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  1. Proceedings of the 2nd international workshop on Patent information retrieval (PaIR '09)
  2. Phrase-based document categorization revisited
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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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Phrase-based document categorization revisited

Content Provider ACM Digital Library
Author Beney, Jean G. Koster, Cornelis H.A.
Abstract This paper takes a fresh look at an old idea in Information Retrieval: the use of linguistically extracted phrases as terms in the automatic categorization (aka classification) of documents. Until now, there was found little or no evidence that document categorization benefits from the application of linguistics techniques. Classification algorithms using the most cleverly designed linguistical representations typically do no better than those using simply the bag-of-words representation. Shallow linguistical techniques are used routinely, but their positive effect on the accuracy is small at best. We have investigated the use of dependency triples as terms in document categorization, which are derived according to a dependency model based on the notion of aboutness. The documents are syntactically analyzed by a parser and transduced to dependency trees, which in turn are unnested into dependency triples following the aboutness-based model. In the process, various normalizing transformations are applied to enhance recall. We describe a sequence of large-scale experiments with different document representations, test collections and even languages, presenting evidence that adding such triples to the words in a bag-of-terms document representation may lead to a significant increase in the accuracy of document categorization.
Starting Page 49
Ending Page 56
Page Count 8
File Format PDF
ISBN 9781605588094
DOI 10.1145/1651343.1651357
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2009-11-06
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Aboutness Text categorization Dependency triples Linguistic terms
Content Type Text
Resource Type Article
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