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Reconstructing NER Corpora: a Case Study on Bulgarian

2020-05-01 · LREC 2020 5 · Iva Marinova, Laska Laskova, Petya Osenova, Kiril Simov, Alex Popov, er

The paper reports on the usage of deep learning methods for improving a Named Entity Recognition (NER) training corpus and for predicting and annotating new types in a test corpus. We show how the annotations in a type-based corpus of named entities (NE) were populated as occurrences within it, thus ensuring density of the training information. A deep learning model was adopted for discovering inconsistencies in the initial annotation and for learning new NE types. The evaluation results get improved after data curation, randomization and deduplication.

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Deep Learningnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NER

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