Data-driven Identification of Idioms in Song Lyrics

Miriam Amin, Peter Fankhauser, Marc Kupietz, Roman Schneider


Abstract
The automatic recognition of idioms poses a challenging problem for NLP applications. Whereas native speakers can intuitively handle multiword expressions whose compositional meanings are hard to trace back to individual word semantics, there is still ample scope for improvement regarding computational approaches. We assume that idiomatic constructions can be characterized by gradual intensities of semantic non-compositionality, formal fixedness, and unusual usage context, and introduce a number of measures for these characteristics, comprising count-based and predictive collocation measures together with measures of context (un)similarity. We evaluate our approach on a manually labelled gold standard, derived from a corpus of German pop lyrics. To this end, we apply a Random Forest classifier to analyze the individual contribution of features for automatically detecting idioms, and study the trade-off between recall and precision. Finally, we evaluate the classifier on an independent dataset of idioms extracted from a list of Wikipedia idioms, achieving state-of-the art accuracy.
Anthology ID:
2021.mwe-1.3
Volume:
Proceedings of the 17th Workshop on Multiword Expressions (MWE 2021)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP | MWE
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
13–22
Language:
URL:
https://aclanthology.org/2021.mwe-1.3
DOI:
10.18653/v1/2021.mwe-1.3
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.mwe-1.3.pdf