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Combining Dependency Parsing and a Lexical Network Based on Lexical Functions for the Identification of Collocations

机译:结合依赖分析和基于词法功能的词法网络以识别搭配

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A collocation is a type of multiword expression formed by two parts: a base and a collocate. Usually, in a collocation, the base has a denotative or literal meaning, while the collocate has a connotative meaning. Examples of collocations: pay attention, easy as pie, strongly condemn, lend support, etc. The Meaning-Text Theory created the lexical functions to, among other objectives, represent the meaning existing between the base and the collocate or to represent the relation between the base and a support verb. For example, the lexical function Magn represents the meaning intensification, while the lexical function Caus, applied to a base, returns the support verb that represents the causality of the action expressed in the collocation. In a dependency parsing, each word (dependent) is directly associated with its governor in a phrase. In this paper, we show how we combine dependency parsing to extract collocation candidates and a lexical network based on lexical functions to identify the true collocations from the candidates. The candidates are extracted from a French corpus according to 14 dependency relations. The collocations identified are classified according to the semantic group of the lexical functions modeling them. We obtained a general precision (for all dependency types) of 76.3%, with a precision higher than 95% for collocations having certain dependency relations. We also found that about 86% of collocations identified belong to only four semantic categories: qualification, support verb, location and action/ event.
机译:并置是一种由两部分组成的多词表达式:基本和并置。通常,在搭配中,基数具有指示性或字面意义,而搭配则具有含义。搭配的示例:注意,容易表达,强烈谴责,提供支持等。意义文本理论创建了词汇功能,以表示(除其他目标外)基础和搭配之间存在的含义或表示之间的关系。基础和辅助动词。例如,词汇函数Magn表示含义强化,而应用于基数的词汇函数Caus返回支持动词,该动词表示并置表达的动作的因果关系。在依存关系分析中,每个词(依存关系)都与短语中的调控器直接关联。在本文中,我们展示了如何结合依赖项解析来提取搭配候选者和基于词汇功能的词汇网络,以从候选者中识别出真正的搭配。根据14种依存关系从法国语料库中提取候选人。识别的搭配根据对它们建模的词汇功能的语义组进行分类。对于所有依赖项类型,我们获得的一般精度为76.3%,对于具有某些依赖关系的并置,其精度高于95%。我们还发现,大约86%的并置仅属于四个语义类别:限定,支持动词,位置和动作/事件。

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