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We introduce a novel machine learning framework based on recursive autoencoders for sentence-level prediction of sentiment label distributions. Our method learns vector space representations for multi-word phrases. In sentiment prediction tasks these representations outperform other state-of-the-art approaches on commonly used datasets, such as movie(More)
Paraphrase detection is the task of examining two sentences and determining whether they have the same meaning. In order to obtain high accuracy on this task, thorough syntactic and semantic analysis of the two statements is needed. We introduce a method for paraphrase detection based on recursive autoencoders (RAE). Our unsupervised RAEs are based on a(More)
Unsupervised word representations are very useful in NLP tasks both as inputs to learning algorithms and as extra word features in NLP systems. However, most of these models are built with only local context and one representation per word. This is problematic because words are often polysemous and global context can also provide useful information for(More)
Blocking the immunoinhibitory PD-1:PD-L1 pathway using monoclonal antibodies has led to dramatic clinical responses by reversing tumor immune evasion and provoking robust and durable antitumor responses. Anti-PD-1 antibodies have now been approved for the treatment of melanoma, and are being clinically tested in a number of other tumor types as both a(More)
CD200R1 expressed on the surface of myeloid and lymphoid cells delivers immune inhibitory signals to modulate inflammation when engaged with its ligand CD200. Signalling through CD200/CD200R1 has been implicated in a number of immune-related diseases including allergy, infection, cancer and transplantation, as well as several autoimmune disorders including(More)
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