Enhancing the Latin Morphological Analyser LEMLAT with a Medieval Latin Glossary
Authors: Flavio Cecchini, Marco Carlo Passarotti, Paolo Ruffolo, Marinella Testori, Lia Draetta, Martina Fieromonte, Annarita Liano, Costanza Marini, Giovanni Piantanida (Università Cattolica del Sacro Cuore, 2018)
Topic: Latin Resources

Abstract
We present the process of expanding the lexical basis of the Latin morphological analyser LEMLAT with the entries from the Medieval Latin glossary Du Cange. This process is performed semiautomatically by exploiting the morphological properties of lemmas, a previously available word list enhanced with inﬂectional information, and the contents of the lexical entries of Du Cange.

Introduction
Introduction Introduction Latin raises particular challenges for Natural Language Processing (NLP). Given that accuracy rates of stochastic NLP tools heavily depend on the training set on which their models are built, this becomes a particularly problematic issue when Latin is concerned, because Latin texts show an enormous linguistic variety resulting from (a) a wide time span (covering more than two millennia), (b) a large set of genres (ranging from literary to philosophical, historical and documentary texts) and (c) a big diatopic diversity (spread all over Europe and beyond). Such complexity impacts NLP to the point that building NLP tools claiming to be suitable for all Latin varieties is an unrealistic task. One practical example comes from an experiment described by Ponti and Passarotti (2016), who show that the performance of a dependency parser trained on Medieval Latin data drops dramatically when the same trained model is applied to texts from the Classical era. This issue affects all layers of linguistic annotation, including fundamental ones, like lemmatisation and morphological analysis. Today, a handful of morphological analysers are available for Latin, chieﬂy Words,1 LEMLAT 3.0,2 Morpheus3 –reimplemented in 2013 as Parsley4–, the PROIEL Latin morphology system5 and LatMor.6 Although LEMLAT, together with LatMor,7 has proved to be the best performing morphological analyser for Latin and the one boasting the largest lexical basis, its lexical coverage is still limited to Classical and Late Latin only. First released as a morphological lemmatiser at the end of the 1980s at ILC-CNR in Pisa (Bozzi and Cappelli, 1990; Marinone, 1990, v 1.0), where it was enhanced with morphological features between 2002 and 2005 (Passarotti, 2004, v 2.0), LEMLAT relies on a lexical basis resulting from the collation of three Latin dictionaries (Georges and Georges, 1913 1918; Glare, 1982; Gradenwitz, 1904) for a total of 40 014 lexical entries and 43 432 lemmas, as more than one lemma can be included in one lexical entry. This lexical basis was further enlarged in version 3.0 of LEMLAT by semiautomatically adding most of the Onomasticon (26 415 lemmas out of 28 178) provided by the 5th edition of the Forcellini dictionary (Budassi and Passarotti, 2016). In order to equip LEMLAT to process Latin texts beyond the Classical period, we recently enhanced its lexical basis with the lexical entries from a large reference glossary for Medieval Latin, namely the Glossarium Mediae et Inﬁmae Latinitatis by Du Cange et alii (1883 1887, hereafter DC). This paper details the process performed to include DC in LEMLAT’s lexical basis.

Conclusion
In this paper we present the rule-based process performed to semi-automatically enhance the Latin morphological analyser LEMLAT with the Du Cange glossary. While dated, such an approach is still necessary if the intent is to minimise the error rate resulting from the automatic PoStagging of the glossary’s deﬁnitions and quotations. Indeed, unless tuned on an in-domain training set, existing stochastic PoS-taggers for Latin are not yet reliable enough when it comes to processing the complex, raw and “freestyle” deﬁnitions of DC. The ever-growing availability of digitised Latin texts from various eras urges us to build NLP tools capable of automatically analysing such varied sets of linguistic data. In this respect, enhancing the lexical basis of LEMLAT with a Medieval Latin dictionary is a ﬁrst step towards the development of well-performing tools on diachronic data. Conversely, even if building a tool suitable for different diachronic varieties of Latin were feasible for low-level annotation tasks (like e. g. lemmatisation and morphological analysis), this does not seem to be the case for tasks such as syntactic parsing or word sense disambiguation, for which either highly ﬂexible or highly specialised tools will be needed. This is an open issue not only for Latin. Indeed, the portability of NLP tools across domains and genres is currently one of the main challenges in NLP. Thanks to its highly diverse corpus, Latin is a perfect case-study language to tackle these problems. For the future, we plan to expand LEMLAT’s lexical database with all of the graphical variants reported in DC and possibly also with other Medieval Latin thesauri, such as the Dictionary of Medieval Latin from British Sources (Ashdown et al., 2018), so as to improve both its diatopic and diachronic coverage. In general, we aspire to make LEMLAT’s algorithm better able to cope with the most widespread and predictable orthographic variations recorded in Medieval manuscripts and texts.17 17An introduction and an approach to this issue can be found in Kestemont and De Gussem (2017).