Open AccessAdapting semantic natural language processing technology to address information overload in influenza epidemic management
Author(s) -
Keselman Alla,
Rosemblat Graciela,
Kilicoglu Halil,
Fiszman Marcelo,
Jin Honglan,
Shin Dongwook,
Rindflesch Thomas C.
Publication year - 2010
Publication title -
journal of the american society for information science and technology
Language(s) - English
Resource type - Journals
eISSN - 1532-2890
pISSN - 1532-2882
DOI - 10.1002/asi.21414
Subject(s) - automatic summarization , computer science , information overload , terminology , ontology , semantics (computer science) , information retrieval , semantic analysis (machine learning) , natural language processing , data science , artificial intelligence , world wide web , linguistics , philosophy , epistemology , programming language
Abstract The explosion of disaster health information results in information overload among response professionals. The objective of this project was to determine the feasibility of applying semantic natural language processing (NLP) technology to addressing this overload. The project characterizes concepts and relationships commonly used in disaster health‐related documents on influenza pandemics, as the basis for adapting an existing semantic summarizer to the domain. Methods include human review and semantic NLP analysis of a set of relevant documents. This is followed by a pilot test in which two information specialists use the adapted application for a realistic information‐seeking task. According to the results, the ontology of influenza epidemics management can be described via a manageable number of semantic relationships that involve concepts from a limited number of semantic types. Test users demonstrate several ways to engage with the application to obtain useful information. This suggests that existing semantic NLP algorithms can be adapted to support information summarization and visualization in influenza epidemics and other disaster health areas. However, additional research is needed in the areas of terminology development (as many relevant relationships and terms are not part of existing standardized vocabularies), NLP, and user interface design.