Expanding Abbreviations in a Strongly Inflected Language: Are Morphosyntactic Tags Sufficient?
This addresses the challenge of natural language processing for inflected languages like Polish, but it is incremental as it builds on existing tag-based methods.
The paper tackles the problem of recovering morphological information lost in abbreviated forms in highly inflected languages, showing that morphosyntactic tags can predict correct inflected expansions with 74.2% accuracy on a Polish corpus.
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.