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Changsheng Liu

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AAAI Conference 2019 Conference Paper

A Generalized Idiom Usage Recognition Model Based on Semantic Compatibility

Changsheng Liu
Rebecca Hwa

Many idiomatic expressions can be used figuratively or literally depending on the context. A particular challenge of automatic idiom usage recognition is that idioms, by their very nature, are idiosyncratic in their usages; therefore, most previous work on idiom usage recognition mainly adopted a “per idiom” classifier approach, i. e. , a classifier needs to be trained separately for each idiomatic expression of interest, often with the aid of annotated training examples. This paper presents a transferred learning approach for developing a generalized model to recognize whether an idiom is used figuratively or literally. Our work is based on the observation that most idioms, when taken literally, would be somehow semantically at odds with their context. Therefore, a quantified notion of semantic compatibility may help to discern the intended usage for any arbitrary idiom. We propose a novel semantic compatibility model by adapting the training of a Continuous Bag-of-Words (CBOW) model for the purpose of idiom usage recognition. There is no need to annotate idiom usage examples for training. We perform evaluative experiments on two corpora; results show that the proposed generalized model achieves competitive results compared to stateof-the-art per-idiom models.

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AAAI Conference 2017 Conference Paper

Representations of Context in Recognizing the Figurative and Literal Usages of Idioms

Changsheng Liu
Rebecca Hwa

Many idiomatic expressions can be interpreted literally or ﬁguratively, depending on the context in which they occur. Developing an appropriate computational model of the context is crucial for automatic idiom usage recognition. While many existing methods incorporate some elements of context, they have not sufﬁciently captured the interactions between the linguistic properties of idiomatic expressions and the representations of the context. In this paper we perform an in-depth exploration of the role of representations of the context for idiom usage recognition; we highlight the advantages and limitations of different representation choices in existing methods in terms of known linguistic properties of idioms; we then propose a supervised ensemble method that selects representations adaptively for different idioms. Experimental result suggests that the proposed method performs better for a wider range of idioms than previous methods.

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