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J Cheminform. 2015 Jan 19;7(Suppl 1 Text mining for chemistry and the CHEMDNER track):S1. doi: 10.1186/1758-2946-7-S1-S1. eCollection 2015.

CHEMDNER: The drugs and chemical names extraction challenge.

Author information

1
Structural Computational Biology Group, Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre, Calle Melchor Fernndez Almagro, 3, Madrid, Spain.
2
Computational Intelligence Group, Department of Artificial Intelligence, Universidad Politecnica de Madrid, Calle Ramiro de Maeztu, 7, Madrid, Spain.
3
Small Molecule Discovery Platform, Center for Applied Medical Research (CIMA), University of Navarra, Avenida de Pio XII, 55, Pamplona, Spain.

Abstract

Natural language processing (NLP) and text mining technologies for the chemical domain (ChemNLP or chemical text mining) are key to improve the access and integration of information from unstructured data such as patents or the scientific literature. Therefore, the BioCreative organizers posed the CHEMDNER (chemical compound and drug name recognition) community challenge, which promoted the development of novel, competitive and accessible chemical text mining systems. This task allowed a comparative assessment of the performance of various methodologies using a carefully prepared collection of manually labeled text prepared by specially trained chemists as Gold Standard data. We evaluated two important aspects: one covered the indexing of documents with chemicals (chemical document indexing - CDI task), and the other was concerned with finding the exact mentions of chemicals in text (chemical entity mention recognition - CEM task). 27 teams (23 academic and 4 commercial, a total of 87 researchers) returned results for the CHEMDNER tasks: 26 teams for CEM and 23 for the CDI task. Top scoring teams obtained an F-score of 87.39% for the CEM task and 88.20% for the CDI task, a very promising result when compared to the agreement between human annotators (91%). The strategies used to detect chemicals included machine learning methods (e.g. conditional random fields) using a variety of features, chemistry and drug lexica, and domain-specific rules. We expect that the tools and resources resulting from this effort will have an impact in future developments of chemical text mining applications and will form the basis to find related chemical information for the detected entities, such as toxicological or pharmacogenomic properties.

KEYWORDS:

BioCreative; ChemNLP; chemical entity recognition; chemical indexing; machine learning; named entity recognition; text mining

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