Reproducible experiments on the master thesis: An experimental survey of Named Entity Recognition methods in the biomedical domain
Semantic Textual Similarity (also known as Semantic Short-text Similarity) is a research problem that aims to calculate the similarity among text units (phrases, sentences, paragraphs or texts) focusing on the semantic content. The importance of Semantic Similarity in Natural Language Processing has...
| Autores: | , |
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| Tipo de recurso: | conjunto de datos |
| Estado: | Versión publicada |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Consorcio Madroño |
| Repositorio: | e-cienciaDatos, Repositorio de Datos del Consorcio Madroño |
| OAI Identifier: | doi:10.21950/DYAZRE |
| Acceso en línea: | https://doi.org/10.21950/DYAZRE |
| Access Level: | acceso abierto |
| Palabra clave: | Computer and Information Science Unsupervised Named Entity Recognition NER biomedical domain reproduction UB-NER |
| Sumario: | Semantic Textual Similarity (also known as Semantic Short-text Similarity) is a research problem that aims to calculate the similarity among text units (phrases, sentences, paragraphs or texts) focusing on the semantic content. The importance of Semantic Similarity in Natural Language Processing has increased in the last years due to its relevance in many tasks and applications, such as Automatic Summarization, Machine Translation, Question Answering or Semantic Indexing. UB-NER is a self-contained Java software library for benchmarking state-of-the-art STS measures in the biomedical domain. It allows to define and execute a set of experiments combining different measures and preprocessing methods. This dataset contains the reproducibility framework and dependencies, whose aim is to allow the exact replication of unsupervised named entity recognition experiment in the biomedical domain as detailed in "ReproductionProtocol.pdf" file. |
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