RI: Small: RUI: AIR: Automatic Idiom Recognition

本课题的主要目标是开发一种独立于语言的成语自动识别方法。“塞翁失马，焉知非福”（a blessing in disguise）和“kick the bucket”（一命百岁）等习语在日常语言中大量存在，尽管它们仍然很神秘，因为人们究竟是如何学习和理解它们的尚不清楚。没有一个统一的成语定义涵盖这一类的所有成员，但成语往往在语法形式和意义上相对固定，但在形式和意义之间的关系上相对缺乏可预测性。此外，许多习惯用语可以有两种字面意思，即完全可预测的解释，比如“小女孩对她妈妈做了个鬼脸”。（习惯用语）vs。小女孩用一根胡萝卜和两个纽扣在雪人身上做了一个脸。因此，习语给各种自然语言处理应用带来了巨大的挑战，包括机器翻译系统，因为机器翻译系统通常无法检测到习语。为了解决这些挑战，提出了一种既不依赖于目标成语类型、词典或大型人工注释语料库的算法，也不受特定类型语言结构的搜索空间限制。首先，习语是违背衔接结构的语义异常值，特别是在局部上下文中。以下属性被量化并纳入离群值检测算法：1)与文字表达式或其他类型的搭配相比缺乏组合性；2)局部衔接关系的破坏，使其在语义上趋向于远离局部话题；3)虽然不是所有的语义异常值都是习语，但非组成语义异常值很可能是习语；4)习惯性不是二元属性；相反，习语属于连续统一体，从构成到部分不可分析，再到完全不可构成。这项研究有助于更好地理解习惯语言，对这类现象进行计算处理，并通过创建高质量、公开可用的语言资源来注释习惯语言，促进机器学习研究和大数据科学。其他好处包括从大型语料库中计算组合性和话题性的有效算法，关于比喻语言性质的有趣的新概括，以及在解决困难的跨学科问题的高度实际工作中训练本科生和研究生骨干。

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.