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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Domain Adaptation of General Natural Language Processing Tools for a Patent Claim Visualization System
verfasst von : Linda Andersson, Mihai Lupu, Allan Hanbury
Erschienen in: Multidisciplinary Information Retrieval
Verlag: Springer Berlin Heidelberg
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In this study we present a first step towards domain adaptation of Natural Language Processing (NLP) tools, which we use in a pipeline for a system to create a dependency claim graph (DCG). Our system takes advantage of patterns occurring in the patent domain notably of the characteristic of patent claims of containing technical terminology combined with legal rhetorical structure. Such patterns make the sentences generally difficult to understand for people, but can be leveraged by our system to assist the cognitive process of understanding the innovation described in the claim. We present this set of patterns, together with an extensive evaluation showing that the results are, even for this relatively difficult genre, at least 90% correct, as identified by both expert and non-expert users. The assessment of each generated DCG is based upon completeness, connection and a set of pre-defined relations.