Named entity recognition (NER) is the task of tagging entities in text with their corresponding type. Approaches typically use BIO notation, which differentiates the beginning (B) and the inside (I) of entities. O is used for non-entity tokens.
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We propose a method for auditing the in-domain robustness of systems, focusing specifically on differences in performance due to the national origin of entities.
Recent attempts to ingest external knowledge into neural models for named-entity recognition (NER) have exhibited mixed results.
Domain adaptation of Pretrained Language Models (PTLMs) is typically achieved by pretraining on in-domain text.
Recognizing non-standard entity types and relations, such as B2B products, product classes and their producers, in news and forum texts is important in application areas such as supply chain monitoring and market research.
Monitoring mobility- and industry-relevant events is important in areas such as personal travel planning and supply chain management, but extracting events pertaining to specific companies, transit routes and locations from heterogeneous, high-volume text streams remains a significant challenge.
For the sentence classification task, our model achieves the macro F1 score of 68. 82% gaining 7. 47% over the score of BERT model trained on Russian data.
In recent years, the amount of Cyber Security data generated in the form of unstructured texts, for example, social media resources, blogs, articles, and so on has exceptionally increased.
We present a new corpus comprising annotations of medical entities in case reports, originating from PubMed Central's open access library.
We created this CORD-19-NER dataset with comprehensive named entity recognition (NER) on the COVID-19 Open Research Dataset Challenge (CORD-19) corpus (2020- 03-13).
They are therefore sensitive to window size selection and are unable to incorporate the context of the entire document.