Taec: a Manually annotated text dataset for trait and phenotype
  extraction and entity linking in wheat breeding literature
Claire Nédellec Clara Sauvion Robert Bossy Mariya Borovikova Louise Deléger
Wheat varieties show a large diversity of traits and phenotypes. Linking them
to genetic variability is essential for shorter and more efficient wheat
breeding programs. Newly desirable wheat variety traits include disease
resistance to reduce pesticide use, adaptation to climate change, resistance to
heat and drought stresses, or low gluten content of grains. Wheat breeding
experiments are documented by a large body of scientific literature and
observational data obtained in-field and under controlled conditions. The
cross-referencing of complementary information from the literature and
observational data is essential to the study of the genotype-phenotype
relationship and to the improvement of wheat selection. The scientific
literature on genetic marker-assisted selection describes much information
about the genotype-phenotype relationship. However, the variety of expressions
used to refer to traits and phenotype values in scientific articles is a hinder
to finding information and cross-referencing it. When trained adequately by
annotated examples, recent text mining methods perform highly in named entity
recognition and linking in the scientific domain. While several corpora contain
annotations of human and animal phenotypes, currently, no corpus is available
for training and evaluating named entity recognition and entity-linking methods
in plant phenotype literature. The Triticum aestivum trait Corpus is a new gold
standard for traits and phenotypes of wheat. It consists of 540 PubMed
references fully annotated for trait, phenotype, and species named entities
using the Wheat Trait and Phenotype Ontology and the species taxonomy of the
National Center for Biotechnology Information. A study of the performance of
tools trained on the Triticum aestivum trait Corpus shows that the corpus is
suitable for the training and evaluation of named entity recognition and
linking.