Difference between revisions of "Hoffmann et al., ACL 2010"
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This is a [[Category::paper]] introducing LUCHS, a self-supervised relations-specific IE system capable of learning more than 5000 relations with an average F1 score of 61%. The system applies ''dynamic lexicon features learning'' is applied as a semi-supervise learning solution cope with sparse training data. | This is a [[Category::paper]] introducing LUCHS, a self-supervised relations-specific IE system capable of learning more than 5000 relations with an average F1 score of 61%. The system applies ''dynamic lexicon features learning'' is applied as a semi-supervise learning solution cope with sparse training data. | ||
| − | + | == System Architecture == | |
| + | The following figure summarizes the architecture of LUCHS. | ||
| + | |||
[[File:LUCHSArchitecture.png|450px]] | [[File:LUCHSArchitecture.png|450px]] | ||
| + | |||
| + | * A classifier is trained by [[Wikipedia|Wikipedia pages]] containing infoboxes, which can be used to decide which schema should be applied to an article without infobox; | ||
| + | * Training Data is generated heuristically by the '''Matcher''' (e.g. the [[Wikipedia]] article "Jerry Seinfeld" contains sentence "Seinfeld was born in Brooklyn, New York." and at the infobox within the same page, contains relation pair "birth_place = Brooklyn"; the '''Matcher''' heuristically generate training data for extractors of different relations; | ||
| + | |||
== Brief description of the method == | == Brief description of the method == | ||
Revision as of 12:23, 30 September 2011
Contents
Citation
Raphael Hoffmann, Congle Zhang, and Daniel S. Weld. 2010. Learning 5000 relational extractors. In ACL '10 (Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics)
Online version
Summary
This is a paper introducing LUCHS, a self-supervised relations-specific IE system capable of learning more than 5000 relations with an average F1 score of 61%. The system applies dynamic lexicon features learning is applied as a semi-supervise learning solution cope with sparse training data.
System Architecture
The following figure summarizes the architecture of LUCHS.
- A classifier is trained by Wikipedia pages containing infoboxes, which can be used to decide which schema should be applied to an article without infobox;
- Training Data is generated heuristically by the Matcher (e.g. the Wikipedia article "Jerry Seinfeld" contains sentence "Seinfeld was born in Brooklyn, New York." and at the infobox within the same page, contains relation pair "birth_place = Brooklyn"; the Matcher heuristically generate training data for extractors of different relations;
