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  1. Proceedings of the 2nd international workshop on Patent information retrieval (PaIR '09)
  2. Patent claim decomposition for improved information extraction
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A case for probabilistic logic for scalable patent retrieval
Ontologies and semantic mining for bio-technology and chemistry data and patents
Extracting problem solved concepts from patent documents
Identification of low/high retrievable patents using content-based features
Phrase-based document categorization revisited
Interactive constrained clustering for patent document set
A design rationale representation model using patent documents
Automatic translation of scholarly terms into patent terms
Using normalized alignment scores to detect incorrectly aligned segments
On the role of classification in patent invalidity searches
Patent claim decomposition for improved information extraction
FindCite: automatically finding prior art patents

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Patent claim decomposition for improved information extraction

Content Provider ACM Digital Library
Author Parapatics, Peter Dittenbach, Michael
Abstract In several application domains research in natural language processing and information extraction has spawned valuable tools that support humans in structuring, aggregating and managing large amounts of information available as text. Patent claims, although subject to a number of rigid constraints and therefore forced into foreseeable structures, are written in a language even good parsing algorithms tend to fail miserably at. This is primarily caused by long and complex sentences that are a concatenation of a multitude of descriptive elements. We present an approach to split patent claims into several parts in order to improve parsing performance for further automatic processing.
Starting Page 33
Ending Page 36
Page Count 4
File Format PDF
ISBN 9781605588094
DOI 10.1145/1651343.1651351
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2009-11-06
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Dependency parser Information extraction Natural language processing Patent claims
Content Type Text
Resource Type Article
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