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dc.date.accessioned2020-08-06T18:11:55Z
dc.date.available2020-08-06T18:11:55Z
dc.date.created2019-11-26T12:22:54Z
dc.date.issued2019
dc.identifier.citationNooralahzadeh, Farhad Lønning, Jan Tore Øvrelid, Lilja . Reinforcement-based denoising of distantly supervised NER with partial annotation. Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019). 2019, 225-234 Association for Computational Linguistics
dc.identifier.urihttp://hdl.handle.net/10852/78169
dc.description.abstractExisting named entity recognition (NER) systems rely on large amounts of human-labeled data for supervision. However, obtaining large-scale annotated data is challenging particularly in specific domains like health-care, e-commerce and so on. Given the availability of domain specific knowledge resources, (e.g., ontologies, dictionaries), distant supervision is a solution to generate automatically labeled training data to reduce human effort. The outcome of distant supervision for NER, however, is often noisy. False positive and false negative instances are the main issues that reduce performance on this kind of auto-generated data. In this paper, we explore distant supervision in a supervised setup. We adopt a technique of partial annotation to address false negative cases and implement a reinforcement learning strategy with a neural network policy to identify false positive instances. Our results establish a new state-of-the-art on four benchmark datasets taken from different domains and different languages. We then go on to show that our model reduces the amount of manually annotated data required to perform NER in a new domain.
dc.languageEN
dc.publisherAssociation for Computational Linguistics
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleReinforcement-based denoising of distantly supervised NER with partial annotation
dc.typeChapter
dc.creator.authorNooralahzadeh, Farhad
dc.creator.authorLønning, Jan Tore
dc.creator.authorØvrelid, Lilja
cristin.unitcode185,15,5,80
cristin.unitnameCentre for Scalable Data Access
cristin.ispublishedtrue
cristin.fulltextoriginal
dc.identifier.cristin1752381
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.btitle=Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)&rft.spage=225&rft.date=2019
dc.identifier.startpage225
dc.identifier.endpage234
dc.identifier.pagecount281
dc.identifier.doihttp://dx.doi.org/ 10.18653/v1/D19-6125
dc.identifier.urnURN:NBN:no-81271
dc.subject.nviVDP::Datateknologi: 551
dc.type.documentBokkapittel
dc.type.peerreviewedPeer reviewed
dc.source.isbn978-1-950737-789
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/78169/1/D19-6125%2B%25282%2529.pdf
dc.type.versionPublishedVersion
cristin.btitleProceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)
dc.relation.projectNOTUR/NORSTORE/NN9447K
dc.relation.projectNFR/237898


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