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Using Verb-Noun Patterns to Detect Process Inputs

机译:使用动词名词模式来检测过程输入

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We present the preliminary results of an ongoing work aimed at using morpho-syntactic patterns to extract information from process descriptions in a semi-supervised manner. The experiments have been designed for generic information extraction tasks and evaluated on detecting ingredients from cooking recipes in French using a large gold standard corpus. The proposed method uses bi-lexical dependency oriented syntactic analysis of the text and extracts relevant morpho-syntactic patterns. Those patterns are then used as features for different machine learning methods to acquire the final ingredient list. Furthermore, this approach may easily be adapted to similar tasks since it relies on mining generic morpho-syntactic patterns from the documents automatically. The method itself is language independent, considering language specific parsers being used. The performance of our method on the DEFT 2013 data set is nevertheless satisfactory since it significantly outperforms the best system from the original challenge (0.75 vs 0.66 MAP).
机译:我们提出了持续的工作初步结果,旨在使用Morpho-Syactic模式以半监督方式从过程描述中提取信息。专为通用信息提取任务设计了实验,并在使用大型金标准语料库中检测法式烹饪食谱的原料。所提出的方法使用文本的双词汇依赖性依赖性句法分析,提取相关的句法模式。然后将这些模式用作不同机器学习方法的特征,以获取最终成分列表。此外,这种方法可以很容易地适应类似的任务,因为它依赖于自动从文档中挖掘通用的常规语法模式。考虑使用语言特定解析器,该方法本身是独立的语言。我们在DEFT 2013数据集上的性能令人满意,因为它显着优于原始挑战的最佳系统(0.75 VS 0.66地图)。

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