Suoidne-varra-bleahkka-mála-bihkka-senet-dielku 'hay-blood-ink-paint-tar-mustard-stain' -Should compounds be lexicalized in NLP?
Authors: Linda Wiechetek, Chiara Argese, Tommi A Pirinen, Trond Trosterud, Divvun Giellatekno (UiT Norgga árktalaš universitehta, 2020)
Topic: Latin Resources

Abstract
Lexicalizing compounds, in addition to treating them dynamically, is a key element in giving us idiomatic translations and detecting compound errors. We present and evaluate an e-dictionary (NDS) and a grammar checker (GramDivvun) for North Sámi. We achieve a coverage of 98% for NDS-queries and of 96% for compound error detection in GramDivvun.

Introduction
In this paper, we discuss the use and necessity of the lexicalization of compounds – in addition to the dynamic approach to compounding – in two rule-based Natural Language Processing (NLP) applications, a grammar checker GramDivvun and an electronic dictionary NDS (short for Neahttadigisánit). We argue for a dual approach and support this view with an evaluation of these tools. For comparison, we also look at a third application, a corpus tool (Korp) for the North Sámi corpus SIKOR. SIKOR, the Sámi International KORpus, is the collection of texts in different Sámi languages compiled by UiT The Arctic University of Norway and the Norwegian Sámi Parliament. In the past, we have mostly focussed on the dynamic approach to morphological analysis. This means that we have a lexicon with lemmata and stems, which in a finite-state manner are combined with inflectional and derivational affixes and other stems and modified when morpho-phonological processes apply. In this way the linguistic processes inflection, derivation and compounding are modelled in a dynamic way, i.e. by means of concatenation and composition as opposed to listing of all forms. Lexicalization, i.e. listing compounds or inflected word forms as such, is the alternative approach to the dynamic one. In addition to these two approaches we also use guessers for certain tasks, i.e. proper name guessing in morphosyntactic parsing. Our approach is entirely rulebased and open source. Within our 20 year experience with language tools for the Sámi languages and other languages with complex morphology, we have achieved good results and produced reliable tools. There are a number of approaches to error detection of a few errortypes for morphologically complex - although less complex than North Sámi languages like Latvian (Deksne, 2019) and Russian (Rozovskaya and Roth, 2019). The Latvian neural network grammar checker focusses on preposition-postposition confusion, adjectivenoun agreement, mood errors in verb forms, number and case in noun forms, definiteness of adjectives and missing commata. All of these error types have a good performance with precisions between 78% and 98.5%. Judging from their regular expressions to insert artificial errors, most of their error types seem to be fairly local errors that can be resolved based on bigrams. The Russian system focusses on more advanced error types - case, number agreement, gender agreement, preposition and aspect. However, the results show that the system is still in its initial phase with low precision and recall for most error types (precision is between 22% and 56%, only gender agreement reaches 68%, and recall is significantly lower, between 9% and 36%). None of these approaches deals with compound error detection. For neural network approaches, large corpora with error mark-up are necessary, which are not available for North Sámi. The error marked-up corpus contains 120 459 words, and when looking at specific error types – as in this case compound errors – the corpus is even smaller. The Russian system is based on an error-marked corpus of 200k words (deemed too small by its authors), the Latvian system works with artificial errors, an approach that can be problematic as it does not reflect real text errors. In compounding, two or several words are combined to form a new word. In Sámi, Finnic and Germanic languages, compounding is a productive process and new compounds like in (1) can be made on the fly. In Romance languages, these compounds typically correspond to prepositional constructions (ital. ‘la federa del cuscino del divano’). The initial motivation for extensive lexicalization of compounds of North Sámi goes back to adapting the spellchecker to users’ needs, i.e. avoiding false alarms in Ávvir newspaper’s texts. North Sámi is a Uralic language spoken in Norway, Sweden and Finland by approximately 25 700 speakers (Simons and Fennig, 2018). It is a synthetic language, where the open parts of speech (PoS) – nouns, adjectives, etc. – inflect for case, person and number. The grammatical categories are expressed by a combination of suffixes and stem-internal processes affecting root vowels and consonants alike, making it perhaps the most fusional of all Uralic languages. In addition to compounding, inflection and derivation are common morphological processes in North Sámi. North Sámi has seven morpho-syntactic cases, i.e. nominative (Nom.), genitive (Gen.), accusative (Acc.), illative (Ill.), locative (Loc.), comitative (Com.), and essive (Ess.). Case plays a more central role in Sámi than in preposition-based case languages, since here syntactic functions are identified based on case only. In addition, nouns can bear possessive suffixes. Verbs are inflected for person, number (singular, dual, plural), tense (present and past tense) and mood (indicative, conditional, and potential). Derivational processes (passive, causative, inchoative, diminutive, reflexive, to name only some of them) enhance the combinatory possibilities of each verb. Table 1 illustrates that compounding in North Sámi is by no means restricted to noun noun combinations, but includes a number of other parts-ofspeech (PoS) as well, also as heads. In North Sámi, compounds are formed without a hyphen, except for those involving a proper noun, a digit, or an acronym like Davvi-Norgii ‘Northern Norway (Ill.)’, 3-juvllatsykkel ‘tricycle’, and ILO-álgoálbmotsoahpamuš ‘ILO-indigenous people agreement’ (Riektačállinrávvagat, 2015, p.46). There are a number of multiwords where a space is obligatory (albma ládje ‘properly’ and duollet dálle ‘sometimes’). Also genitive first compounds have an alternative interpretation when written apart, which makes error detection more difficult.

Conclusion
We have shown that the lexicalization of compounds – in addition to their dynamic treatment – is useful and necessary for two language applications for North Sámi, an e-dictionary (NDS) and a grammar checker (GramDivvun). The evaluation of NDS shows that we get a good coverage: 98% of the compounds logged do get a translation and 72% are lexicalized in the FST. The evaluation of GramDivvun has shown that we manage to identify compound errors with a precision of 98% and a recall of 49% utilising a combination of information from the lexicon and syntax. We conclude that there are perfectly good reasons for lexicalizing compounds, i.e. providing idiomatic translations for when it cannot be derived from the parts, and to support compound grammar checking. At the same time, lexicalization can dissimulate word formation information in corpus tools. This can be resolved and we have already implemented a solution in Constraint Grammar to make the information available in a future version of the corpus tool. As dynamic compounding is limited to few PoS at the moment, in the future we want to investigate and model compounding of other PoS (in the FST). Also experiments with neural network approaches and a comparison of the results to our rule-based grammar checker could be an interesting future project.