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