Incorporating prepositions in the B ul T ree B ank W ord N et (2023)

Abstract: A model for preposition incorporation in the BulTreeBank WordNet is presented which follows the model for presenting open class words in wordnets. An adapted semantic classification of prepositions is done on the base of Bulgarian grammars and the classes are used for synset categories. The good coverage of prepositions in the wordnet will be used for the aim of neural language models creation for Bulgarian. This extension of the wordnet improves its utility for semantic annotation.

## introduction the paper aims at presenting a model for preposition incorporation in the bultreebank wordnet (btb-wn) @xcite . prepositions are considered a beneficial extension of the part of speech coverage of btb-wn, because they would improve its utility for semantic annotation, word sense disambiguation, machine translation, etc. additionally, they would provide a better quality of neural language models for bulgarian, which is the long term purpose of this task. btb-wn was created on the base of the core wordnet subset foot_0 of princeton wordnet (pwn) @xcite that contains the 5000 most frequent english senses. after that it was expanded with content words from the bultreebank and a bulgarian frequency list, as well as with senses from the bulgarian versions of wikipedia and wiktionary. initially it was mapped to the pwn, but since 2020 it shifted to the open english wordnet @xcite , because it is being developed and updated, in contrast to the pwn. the current version of btb-wn foot_1 -4.0 -contains more than 33 000 synsets and could be browsed online foot_2 . btb-wn will be soon freely available for downloading in the wordnet lmf format. prepositions like any other closed class words usually are not presented in wordnets, including btb-wn, which contains the four most common parts of speech -nouns, verbs, adjectives and adverbs. prepositions are one of the most frequent and at the same time ambiguous parts of speech and additionally the independence of their semantics is often argued @xcite . here a semantic categorisation of bulgarian prepositions is done and the model for presenting prepositions in btb-wn follows the model for the open class words. prepositions serve to establish different relations between words: on one hand grammatical -they indicate the syntactic position of words in phrases (object, adverbial, modifier), and on the other hand semantic -they reveal their sense relations @xcite . in this research only the semantic function of prepositions is considered. section 2 gives an overview of different preposition research, section 3 presents the semantic preposition classification that is used, section 4 contains the synset model for prepositions in btb-wn, and section 5 concludes the paper. ## related works bulgarian grammars classify prepositions by their origin, morphological composition and semantics. a detailed review of the history of preposition classifications is presented in @xcite . for the aim of preposition incorporation in btb-wn two classifications are considered and a new more compact categorization is compiled @xcite , @xcite ). the role of prepositions in nlp and the variety of approaches towards their processing are thorthere are two resources particularly dedicated to prepositions: prepnet @xcite and the preposition project @xcite . the preposition project is a semantic database for english prepositions which has been used for word sense disambiguation. it combines data for prepositions from a dictionary and from framenet foot_4 , where english prepositions are functionally tagged. prepnet was originally build for french, but later extended for several languages. the approach in prepnet is inspired by thematic role classifications and it also uses data from framenet. o'hara and wiebe (2009) approached preposition disambiguation using the semantic roles from penn treebank, the semantic network factotum and framenet, and hypernyms from the princeton wordnet foot_5 . preposition classifications particularly directed towards wordnets are done by @xcite and @xcite . @xcite shows an approach where by using information from princeton wordnet and applying inferential heuristics two types of phrases are analysed -noun+preposition+noun and verb+preposition+noun. the phrases are organized in classes if there are hyperonymy, hyponymy or synonymy relations between the verbs and the nouns in the phrases or if they have common hypernym/hyponym. @xcite presents a very interesting approach towards preposition integration in wordnet, including visual description by using typical wordnet relations for portuguese prepositions. the integration is not large scale, only prepositions for movement are processed and the following relations are introduced: synonymy, antonymy, hyponymy/hyperonymy and causes/is caused by. as far as i am concerned bulnet 7 is the only wordnet that has prepositions but the incorporation in it is not beneficial enough for many nlp tasks -they are presented with definitions, synonyms, examples and english translations, but they do not have any relations. another relevant work is the research of da costa and bond (2016) on the incorporation of non referential words in wordnet. they expand the open multilingual wordnet foot_7 with interjections and numeral classifiers motivated by the task of semantic annotation, similarly to the case of btb-wn. for interjections two relations are used: exemplifies (with other parts of speech) and see also (with interjections). for the classifiers exemplifies is used, but also two new relations are introduced: classifies and classified by. the penn discourse treebank also contains annotations of closed class words