Automated Annotation of Scientific Texts for ML-based Keyphrase
  Extraction and Validation
Oluwamayowa O. Amusat Harshad Hegde Christopher J. Mungall Anna Giannakou Neil P. Byers Dan Gunter Kjiersten Fagnan Lavanya Ramakrishnan
Advanced omics technologies and facilities generate a wealth of valuable data
daily; however, the data often lacks the essential metadata required for
researchers to find and search them effectively. The lack of metadata poses a
significant challenge in the utilization of these datasets. Machine
learning-based metadata extraction techniques have emerged as a potentially
viable approach to automatically annotating scientific datasets with the
metadata necessary for enabling effective search. Text labeling, usually
performed manually, plays a crucial role in validating machine-extracted
metadata. However, manual labeling is time-consuming; thus, there is an need to
develop automated text labeling techniques in order to accelerate the process
of scientific innovation. This need is particularly urgent in fields such as
environmental genomics and microbiome science, which have historically received
less attention in terms of metadata curation and creation of gold-standard text
mining datasets.
  In this paper, we present two novel automated text labeling approaches for
the validation of ML-generated metadata for unlabeled texts, with specific
applications in environmental genomics. Our techniques show the potential of
two new ways to leverage existing information about the unlabeled texts and the
scientific domain. The first technique exploits relationships between different
types of data sources related to the same research study, such as publications
and proposals. The second technique takes advantage of domain-specific
controlled vocabularies or ontologies. In this paper, we detail applying these
approaches for ML-generated metadata validation. Our results show that the
proposed label assignment approaches can generate both generic and
highly-specific text labels for the unlabeled texts, with up to 44% of the
labels matching with those suggested by a ML keyword extraction algorithm.