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The value of parsing as feature generation for gene mention recognition.

机译:解析作为特征生成对于基因提及识别的价值。

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We measured the extent to which information surrounding a base noun phrase reflects the presence of a gene name, and evaluated seven different parsers in their ability to provide information for that purpose. Using the GENETAG corpus as a gold standard, we performed machine learning to recognize from its context when a base noun phrase contained a gene name. Starting with the best lexical features, we assessed the gain of adding dependency or dependency-like relations from a full sentence parse. Features derived from parsers improved performance in this partial gene mention recognition task by a small but statistically significant amount. There were virtually no differences between parsers in these experiments.
机译:我们测量了基本名词短语周围的信息反映出基因名称的存在的程度,并评估了七个不同的解析器为此目的提供信息的能力。使用GENETAG语料库作为黄金标准,当基本名词短语包含基因名称时,我们进行了机器学习以从其上下文中识别。从最佳的词汇功能开始,我们评估了从完整的句子解析中添加依赖项或类似依赖关系的收益。解析器派生的功能将这个部分基因提及识别任务的性能提高了少量,但在统计上意义重大。在这些实验中,解析器之间实际上没有差异。

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