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Text: now in 2D! A framework for lexical expansion with contextual similarity

机译:文字:现在为2D!具有上下文相似性的词汇扩展框架

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摘要

A new metaphor of two-dimensional text for data-driven semantic modeling of natural language is proposed, which provides an entirely new angle on the representation of text: not only syntagmatic relations are annotated in the text, but also paradigmatic relations are made explicit by generating lexical expansions. We operationalize distributional similarity in a general framework for large corpora, and describe a new method to generate similar terms in context. Our evaluation shows that distributional similarity is able to produce highquality lexical resources in an unsupervised and knowledge-free way, and that our highly scalable similarity measure yields better scores in a WordNet-based evaluation than previous measures for very large corpora. Evaluating on a lexical substitution task, we find that our contextualization method improves over a non-contextualized baseline across all parts of speech, and we show how the metaphor can be applied successfully to part-of-speech tagging. A number of ways to extend and improve the contextualization method within our framework are discussed. As opposed to comparable approaches, our framework defines a model of lexical expansions in context that can generate the expansions as opposed to ranking a given list, and thus does not require existing lexical-semantic resources.
机译:提出了一种新的二维文本隐喻,用于数据驱动的自然语言语义建模,它为文本表示提供了一个全新的角度:不仅在文本中注释了标记关系,而且通过范式也明确了范式关系生成词汇扩展。我们在大型语料库的通用框架中实现分布相似性,并描述了一种在上下文中生成相似术语的新方法。我们的评估表明,分布相似性能够以无监督且无知识的方式生成高质量的词汇资源,并且与以前的大型语料库度量相比,我们的高度可扩展的相似性度量在基于WordNet的评估中得分更高。评估词汇替换任务后,我们发现上下文化方法在整个语音所有部分的非上下文化基线上都有改进,并且我们展示了隐喻如何成功地应用于词性标记。讨论了在我们的框架内扩展和改进上下文方法的多种方法。与可比方法相反,我们的框架在上下文中定义了词法扩展模型,该模型可以生成扩展名,而不是对给定列表进行排名,因此不需要现有的词法语义资源。

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