CharaParser for fine‐grained semantic annotation of organism morphological descriptions

Abstract

Biodiversity information organization is looking beyond the traditional document‐level metadata approach and has started to look into factual content in textual documents to support more intelligent and semantic‐based access. This article reports the development and evaluation of CharaParser, a software application for semantic annotation of morphological descriptions. CharaParser annotates semistructured morphological descriptions in such a detailed manner that all stated morphological characters of an organ are marked up in Extensible Markup Language format. Using an unsupervised machine learning algorithm and a general purpose syntactic parser as its key annotation tools, CharaParser requires minimal additional knowledge engineering work and seems to perform well across different description collections and/or taxon groups. The system has been formally evaluated on over 1,000 sentences randomly selected from Volume 19 of Flora of North American and Part H of Treatise on Invertebrate Paleontology. CharaParser reaches and exceeds 90% in sentence‐wise recall and precision, exceeding other similar systems reported in the literature. It also significantly outperforms a heuristic rule‐based system we developed earlier. Early evidence that enriching the lexicon of a syntactic parser with domain terms alone may be sufficient to adapt the parser for the biodiversity domain is also observed and may have significant implications.