(Stem and Word) Predictability in Italian Verb Paradigms: An Entropy-Based Study Exploiting the New Resource LeFFI
Authors: Matteo Pellegrini, Alessandra Teresa Cignarella (Liceo Statale ``Augusto Monti'' di Chieri, Università di Torino, University of Valencia, 2020)
Topic: Lexical and Semantic Resources and Analysis

Abstract
In this paper we present LeFFI, an inflected lexicon of Italian listing all the available wordforms of 2,053 verbs. We then use this resource to perform an entropy-based analysis of the mutual predictability of wordforms within Italian verb paradigms, and compare our findings to the ones of previous work on stem predictability in Italian verb inflection.

Introduction
The pioneering work of AUTHOR has inspired an influential line of research where predictability within inflectional paradigms is modelled by resorting to the notion of morphomic stems – i.e., stems that cannot be considered as bearing any meaning, as they appear in groups of cells that do not share a fixed morphosyntactic content. In this perspective, every lexeme is seen as equipped with a set of indexed stems, that only for regular lexemes are mutually predictable, while for irregular verbs they need to be independently stored. From each of these stems, a fixed set of wordforms can be obtained by adding the appropriate inflectional endings. An analysis relying on these assumptions was proposed by AUTHOR and subsequent work -- see AUTHOR for a recent survey -- to account for the patterns of stem allomorphy that are found in the verbal inflection of Romance languages in general. More detailed implementations of these ideas have then been provided for individual languages, among them Italian AUTHOR. Another possibility that has been explored in more recent times is tackling the issue of inflectional predictability in terms of predictions of wordforms from one another, without assuming a given segmentation in stems vs. endings, in a fully word-based, abstractive AUTHOR approach. %Within this framework, Stump and Finkel (2013) focus on Principal Parts – i.e., sets of inflected wordforms that allow to infer all the remaining inflected wordforms of a lexeme. On the other hand, Within this framework, AUTHOR propose to estimate the reliability of inflectional predictions by means of the information-theoretic notion of conditional entropy. Building on this work, AUTHOR outline a procedure that allows to compute entropy values estimating the uncertainty in predicting one cell from another one directly from a lexicon of fully inflected wordforms in phonological transcription, using the type frequency of different inflectional patterns to estimate their probability of application. This method has been applied to French by AUTHOR, to Latin by AUTHOR, and it has been used for typological comparison on a small sample of languages by AUTHOR, who also provides a freely available toolkit (Qumin) allowing to perform this computation automatically for any language. A similar entropy-based analysis has not been proposed for Italian yet. To be able to use the Qumin toolkit to perform it, it is necessary to have an inflected lexicon listing all the wordforms of a representative number of lexemes in phonological transcription, like e.g. Flexique for French AUTHOR or LatInflexi for Latin AUTHOR. Looking for such a resource for Italian, we can see that in most lexicons wordforms are given in orthographic transcription – see e.g. Morph-it! AUTHOR and CoLFIS AUTHOR. On the other hand, in PhonItalia AUTHOR there are phonological transcriptions, but not all the inflected wordforms of each lexeme are listed. To the best of our knowledge, the only resource providing phonological transcriptions of the full paradigm of lexemes is GLAFF-IT AUTHOR, but due to the way in which it was created, it proves to be too noisy to be used for entropy computations as such. In this paper, we describe the work that was done to obtain a smaller, but cleaner version of GLAFF-IT. We then use this resource to perform an entropy-based analysis of predictability in Italian verb inflection. After briefly describing the methodology, we present our results comparing them with the findings of previous stem-based analyses.

Conclusion
In this paper, we have presented the inflected lexicon of Italian verbs LeFFI. We have then exploited it to investigate predictability in Italian verb inflection, using implicative entropy to estimate the uncertainty in predicting wordforms from one another. The results have been used to obtain a mapping of the paradigm in zones of interpredictability, that we have compared to the mapping of stems proposed in previous work, showing that our word-based procedure is capable of capturing aspects that are downplayed, if not ignored in the stem-based approach. Besides their theoretical interest, both the resource and the information-theoretic approach potentially have more practical applications, for instance in the field of psycholinguistics. The resource provides a very clean but sufficiently large dataset of forms that can be used as a source of input for fine-grained experiments. In such experiments, it would be possible to test if the different levels of predictability between cells identified by different values of implicative entropy find a correspondence in the process of acquisition of inflectional morphology by L1 and L2 speakers – i.e., if the pairs of cells between which there are higher implicative entropy values are indeed the ones on which learners are more uncertain. More generally, our entropy-based evaluation of uncertainty in inflectional predictions can be considered as a measure of (at least one aspect of) morphological complexity, that can be used also in other areas, for instance to asses text readibility.