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Patent classification experiments with the Linguistic Classification System LCS
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@MISC{Verberne_patentclassification,
author = {Suzan Verberne and Merijn Vogel},
title = {Patent classification experiments with the Linguistic Classification System LCS},
year = {}
}
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Abstract
Abstract. In the context of the CLEF-IP 2010 classification task, we conducted a series of experiments with the Linguistic Classification System (LCS). We compared two document representations for patent abstracts: a bag-of-words representation and a syntactic/semantic representation containing both words and dependency triples. We evaluated two types of output: using a fixed cut-off on the ranking of the classes and using a flexible cut-off based on a threshold on the classification scores. Using the Winnow classifier, we obtained an improvement in classification scores when triples are added to the bag of words. However, our results are remarkably better on a held-out subset of the target data than on the 2 000-topic test set. The main findings of this paper are: (1) adding dependency triples to words has a positive effect on classification accuracy and (2) selecting classes by using a threshold on the classification scores instead of returning a fixed number of classes per document improves classification scores while at the same time it lowers the number of classes needs to be judged manually by the professionals at the patent office. 1
Keyphrases
classification score patent classification experiment linguistic classification system lcs dependency triple linguistic classification system held-out subset document representation class need positive effect flexible cut-off winnow classifier fixed number selecting class bag-of-words representation classification task patent abstract 000-topic test set main finding target data fixed cut-off patent office classification accuracy syntactic semantic representation
