Abstract
Named entity recognition (NER) seeks to locate and classify named entities into predefined categories (persons, organizations, brandnames, sports teams, etc.). NER is often considered as one of the main modules designed to structure a text. We describe our system which is characterized by (1) the use of limited resources, and (2) the embedding of results from other modules such as coreference resolution and relation extraction. The system is based on the output of a dependency parser that adopts an iterative execution flow that embeds results from other modules. At each iteration, candidate categories are generated and are all considered in subsequent iterations. The main advantage of such a system is to select the best candidate only at the end of the process, taking into account all the elements provided by the different modules. Another advantage is that the system does not need a large amount of resources. The system is compared to state-of-the-art academic and industrial systems and obtains the best results.
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Notes
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cf. Lopez et al. (2016).
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This class already exists in NERD.
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From Lopez et al. (2017).
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Thanks to the “Person, to phone, Person” triple.
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These scores are: Acronym: 0.8; Completion: 0.4; Coordination: 0.6; Coreference: 0.3; Descriptor: 0.2; Left and right context: 1.0; Relation: 0.6; Relation: 0.6; Comparison: 0.9; Local expression: 1.0; Projection by memory: 0.2; Meta-rules: 1.0.
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To compare, DBpedia contains hundreds of thousands of people, places and organizations.
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Many equivalent classes have been added manually to cover the ontologies used by DBpedia.
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The goal of SRL is to determine “who does what to whom”, “when”, “where” etc.
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Lopez, C. et al. (2022). Recursive Named Entity Recognition. In: Jaziri, R., Martin, A., Rousset, MC., Boudjeloud-Assala, L., Guillet, F. (eds) Advances in Knowledge Discovery and Management. Studies in Computational Intelligence, vol 1004. Springer, Cham. https://doi.org/10.1007/978-3-030-90287-2_2
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