Leaving No Stone Unturned When Identifying and Classifying Verbal Multiword Expressions in the R omanian W ordnet (2019)

Abstract: We present here the enhancement of the Romanian wordnet with a new type of information, very useful in language processing, namely types of verbal multi-word expressions. All verb literals made of two or more words are attached a label specific to the type of verbal multi-word expression they correspond to. These labels were created in the PARSEME Cost Action and were used in the version 1.1 of the shared task they organized. The results of this annotation are compared to those obtained in the annotation of a Romanian news corpus with the same labels. Given the alignment of the Romanian wordnet to the Princeton WordNet, this type of annotation can be further used for drawing comparisons between equivalent verbal literals in various languages, provided that such information is annotated in the wordnets of the respective languages and their wordnets are aligned to Princeton WordNet, and thus to the Romanian wordnet.

## introduction the romanian wordnet (rown) is a rich lexical and semantic resource. its development followed the expand method @xcite and started within the balkanet project @xcite . alignment with princeton wordnet (pwn) @xcite was a consequence of this working method and has always been one of the objectives whenever new synsets were developed for enlarging the rown. consequently, alignment with all the other wordnets aligned with pwn is obtained, which is a great asset for both interlingual lexical comparison or for applications working in a multilingual environment. the expand model in wordnets development implies importing the structure of the pwn (that is, its semantic relations) and translating the source synsets (from pwn), so that the meaning encoded by the english synset is rendered in the target language (romanian, here). as a consequence, a romanian synset may have one of the following structures: (i) list of words; (ii) list of free word combinations; (iii) empty list. (i) a list of words is a list of simple words (ex. zâmbi ("smile")) and/or expressions (ex. casȃ de bani (house of money "strong box")). these expressions are what in lexicographic terms is called idioms, terms, etc. (ii) whenever no word or expression could be found in romanian for rendering the meaning of the english synset, a free word combination, when possible, was used for implementing the respective synset: ex.: pune jos is a literal in the romanian synset equivalent to the pwn 3.1 {ground:10} (gloss: place or put on the ground). these are examples of recurrent free phrases, as @xcite call them. (iii) in case not even such a combination could be found, the synset was left empty and a special tag is used for keeping track of them (they are marked as nl, i.e. non lexicalized): ex.: the english synset {change state:1, turn:4} (gloss: undergo a transformation or a change of position or action) has a non-lexicalized corresponding synset in rown. however, as already pointed out @xcite @xcite , these lexical gaps should be reduced as much as possible when use of wordnets is envisaged for tasks in a multilingual environment (see machine translation), but also for word sense disambiguation @xcite . as far as this structure of its synsets is concerned, rown looks as rendered in types of synsets number percent all synsets 59,348 synsets containing only simple literals 41,188 69.5% synsets containing simple literals, expressions and free word combinations 2,813 4.7% synsets containing expressions and/or free word combination 12,590 21.2% non-lexicalized synsets 2,757 4.6% as far as the distribution of simple literals and expressions in rown is concerned, at present, we are carrying out a bilateral (romanian-bulgarian) project of annotating the different types of multiword expressions in the romanian wordnet. the first step is annotating the verbal multiword expressions (vmwes). this follows naturally from our participation in the parseme cost action 1 and in the creation and annotation of the corpora used in the parseme shared tasks 1.0 @xcite and 1.1 @xcite . this paper focuses on the annotation of romanian wordnet data. we present the parseme typology of vmwes and the types applicable to romanian (section 2), the process of annotating the verbal literals in rown with these types of vmwes (section 3) and we discuss the obtained results, as well as a comparison with those from the annotation of a romanian news corpus with the same types of vmwes (section 4), before concluding the paper. ## typology of verbal multiword expressions for the organization of a shared task on the automatic identification and classification of vmwes, the existence of an annotated corpus was one of the prerequisites. the interest in this initiative manifested by representatives of quite a large number of languages lead to fruitful discussions and the creation of an annotation manual defining the scope of the task, the types of vmwes to be annotated and their characteristics. the annotation guidelines capture the idiosyncrasies of all the languages involved. according to the last version of these guidelines 2 , vmwes fall into universal, quasi-universal and language specific categories, the first two having some subcategories, as follows: romanian displays only the following types of vmwes from the parseme classification: lvc.full: lua o decizie (make a decision), @xmath0.cause: da bȃtȃi de cap (give headaches), vid: trage pe sfoarȃ (pull on rope "cheat") and irv: se preface ("pretend"). these labels were used for the annotation of the romanian corpus used in the shared task version 1.1 (as in version 1.0 the vmwes types were slightly different). no language specific categories were necessary in the corpus annotation. ## annotation of the types of vmwes in rown the task of annotating the vmwes in a wordnet is different in some respects from their annotation in a corpus. first, all components are present as one literal