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  1. Proceedings of the 2nd international workshop on Patent information retrieval (PaIR '09)
  2. Identification of low/high retrievable patents using content-based features
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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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Identification of low/high retrievable patents using content-based features

Content Provider ACM Digital Library
Author Rauber, Andreas Bashir, Shariq
Abstract Document retrievability is a measurement used in information retrieval for identifying the bias of retrieval systems. In order to measure system bias for a specific document collection, an exhaustive set of queries is processed, measuring the frequency with which each document is retrieved. For better understanding and handling system bias, we need to understand the characteristics of documents that influence retrievability, and ideally be able to identify documents with high and low retrievability in advance. For this purpose, we identify a number of content-based features, which can be used effectively to classify a corpus into documents with low and high retrievability w.r.t a specific retrieval system. Our experiments on patent collections show that these features can achieve more than 80% classification accuracy on different systems, and hint at the need to combine different retrieval systems for optimizing recall.
Starting Page 9
Ending Page 16
Page Count 8
File Format PDF
ISBN 9781605588094
DOI 10.1145/1651343.1651346
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2009-11-06
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Patent retrieval Feature analysis Retrievability Text classification
Content Type Text
Resource Type Article
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