Name
Abstract | ||
|---|---|---|
The automatic extraction of relations from unstructured natural text is challenging but offers practical solutions for many problems like automatic text understanding and semantic retrieval. Relation extraction can be formulated as a classification problem using support vector machines and kernels for structured data that may include parse trees to account for syntactic structure. In this paper we present new tree kernels over dependency parse trees automatically generated from natural language text. Experiments on a public benchmark data set show that our kernels with richer structural features significantly outperform all published approaches for kernel-based relation extraction from dependency trees. In addition we optimize kernel computations to improve the actual runtime compared to previous solutions. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-04174-7_18 | ECML/PKDD |
Keywords | Field | DocType |
natural language text,dependency tree kernels,kernel-based relation extraction,relation extraction,unstructured natural text,public benchmark data,automatic extraction,automatic text understanding,parse tree,dependency tree,dependency parse tree,support vector machine,natural language,dependency parsing,structured data | Parse tree,Computer science,Support vector machine,Tree kernel,Natural language,Information extraction,Artificial intelligence,Natural language processing,Parsing,Data model,Machine learning,Relationship extraction | Conference |
Volume | ISSN | Citations |
5782 | 0302-9743 | 18 |
PageRank | References | Authors |
0.75 | 18 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Frank Reichartz | 1 | 92 | 6.77 |
| Hannes Korte | 2 | 37 | 1.90 |
| Gerhard Paass | 3 | 1136 | 83.63 |