Abstract -Because of privacy concerns and the expense involved in creating an annotated corpus, the existing small annotated corpora might not have sufficient number of examples for statistically learning to extract all the named-entities precisely. In this work, we evaluate what value may lie in automatically generated features based on distributional semantics when using machine-learning named entity recognition (NER). The features we generated and experimented with include n-nearest words, support vector machine (SVM)-regions, and term clustering, all of which are considered semantic (or distributional semantic) features. The addition of n-nearest words feature resulted in a greater increase in F-score than adding a manually constructed lexicon to a baseline system that extracts medical concepts from clinical notes. Although the need for relatively small annotated corpora for retraining is not obviated, lexicons empirically derived from unannotated text can not only supplement manually created lexicons, but replace them. This phenomenon is observed in extracting concepts both from biomedical literature and clinical notes. One of the most time-consuming tasks faced by a Natural Language Processing (NLP) researcher or practitioner trying to adapt a machine-learning-based NER system to a different domain is the creation, compilation, and customization of the needed lexicons. Lexical resources, such as lexicons of concept classes are considered necessary to improve the performance of NER. It is typical for medical informatics researchers to implement modularized systems that cannot be generalized (Stanfill et al. 2010) . As the work of constructing or customizing lexical resources needed for these highly specific systems is human-intensive, automatic generation is a desirable alternative. It might be possible that empirically created lexical resources might incorporate domain knowledge into a machine-learning NER engine and increase its accuracy.
REF used various medical resources in their NER system and showed an increase in an extraction performance of medical concepts.
Evaluating the Use of Empirically Constructed Lexical Resources for Named Entity Recognition
Computer Science