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 min- ing. In this paper we demonstrate the impor- tance 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 specu- lative 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 pos- sible ways of tackling speculative language in biomedical texts.

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