Expanding Abbreviations in a Strongly Inflected Language: Are Morphosyntactic Tags Sufficient?

TL;DR

This paper investigates whether morphosyntactic tags alone are sufficient to recover morphological information in abbreviated forms of highly inflected languages, using a deep learning model trained on Polish corpora.

Contribution

It demonstrates that a deep bidirectional LSTM can predict inflected forms from context tags with substantial accuracy, highlighting the potential of morphosyntactic tags for abbreviation expansion.

Findings

Achieved 74.2% accuracy on Polish corpus

Model trained on over 10 million words

Error analysis suggests incorporating prior knowledge could improve performance

Abstract

In this paper, the problem of recovery of morphological information lost in abbreviated forms is addressed with a focus on highly inflected languages. Evidence is presented that the correct inflected form of an expanded abbreviation can in many cases be deduced solely from the morphosyntactic tags of the context. The prediction model is a deep bidirectional LSTM network with tag embedding. The training and evaluation data are gathered by finding the words which could have been abbreviated and using their corresponding morphosyntactic tags as the labels, while the tags of the context words are used as the input features for classification. The network is trained on over 10 million words from the Polish Sejm Corpus and achieves 74.2% prediction accuracy on a smaller, but more general National Corpus of Polish. The analysis of errors suggests that performance in this task may improve if some prior knowledge about the abbreviated word is incorporated into the model.