Indirectly Named Entity Recognition

[EN] We define here indirectly named entities, as a term to denote multiword expressions referring to known named entities by means of periphrasis.  While named entity recognition is a classical task in natural language processing, little attention has been paid to indirectly named entities and their treatment. In this paper, we try to address this gap, describing issues related to the detection and understanding of indirectly named entities in texts. We introduce a proof of concept for retrieving both lexicalised and non-lexicalised indirectly named entities in French texts. We also show example cases where this proof of concept is applied, and discuss future perspectives. We have initiated the creation of a first lexicon of 712 indirectly named entity entries that is available for future research.This research has been funded by the FEDER (Fonds européen de développement régional) and selected by the French-Swiss programme Interreg V. We would like to thank Claire Wuillemin for her preliminary work in the DecRIPT project about the State-of-the-Art in NER and SER in 2020. We would also like to thank for their advice Gilles Falquet, Luka Nerima, Eric Wehrli and Jean-Philippe Goldman at the University of Geneva.Kauffmann, A.; Rey, F.; Atanassova, I.; Gaudinat, A.; Greenfield, P.; Madinier, H.; Cardey, S. (2021). Indirectly Named Entity Recognition. Journal of Computer-Assisted Linguistic Research. 5(1):27-46. https://doi.org/10.4995/jclr.2021.15922OJS274651Abney, Steven. 1987. "The English Noun Phrase in its Sentential Aspect." PhD diss., Massachusetts Institute of Technology.Alsharaf, H., S. Cardey, P. Greenfield, D. Limame, and I. Skouratov. 2003. "Fixedness, the complexity and fragility of the phenomenon: some solutions for natural language processing." In Proceedings of ICL17. Prague, Czech Republic: Matfyzpress.Ananthanarayanan, Rema, Vijil Chenthamarakshan, Prasad M Deshpande, and Raghuram Krishnapuram. 2008. 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[Abstract] Parsing is a core natural language processing technique that can be used to obtain the structure underlying sentences in human languages. Named entity recognition (NER) is the task of identifying the entities that appear in a text. NER is a challenging natural language processing task that is essential to extract knowledge from texts in multiple domains, ranging from financial to medical. It is intuitive that the structure of a text can be helpful to determine whether or not a certain portion of it is an entity and if so, to establish its concrete limits. However, parsing has been a relatively little-used technique in NER systems, since most of them have chosen to consider shallow approaches to deal with text. In this work, we study the characteristics of NER, a task that is far from being solved despite its long history; we analyze the latest advances in parsing that make its use advisable in NER settings; we review the different approaches to NER that make use of syntactic information; and we propose a new way of using parsing in NER based on casting parsing itself as a sequence labeling task.Xunta de Galicia; ED431C 2020/11Xunta de Galicia; ED431G 2019/01This work has been funded by MINECO, AEI and FEDER of UE through the ANSWER-ASAP project (TIN2017-85160-C2-1-R); and by Xunta de Galicia through a Competitive Reference Group grant (ED431C 2020/11). CITIC, as Research Center of the Galician University System, is funded by the Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF/FEDER) with 80%, the Galicia ERDF 2014-20 Operational Programme, and the remaining 20% from the Secretaría Xeral de Universidades (Ref. ED431G 2019/01). Carlos Gómez-Rodríguez has also received funding from the European Research Council (ERC), under the European Union’s Horizon 2020 research and innovation programme (FASTPARSE, Grant No. 714150)