This work proposes a tree-based convolutional neural community model which exploit various long-distance relationships between phrases, which improves the sequential baselines on all three sentiment and query classification tasks, and achieves the highest printed accuracy on TREC. We have explored classification of sentences in abstracts with medical tags. Unlike earlier work, we identify each irrelevant sentences and the semantic tags of relevant sentences using supervised methods. We evaluated the efficiency of a selection of feature configurations over different units of knowledge, together with an exterior corpus. The results for the indirect strategy are given in Table 7, together with the benchmark system.
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The features consist of word features , positional information, section headings, and windowed options . The difference from the experiments described in is that we do not perform the time period normalisation step, and we applied a essay writing service plagiarism free unique POS tagger. We constructed a corpus of 1,000 medical abstracts annotated by hand with specified medical categories (e.g. Intervention, Outcome). We explored using numerous features based on lexical, semantic, structural, and sequential information in the knowledge, utilizing Conditional Random Fields for classification.
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