TAILIEUCHUNG - Báo cáo khoa học: "Simple Algorithms for Complex Relation Extraction with Applications to Biomedical IE"

A complex relation is any n-ary relation in which some of the arguments may be be unspecified. We present here a simple two-stage method for extracting complex relations between named entities in text. The first stage creates a graph from pairs of entities that are likely to be related, and the second stage scores maximal cliques in that graph as potential complex relation instances. We evaluate the new method against a standard baseline for extracting genomic variation relations from biomedical text. ing named entities. Such relations would be extremely useful in applications like question answering, automatic database generation, and. | Simple Algorithms for Complex Relation Extraction with Applications to Biomedical IE Ryan McDonald1 Fernando Pereira1 Seth Kulick2 1 CIS and 2IRCS University of Pennsylvania Philadelphia PA ryantm pereira @ skulick@ Scott Winters Yang Jin Pete White Division of Oncology Children s Hospital of Pennsylvania Philadelphia PA winters jin white @ Abstract A complex relation is any n-ary relation in which some of the arguments may be be unspecified. We present here a simple two-stage method for extracting complex relations between named entities in text. The first stage creates a graph from pairs of entities that are likely to be related and the second stage scores maximal cliques in that graph as potential complex relation instances. We evaluate the new method against a standard baseline for extracting genomic variation relations from biomedical text. 1 Introduction Most research on text information extraction IE has focused on accurate tagging of named entities. Successful early named-entity taggers were based on finite-state generative models Bikel et al. 1999 . More recently discriminatively-trained models have been shown to be more accurate than generative models McCallum et al. 2000 Lafferty et al. 2001 Kudo and Matsumoto 2001 . Both kinds of models have been developed for tagging entities such as people places and organizations in news material. However the rapid development of bioinformatics has recently generated interest on the extraction of biological entities such as genes Collier et al. 2000 and genomic variations McDonald et al. 2004b from biomedical literature. The next logical step for IE is to begin to develop methods for extracting meaningful relations involv ing named entities. Such relations would be extremely useful in applications like question answering automatic database generation and intelligent document searching and indexing. Though not as well studied as entity extraction relation extraction has .

• Báo cáo khoa học
• Simple Algorithms
• Complex Relation Extraction
• Applications to Biomedical IE
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Three metaphor less simple algorithms
• Solving optimization problems
• Metaphor less algorithms
• Shown good performance
• 10 fixed dimension multimodal functions
• Design and Analysis of Algorithms
• Analysis of Algorithms
• Design of Algorithms
• Data structures
• Linear programming
• Simple uniform
• Universal hashing
• Distributed Systems
• Lecture Distributed Systems
• Routing Algorithms
• Good paths
• Destination based forwarding
• The simple distributed algorithm
• Data Mining
• Alternative Techniques
• training data
• Simple models
• Logistic regression
• Decision trees
• Machine learning
• Lecture Machine learning
• Machine learning algorithms
• Data analysis methods
• Logistic regression likelihood