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On term selection techniques for patent prior art search

Version 2 2024-06-05, 06:21
Version 1 2019-11-06, 13:50
conference contribution
posted on 2024-06-05, 06:21 authored by MG Far, S Sanner, Mohamed Reda BouadjenekMohamed Reda Bouadjenek, G Ferraro, D Hawking
In this paper, we investigate the inuence of term selection on retrieval performance on the CLEF-IP prior art test collection, using the Description section of the patent query with Language Model (LM) and BM25 scoring functions. We find that an oracular relevance feedback system that extracts terms from the judged relevant documents far outperforms the baseline and performs twice as well on MAP as the best competitor in CLEF-IP 2010. We find a very clear term selection value threshold for use when choosing terms. We also noticed that most of the useful feedback terms are actually present in the original query and hypothesized that the baseline system could be substantially improved by removing negative query terms. We tried four simple automated approaches to identify negative terms for query reduction but we were unable to notably improve on the baseline performance with any of them. However, we show that a simple, minimal interactive relevance feedback approach where terms are selected from only the first retrieved relevant document outperforms the best result from CLEF-IP 2010 suggesting the promise of interactive methods for term selection in patent prior art search.

History

Pagination

803-806

Location

Santiago, Chile

Start date

2015-08-09

End date

2015-08-13

ISBN-13

9781450336215

Language

eng

Publication classification

E1.1 Full written paper - refereed

Title of proceedings

SIGIR 2015 : Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

Event

Research and Development in Information Retrieval. Conference (2015 : Santiago, Chile)

Publisher

ACM

Place of publication

New York, N.Y.

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