including some prepositions @xcite . my approach is similar to that of amaro (2018) and @xcite , because the presentation of prepositions in btb-wn is following the model for the open class words in wordnets. ## semantic classification of prepositions for their integration in btb-wn prepositions have been semantically classified in 15 groups: location, time, transition, manner and instrument of action, possession, quantity, degree and exceeding of limit, purpose, origin and part of a whole, opposition, comparison, cause and object class: exchange, exclusion, opinion and thought. there are several differences from the grammars in the adapted classification mainly motivated by the aim of having a more compact and generalpurpose system. for example, the classes manner of action (слушам с внимание 'listen with attention') and instrument of action (пиша с молив 'write with a pencil') here are united in one class, because they are very closely related. the same applies for the origin (тя е от града 'she is from the city') and part of a whole (яж от този хляб 'eat from this bread') classes. the approximation of time (към 9 часа 'around 9 o'clock') and approximation of quantity (около 3 килограма 'about 3 kilograms') classes from @xcite here are included respectively in the time and quantity classes. the exceeding of limit sense (това е свръх възможностите ми 'this is beyond my abilities') is included in the quantity category. an object superclass is outlined to unite the expression of relations for exchange (отиди вместо мен 'go instead of me'), exclusion (няма други гости освен семейството 'there are no other guests except the family'), thought (разкажи ми за пътешествието 'tell me about the journey') and opinion (според мен това е добра идея 'to me this is a good idea'). the prep.obj.thought class includes expression of object of thought, speech and writing. the metaphorical usages of a given class are considered part of it, not a separate class. for instance, usages like "тия неща са врязани в паметта ми." ('these things are etched in my memory') are considered as examples of the location class. ## preposition synset model preposition synsets have synset category (based on their semantic class), detailed definition, examples, synonyms if available and as much as possible relations. the part of speech value for prepositions in btb-wn is p, following the format of the global wordnet association foot_8 . an example is shown in figure 1 with the synsets for preposition в ('in'). the main intention for the preposition relations is that they follow the relations model of any other part of speech in wordnets. two types of relations are used: between preposition synsets and between a preposition synset and other parts of speech. examples for the first type are: synonymy (the prepositions върху, въз, връз, на 'over, on' all express position or motion over some surface, something or someone), antonymy (върху 'over' is antonym of под 'under'), hyperonymy and hyponymy (в 'in' in its most general meaning for 'position or action in the limits of something, somewhere' is hypernym of several prepositions which express more specific location relations, such as сред, всред, насред, посред 'in the middle', из, низ, по 'through', между 'between', през, пряко 'across'), similar (върху 'over' is similar with над 'above'). the second type is intended to link combinations of verbs and prepositions (and as a plan for future work -nouns and prepositions) which tend to express a particular meaning together (such as the combination of the verb превръщам се 'turn into' and the preposition в 'in, into' express transition in new state). the sem-derived-from relation can be used both between prepositions and between prepositions and other parts of speech (върху 'over' is derived from the noun връх 'top' and so does the preposition свръх 'above', so they are also linked with this relation). more relations applicable to prepositions are planned to be considered. currently 62 preposition lemmas are available in btb-wn with 105 synsets. the most polysemous prepositions prove to be на (most frequently could be translated as 'on', 'of', 'in', etc.) with 12 synsets, followed by по ('over', 'in', 'on', etc.) with 11 synsets. the prepositions за ('for', 'to', 'about', etc.) and от ('from') are part of nine synsets each; с ('with') is in eight synsets and до ('to', 'until', etc.) and в ('in', 'at') are found in seven. as ## conclusion and future work an attempt for preposition incorporation in the btb-wn is presented. a semantic classification of prepositions is adapted on the base of bulgarian grammars. the preposition synsets follow the structure and relations model of the nouns, verbs, adjectives and adverbs in wordnets and currently six semantic relations are introduced for prepositions: synonymy, antonymy, hyperonymy, hyponymy, similarity and semantic derivation. there are several directions in which this research would be elaborated: the hierarchy inheritance and categorization of the verbs and nouns in wordnet will be used and also features from a valency lexicon for bulgarianit will provide data about the types of prepositions which occur in the verbs' frames and about the semantic roles of their arguments. a classification based on semantic roles could be applied, given the good results that it provides for different languages. recent research @xcite , da costa and bond (2016), etc.) show that closed class words have a place in wordnets and contribute for different nlp tasks if integrated. the good coverage of prepositions in btb-wn will benefit its utility for semantic annotation and generation of pseudo corpora, which to be used for creation of neural language models in bulgarian.