Animal Disease Event Recognition and Classication
Svitlana Volkova, Doina Caragea, William H. Hsu, Swathi Bujuru · 2010
Monitoring epidemic crises, caused by rapid spread of infec- tious animal diseases, can be facilitated by the plethora of information about disease-related events that is available online. Therefore, the abil- ity to use this information to perform domain-specic entity recognition and event-related sentence classication, which in turn can support time and space visualization of automatically extracted events, is highly desir- able. Towards this goal, we present a rule-based approach to the problem of extracting animal disease-related events from web documents. Our ap- proach relies on the recognition of structured entity tuples, consisting of attributes, which describe events related to animal diseases. The event attributes that we consider include animal diseases, dates, species and geo-referenced locations. We perform disease names and species recog- nition using an automatically-constructed ontology, dates are extracted using regular expressions, while location are extracted using a condi- tional random elds tool. The extracted events are further classied as conrmed or suspected based on semantic features, obtained from the e.g., GoogleSets 1 and WordNet 2 . Our preliminary results demonstrate the feasibility of the proposed approach.