NDLI: Increasing NER Recall with Minimal Precision Loss

Content Provider	IEEE Xplore Digital Library
Author	Kuperus, J. Veenman, C.J. van Keulen, M.
Copyright Year	2013
Description	Author affiliation: Kecida, Netherlands Forensic Inst., The Hague, Netherlands (Veenman, C.J.) \|\| EEMCS, Univ. of Twente, Enschede, Netherlands (van Keulen, M.) \|\| Sogeti Nederland B.V., Vianen, Netherlands (Kuperus, J.)
Abstract	Named Entity Recognition (NER) is broadly used as a first step toward the interpretation of text documents. However, for many applications, such as forensic investigation, recall is currently inadequate, leading to loss of potentially important information. Entity class ambiguity cannot be resolved reliably due to the lack of context information or the exploitation thereof. Consequently, entity classification introduces too many errors, leading to severe omissions in answers to forensic queries. We propose a technique based on multiple candidate labels, effectively postponing decisions for entity classification to query time. Entity resolution exploits user feedback: a user is only asked for feedback on entities relevant to his/her query. Moreover, giving feedback can be stopped anytime when query results are considered good enough. We propose several interaction strategies that obtain increased recall with little loss in precision.
Starting Page	106
Ending Page	111
File Size	959675
Page Count	6
File Format	PDF
ISBN	9780769550626
DOI	10.1109/EISIC.2013.23
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-12
Publisher Place	Sweden
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Context Named Entity Recognition Forensics Probabilistic Named Entity Recognition Europe Probability Probabilistic logic Proposals PNER Reference Ambiguity Semantics Precision Structural Ambiguity Targeted Feedback NER Recall Semantic Ambiguity Ambiguity
Content Type	Text
Resource Type	Article

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