in the synset, whereas in a corpus they need to be identified, according to the specifications available for all languages (e.g., auxiliaries, clitics or negation are not annotated as parts of the expression). second, whenever at least one element of the vmwe inflects for number, gender, etc., it has a unique form in the wordnet, the one considered lemma, while in the corpus all inflected forms may be found and need to be recognized. third, no voice alternation is to be found in the wordnet, while this can be spotted in a corpus. fourth, when the decision on whether a word combination is a vmwes depends on the meaning of that combination, the gloss attached to the synset is useful for this and the decision is based on it. the annotation of vmwes in rown was done by one linguist, with experience in annotating vmwes in a corpus, following the parseme guidelines. thus, we cannot discuss here the difficulty of this annotation or any controversial cases. the data are stored in a standoff file foot_1 . the file contains the literals in each synset, their vmwe label and the unique identifier of each synset, which is taken from pwn 3.0. all vmwes in rown were identified, extracted and were assigned to one of the types of vmwes applicable to romanian (lvc.full, lvc.cause, irv and vid). however, these types proved not enough for this task. the free word combinations with a verb as head could not be annotated with any of these labels, as expected, in fact. consequently, we marked them with a new label, none: they have a literal, compositional meaning, they do not display the characteristics of the vmwe classes: such an example is culege nuci (pick nuts). this type of annotation is done at the literal, not at the synset level (see also the discussion about the distribution of different types of vmwes within a synset, in the next section). although the vast majority of vmwes belong to only one type, there are literals which are annotated differently when belonging to different synsets, i.e. when having different meanings. out of only a handful of such cases, here is one example: the expression scoate fum (give out smoke) is annotated as none when being in the synset corresponding to the english {fume:4; smoke:4} (gloss: emit a cloud of fine particles) and it is annotated as vid when belonging to the synsets corresponding to the english {steam:3} (gloss: get very angry). ## annotation results the distribution of the types of vmwes in the rown is presented in as far as the correlation of these figures with those found in the corpus annotated in parseme (see we can conclude that the irv type is the most frequent both at the lexicographic level and in language use for romanian. figure 1 shows the presence of vmwes in synsets of different lengths. we notice their greatest presence in shorter synsets (especially of lengths 1 or 2). figure 2 shows the distribution of rown synsets made up only of vmwes by the number of literals in the synset. this is relevant for the productivity of the synonymy relation between vmwes. as one can see, most of these expressions (867) do not have synonyms. it is noteworthy that this is the case mainly with those annotated as none, which is further proof of their artificial nature. there are 220 literals in which there are pairs of synonymous vmwes. synonymy among three vmwes is displayed by 41 synsets, among four vmwes by 12 synsets, among five vmwes by 4 synsets, among six vmwes by 1 synset, and among twelve vmwes by 1 synset. this very rich synset is {fi de gardȃ, fi de pazȃ, fi de strajȃ, fi de santinelȃ, face de gardȃ, face de strajȃ, face de pazȃ, face de santinelȃ, sta de pazȃ}, which is the equivalent of the pwn synset {stand guard:1, stand watch:1, keep guard:1, stand sentinel:1} (gloss: watch over so as to protect). this romanian synset is based, on the one hand, on the synonymy among the nouns in the vmwes structure (gardȃ, pazȃ, strajȃ, santinelȃ) and, on the other hand, on their collocation with three different verbs (fi, sta, face) for rendering the same meaning. we analyzed the (277) synsets in which all literals are vmwes in order to identify the synsets for which all types of mwes occurring in the respective synsets are the same. after excluding those synsets containing only strings annotated as none (129), we counted 37 synsets in which the literals are all vid, 3 in which they are all lvc.full, 2 in which they are all lvc.cause and other 2 in which they are all irv. ## conclusions we have presented here the enhancement of the rown with a new type of syntagmatic information, namely labels for vmwes. the importance of and, at the same time, the challenges raised by these lexical units for processing natural languages have been previously discussed (see, among many others, @xcite , @xcite ). moreover, the impact of mwes resources on the mwes recognition in texts was proven by riedlbiemann, : "in the case that high quality mwe resources exist, these should be used. if not, it is possible to replace them with unsupervised extraction methods". @xcite are also in favour of the creation of language resources containing mwes, as many and diverse as possible; their presence in resources available for training systems for mwe identification being more important than their frequency (in annotated corpora). the results obtained in the annotation of the vmwes in the rown are presented, as well as a a comparison with those obtained by annotating a news corpus with these types of vmwes is drawn, showing that the distribution of types and their frequencies at the lexicon level are different from those at the corpus level. as further work, we envisage adding information about prepositional restrictions of the verbs in rown. this was another type of vmwes in parseme, but annotating it was optional and we neglected it. the data annotated as presented here have been compared and discussed with the bulgarian data, as the wordnets for both these languages have been annotated with vmwes @xcite .