacle to supervised relation extraction methods. In this paper we investigate a graph based semi-supervised learning algorithm, a label propagation (LP) algorithm, for relation extraction. It represents labeled and unlabeled examples and their distances as the nodes and the weights of edges of a graph, and tries to obtain a labeling function to satisfy two constraints: 1) it should be fixed on the labeled nodes, 2) it should be smooth on the whole graph. . | Relation Extraction Using Label Propagation Based Semi-supervised Learning Jinxiu Chen1 Donghong Ji1 Chew Lim Tan2 Zhengyu Niu1 institute for Infocomm Research 2Department of Computer Science 21 Heng Mui Keng Terrace National University of Singapore 119613 Singapore 117543 Singapore jinxiu dhji zniu @ tancl@ Abstract Shortage of manually labeled data is an obstacle to supervised relation extraction methods. In this paper we investigate a graph based semi-supervised learning algorithm a label propagation LP algorithm for relation extraction. It represents labeled and unlabeled examples and their distances as the nodes and the weights of edges of a graph and tries to obtain a labeling function to satisfy two constraints 1 it should be fixed on the labeled nodes 2 it should be smooth on the whole graph. Experiment results on the ACE corpus showed that this LP algorithm achieves better performance than SVM when only very few labeled examples are available and it also performs better than bootstrapping for the relation extraction task. 1 Introduction Relation extraction is the task of detecting and classifying relationships between two entities from text. Many machine learning methods have been proposed to address this problem . supervised learning algorithms Miller et al. 2000 Zelenko et al. 2002 Culotta and Soresen 2004 Kambhatla 2004 Zhou et al. 2005 semi-supervised learning algorithms Brin 1998 Agichtein and Gravano 2000 Zhang 2004 and unsupervised learning algorithms Hasegawa et al. 2004 . Supervised methods for relation extraction perform well on the ACE Data but they require a large amount of manually labeled relation instances. Unsupervised methods do not need the definition of relation types and manually labeled data but they cannot detect relations between entity pairs and its result cannot be directly used in many NLP tasks since there is no relation type label attached to each instance in clustering result. Considering both

• Báo cáo khoa học
• Relation Extraction
• Label Propagation Based Semi supervised Learning
• Jinxiu Chen
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Luận án Tiến sĩ
• Công nghệ thông tin
• Semantic relation extraction
• Khoa học máy tính
• Advanced deep learning models
• Computer science
• Shortest dependency path
• BMC Bioinformatics
• Predicate argument structures
• Biomedical relation extraction
• Open information extraction
• Fundamental technology
• Reducing Wrong Labels
• Distant Supervision
• báo cáo ngôn ngữ
• ngôn ngữ tự nhiên
• A Graph based Cross lingual Projection Approach
• Weakly Supervised Relation Extraction
• Gary Geunbae Lee
• Pattern Learning
• Hierarchical Topic Model
• Temporally Anchored Relation Extraction
• Guillermo Garrido
• Anselmo Penas
• End to End Relation Extraction
• External Semantic Repositories
• An Error Analysis
• Social Media Documents
• labeled data
• Multi Task Transfer Learning
• Jing Jiang
• Unsupervised Relation Extraction
• Mining Wikipedia Texts
• Information from the Web
• Semi supervised Relation Extraction
• Large scale Word Clustering
• Ang Sun
• Exploiting Syntactico Semantic Structures
• Yee Seng Chan
• Composite Kernels
• Frank Reichartz Hannes Korte Gerhard Paass Fraunhofer
• The Tradeoffs Between Open
• Traditional Relation Extraction
• Michele Banko and Oren Etzioni
• A Novel Feature based Approach to Chinese
• Entity Relation Extraction
• Long Papers
• Modeling Commonality
• Related Classes in Relation Extraction
• Zhou GuoDong Su Jian Zhang Min
• Exploring Correlation
• Dependency Relation Paths
• Answer Extraction
• Using Corpus Statistics on Entities
• Improve Semi supervised Relation
• Extraction from the Web
• Instance Splitting Strategies
• Dependency Relation based
• Information Extraction
• an Unsupervised Web Relation
• Extraction System
• Benjamin Rosenfeld
• Exploring Various Knowledge
• ZHOU GuoDong
• Simple Algorithms
• Complex Relation Extraction
• Applications to Biomedical IE
• Dependency Tree Kernels
• Aron Culotta