Search for patents using treatment and causal relationships

Authors:
Ashwathi Krishnan;Alfonso F. Cardenas;Derek Springer
Affiliations:
University of California, Los Angeles, Los Angeles, CA, USA;University of California, Los Angeles, Los Angeles, CA, USA;University of California, Los Angeles, Los Angeles, CA, USA
Venue:
PaIR '10 Proceedings of the 3rd international workshop on Patent information retrieval
Year:
2010

Citing 6
Cited 0

A patent search and classification system

Proceedings of the fourth ACM conference on Digital libraries
Collection selection and results merging with topically organized U.S. patents and TREC data

Proceedings of the ninth international conference on Information and knowledge management
Learning surface text patterns for a Question Answering system

ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Automatic detection of causal relations for Question Answering

MultiSumQA '03 Proceedings of the ACL 2003 workshop on Multilingual summarization and question answering - Volume 12
Espresso: leveraging generic patterns for automatically harvesting semantic relations

ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Automatic detection of treatment relationships for patent retrieval

Proceedings of the 1st ACM workshop on Patent information retrieval

Quantified Score

Hi-index	0.00

Visualization

Abstract

An interesting area of research in information retrieval is that of relationship extraction. The ability to scan an article or set of articles and extract relationships such as "X treats Y" or "A happens because of B" is key to retrieving articles of interest to a large population. In this paper, we describe our method of identifying and extracting treatment and causal relationships from medical patent documents. We use a medical patent corpus to show that using relationship patterns to retrieve medical patent documents helps improving the recall of the system immensely. We also show that expanding our search to look for a broader set of relationships and including causal relationships along with treatment relationships, addresses a larger range of patent documents thereby improving the recall of the system significantly.