Cross-Corpus Textual Entailement for Sublanguage Analysis in Epidemic Intelligence

Textual entailment has been recognized as a generic task that captures major semantic inference needs across many natural language processing applications. To date, textual entailment has not been considered in a cross-corpus setting, nor for user generated content. The emergence of Medicine 2.0, has made medical blogs an increasingly accepted source of information; but given the characteristics of blogs (which tend to be noisy and informal; or contain a interspersing of subjective and factual sentences) a potentially large amount of irrelevant information may be present. Considering this potential noise, the overarching problem with respect to information extraction from social media for medical intelligence gathering, is achieving the correct level of sentence filtering - as opposed to document or blog post level. In this paper, we propose an approach to textual entailment which uses the text from one source of user generated content (T text) for sentence-level filtering within a new and less amenable one (H text), when the underlying domain, tasks or semantic information is the same, or overlaps.