TY - JOUR
AU - Sayle, Roger
AB - Background: Chemical entity recognition has traditionally been performed by machine learning approaches. Here we describe an approach using grammars and dictionaries. This approach has the advantage that the entities found can be directly related to a given grammar or dictionary, which allows the type of an entity to be known and, if an entity is misannotated, indicates which resource should be corrected. As recognition is driven by what is expected, if spelling errors occur, they can be corrected. Correcting such errors is highly useful when attempting to lookup an entity in a database or, in the case of chemical names, converting them to structures. Results: Our system uses a mixture of expertly curated grammars and dictionaries, as well as dictionaries automatically derived from public resources. We show that the heuristics developed to filter our dictionary of trivial chemical names (from PubChem) yields a better performing dictionary than the previously published Jochem dictionary. Our final system performs post-processing steps to modify the boundaries of entities and to detect abbreviations. These steps are shown to significantly improve performance (2.6% and 4.0% F -score respectively). Our complete system, with incremental post-BioCreative workshop improvements, achieves 89.9% precision and 85.4% recall (87.6% F -score) 
TI - LeadMine: a grammar and dictionary driven approach to entity recognition
JF - Journal of Cheminformatics
DO - 10.1186/1758-2946-7-S1-S5
DA - 2015-01-19
UR - https://www.deepdyve.com/lp/springer-journals/leadmine-a-grammar-and-dictionary-driven-approach-to-entity-7hzHK63q5I
SP - 1
EP - 9
VL - 7
IS - 1
DP - DeepDyve
ER -