RI: Small: RUI: AIR: Automatic Idiom Recognition

本研究的主要目标是开发一种与语言无关的成语自动识别方法。习惯表达，如“塞翁失马焉知非福”和“kick the bucket”在日常语言中大量存在，尽管它们仍然很神秘，因为人们不清楚人们是如何学习和理解它们的。成语并没有一个统一的定义可以涵盖所有的成语，但是成语的语法形式和意义相对固定，而形式和意义之间的关系则相对没有什么可预测性。此外，许多习惯表达可以出现两个字面意思，即完全可预测的，解释给他们的形式-比较'小女孩做了一个鬼脸在她的母亲。小女孩用胡萝卜和两颗纽扣在雪人上做了个鬼脸。因此，习语对各种自然语言处理应用程序提出了巨大的挑战，包括机器翻译系统，它们通常无法检测习语。为了解决这些挑战，提出了一种算法，既不依赖于目标成语类型，词典，或大型手动注释语料库，也不限制搜索空间的特定类型的语言结构。本文的出发点是习语是违反衔接结构的语义离群点，尤其是在局部语境中。以下属性被量化并被纳入离群点检测算法中：1）与字面表达或其他类型的搭配相比缺乏组合性; 2）违反局部内聚关系，使得它们倾向于在语义上远离局部主题; 3）虽然不是所有语义离群点都是习语，但非组合语义离群点可能是习语;（4）习语性不是一个二元属性;本研究有助于更好地理解习语语言，有助于对习语现象的计算处理，并有助于创建高质量的、公开可用的习语注释语言资源，促进机器学习研究和大数据科学。额外的好处包括有效的算法计算组合性和话题性从大型语料库，有趣的新概括的性质比喻语言，和培训干部的本科生和研究生在高度实用的工作上一个困难的跨学科问题。

The main goal of this research project is to develop a language independent method for automatic idiom recognition. Idiomatic expressions, such as 'a blessing in disguise' and 'kick the bucket' are plentiful in everyday language, though they remain mysterious, as it is not clear exactly how people learn and understand them. There is no single agreed-upon definition of idiom that covers all members of this class, but idioms tend to be relatively fixed in grammatical form and meaning, but with relatively little predictability in the relation between form and meaning. Also, many idiomatic expressions can appear with both literal, i.e. fully predictable, interpretations given their form -- compare 'The little girl made a face at her mother.' (idiomatic) vs. 'The little girl made a face on the snowman using a carrot and two buttons.' (literal) As a result, idioms present great challenges for a variety of natural language processing applications, including machine translation systems, which often do not detect idiomatic language. To address these challenges, an algorithm is proposed that neither relies on target idiom types, lexicons, or large manually annotated corpora, nor limits the search space by a particular type of linguistic construction. The starting point is that idioms are semantic outliers that violate cohesive structure, especially in local contexts. The following properties are quantified and are incorporated into the outlier detection algorithm: 1) lack of compositionality comparing to literal expressions or other types of collocations; 2) violation of local cohesive ties, so that they tend to be semantically distant from the local topics; 3) while not all semantic outliers are idioms, non-compositional semantic outliers are likely to be idiomatic; 4) idiomaticity is not a binary property; rather, idioms fall on the continuum from being compositional to being partly unanalyzable to completely non-compositional.This research contributes to the better understanding of idiomatic language, to the computational treatment of such phenomena and, with the creation of high quality, publicly available linguistic resources annotated for idioms, to the facilitation of machine learning research and big data science. Additional benefits include efficient algorithms for computing compositionality and topicality from large corpora, interesting new generalizations about the nature of figurative language, and the training of a cadre of undergraduate and graduate students in highly practical work on a difficult interdisciplinary problem.