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In this paper, the problem of recovery of morphological information lost inabbreviated forms is addressed with a focus on highly inflected languages.Evidence is presented that the correct inflected form of an expandedabbreviation can in many cases be deduced solely from the morphosyntactic tagsof the context. The prediction model is a deep bidirectional LSTM network withtag embedding. The training and evaluation data are gathered by finding thewords which could have been abbreviated and using their correspondingmorphosyntactic tags as the labels, while the tags of the context words areused as the input features for classification. The network is trained on over10 million words from the Polish Sejm Corpus and achieves 74.2% predictionaccuracy on a smaller, but more general National Corpus of Polish. The analysisof errors suggests that performance in this task may improve if some priorknowledge about the abbreviated word is incorporated into the model.

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