Named-Entity Recognizers (NERs) are an important part of information extraction systems in annotation tasks. Although substantial progress has been made in recognizing domain-independent named entities (e.g. location, organization and person), there is a need to recognize named entities for domain-specific applications in order to extract relevant concepts. Due to the growing need for smart health applications in order to address some of the latest worldwide epidemics of behavioral issues (e.g. over eating, lack of exercise, alcohol and drug consumption), we focused on the domain of behavior change, especially lifestyle change. To the best of our knowledge, there is no named-entity recognizer designed for the lifestyle change domain to enable applications to recognize relevant concepts. We describe the design of an ontology for behavioral health based on which we developed a NER augmented with lexical resources. Our NER automatically tags words and phrases in sentences with relevant (lifestyle) domain-specific tags (e.g. [un/]healthy food, potentially-risky/healthy activity, drug, tobacco and alcoholic beverage). We discuss the evaluation that we conducted with with manually collected test data. In addition, we discuss how our ontology enables systems to make further information acquisition for the recognized named entities by using semantic reasoners.
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