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Abstract
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| Named Entity Recognition (NER) poses new challenges in real-world documents in which there are entities with different roles according to their purpose or meaning. Retrieving all the possible entities in scenarios in which only a subset of them based on their role is needed, produces noise on the overall precision. This work proposes a NER model that relies on role classification models that support recognizing entities with a specific role. The proposed model has been implemented in two use cases using Spanish drug Summary of Product Characteristics: identification of therapeutic indications and identification of adverse reactions. The results show how precision is increased using a NER model that is oriented towards a specific role and discards entities out of scope. | |
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International
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Si |
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Congress
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Recent Advances in Natural Language Processing, RANLP 2017 |
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960 |
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Place
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Varna, Bulgaria |
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Reviewers
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Si |
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ISBN/ISSN
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1313-8502 |
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10.26615/978-954-452-049-6_021 |
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Start Date
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04/09/2017 |
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End Date
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06/09/2017 |
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From page
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149 |
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To page
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156 |
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Role-based model for Named Entity Recognition |