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Meanwhile, the Paragraph Vector model proves less effective (F1 = 0.368), even after ext","title":"Detecting Speculative Language in Biomedical Texts using Recurrent Neural Tensor Networks","url":"https://arxiv.org/abs/2606.10471","vendor":"arxiv_cs_ai"},"summary":"arXiv:2606.10471v1 Announce Type: cross \nAbstract: In this investigation, we delve into the automated detection of speculative language within biomedical articles by utilizing distributed sentence representations and advanced deep learning techniques. The implications of such identification extend to information retrieval, multi-document summarization, and the exploration of new knowledge. Our exploration encompasses two distinct approaches for acquiring distributed sentence representations: the Paragraph Vector model and the Recursive Neural Tensor Network. These methodologies are then rigorously compared against three foundational baseline algorithms: Support Vector Machines, Naive Bayes, and pattern matching. 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