EPIE Dataset: A Corpus For Possible Idiomatic Expressions

• URL: http://arxiv.org/abs/2006.09479v1
• Date: Tue, 16 Jun 2020 19:43:30 GMT
• Title: EPIE Dataset: A Corpus For Possible Idiomatic Expressions
• Authors: Prateek Saxena and Soma Paul
• Abstract summary: We present our English Possibleatic(EPIE) corpus containing 25206 sentences labelled with lexical instances of 717 idiomatic expressions. We also present the utility of our dataset by using it to train a sequence labelling module and testing on three independent datasets with high accuracy, precision and recall scores.
• Score: 11.891511657648941
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Idiomatic expressions have always been a bottleneck for language comprehension and natural language understanding, specifically for tasks like Machine Translation(MT). MT systems predominantly produce literal translations of idiomatic expressions as they do not exhibit generic and linguistically deterministic patterns which can be exploited for comprehension of the non-compositional meaning of the expressions. These expressions occur in parallel corpora used for training, but due to the comparatively high occurrences of the constituent words of idiomatic expressions in literal context, the idiomatic meaning gets overpowered by the compositional meaning of the expression. State of the art Metaphor Detection Systems are able to detect non-compositional usage at word level but miss out on idiosyncratic phrasal idiomatic expressions. This creates a dire need for a dataset with a wider coverage and higher occurrence of commonly occurring idiomatic expressions, the spans of which can be used for Metaphor Detection. With this in mind, we present our English Possible Idiomatic Expressions(EPIE) corpus containing 25206 sentences labelled with lexical instances of 717 idiomatic expressions. These spans also cover literal usages for the given set of idiomatic expressions. We also present the utility of our dataset by using it to train a sequence labelling module and testing on three independent datasets with high accuracy, precision and recall scores.

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