Since facts or statements in a hedge or negated context typically appear as false positives, the proper handling of these language phenomena is of great importance in biomedical text mining. In this paper we demonstrate the importance of hedge classification experimentally in two real life scenarios, namely the ICD-9-CM coding of radiology reports and gene name Entity Extraction from scientific texts. We analysed the major differences of speculative language in these tasks and developed a maxent-based solution for both the free text and scientific text processing tasks. Based on our results, we draw conclusions on the possible ways of tackling speculative language in biomedical texts.
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