A (Non)-Perfect Match: Mapping pl W ord N et onto P rinceton W ord N et (2021)

Abstract: The paper reports on the methodology and final results of a large-scale synset mapping between plWordNet and Princeton WordNet. Dedicated manual and semi-automatic mapping procedures as well as interlingual relation types for nouns, verbs, adjectives and adverbs are described. The statistics of all types of interlingual relations are also provided.

## introduction the goal of this paper is to present the machinery and guiding ideas behind a large-scale synset mapping between the polish plwordnet (henceforth, plwn) and the english princeton wordnet (henceforth, pwn). the resulting mapping is unique in terms of its character, scale and methodology. first, it is probably the only mapping built between the two very large wordnets constructed completely independently of each other @xcite , @xcite , that is with no form of pwn content (synset) translation or structure mapping (in plwn construction) 1 . second, it employs and further extends the whole array of inter-lingual inter-wordnet relations proposed for eurowordnet by @xcite , yet never fully implemented. third, it is a manual mapping partially enhanced by automatic prompt systems yet not relying on them @xcite , @xcite . as such, the mapping had a lot of challenges: (partially) different wordnet construction methodologies of plwn and pwn; profound cross-linguistic differences between the synthetic polish and the analytic english language; numerous cultural, sociological and historical differences between the two language communities affecting their lexicons to a large extent. still, it seems the above challenges have been successfully met, proven by a number of cross-linguistic applications of the bilingual polish-english wordnet. in the paper we describe the systems of interlingual relations proposed and implemented for noun, adjective, adverb and verb synsets, presented in a chronological order motivated by the increasing difficulty of the milestones of the project. we close with the current statistics of interlingual synset relations. ## related works wordnet started off as a lexico-semantic network for english @xcite . with a quickly manifested potential both for linguistics and nlp research, it soon found its followers for other languages, e.g. germanet for german, @xcite . since crosslinguistic applications are always welcome, the idea of linking monolingual wordnets into a multilingual network naturally arose. it was put into practice in the eurowordnet project @xcite and more recently in the openmultilingualwordnet project @xcite . even before the start of eu-rowordnet project, it was clear that constructing a wordnet from scratch and later linking it to similar resources is time and moneyconsuming. few teams could afford it. on the other hand, taking princeton wordnet as a basic template and expanding on it proved much more economical in terms of the investment needed. the two approaches were called merge and expand, respectively. yet, science does not operate on a simple winlose model. the expand approach rests on a long-abandoned assumption of the universality of mental lexicon @xcite . thus, expand wordnets are useful language resources, but the accuracy of the specific language structure of the internal relation network may be arguable. examples of expand wordnets are multiwordnet @xcite , asianwordnet @xcite , indowordnet @xcite , open dutch wordnet @xcite , and slownet for slovenian @xcite . the few wordnets built through the merge approach are germanet @xcite , dannet, built on the basis of a danish dictionary @xcite , and ruwordnet, constructed by a semi-automatic transformation of a russian thesaurus @xcite . ## nouns -bottom up the prerequisite for mapping plwordnet onto princeton wordnet was a reasonably advanced state of development of the former. thus, we started with plwn 2.0 @xcite and pwn 3.1, see the first challenge of mapping were partly different philosophies behind the construction of plwn and pwn @xcite . in plwn, synset is rigidly defined as a set of lexical units sharing a set of constitutive relations (hypo/hypernymy, mero/holonymy, antonymy) @xcite . this explains more fine-grained sense differentiation in plwn than in pwn, where synset member-ship is more arbitrary @xcite . the second challenge of mapping were substantial cross-linguistic differences between english and polish that also found its reflection in relation structures of the two wordnets. to explore the synthetic character of polish, new types of lexical unit relations (pwn morphosemantic links) were added to plwn, such as, for instance, diminutivity (e.g. pies 2 -'a dog'piesek 1 -'a small or young dog'), or crosscategorial synonymy (e.g. piesek 1 -pieskowy 1 -'[adj] related to a small dog'. the latter relation is established between a base and its derivative of a different pos when they relate to the same concept. in addition, pwn provided short definitions called glosses, sometimes followed by examples, for every synset, while plwn started adding glosses for lexical units (not for synsets) only at a later stage foot_1 of its development around the 3.0 version. with the above challenges in mind, the guiding idea of the mapping was to link nodes of wordnet graphs that would mainly correspond in terms of relation structures (and possibly also with respect to glosses). this turned out a non-trivial task. often, even for closely related concepts, their relation structures were partially or wholly different @xcite . this explains the use of different types of interlingual relations (i-relations) going far beyond interlingual synonymy defined as simple equivalence by @xcite . most of vossen's complex equivalence relations were adopted (e.g. i-hypo/hypernymy, inear-synonymy, or i-mero/holonymy). some new ones were added too, such as, for instance, interlingual inter-register synonymy. of the four parts of speech described by plwn and pwn, nouns share the most in terms of the fundamentals of their internal synset relation structure @xcite . the basic relation is hypo/hypernymy, followed by mero/holonymy and near-synonymy. therefore, the set of irelations and the mapping procedure were first defined for and applied to noun synsets. it consisted of i-synonymy, i-partial-synonymy, for the first stage of mapping, we chose nouns from semantic domains (pwn lexicographer files) such as person, artefact, food, place, time, and names connected with thinking and communication, so as to start with concrete nouns, more likely to have unique referents regardless of a language, and then move to abstract nouns, for which the reference is often more culturally and socially dependent. we decided to go 'bottom up' in a wordnet graph. such a move was motivated by the idea to start with the most specific, possibly unambiguous part of plwn which would form a basis for the further mapping. also, we were trying to cover the whole branches of the hyponymy tree. the mapping direction was plwn-pwn. ## adjectives and adverbs -from hierarchy to dumbbell and island with the mapping of nouns in progress, we proceeded to adjectives. the biggest challenge of adjective mapping were very different models of their internal synset relation structure in plwordnet and princeton wordnet, shown in on the contrary, adjectives in pwn are organized around similar to relation with central and peripheral adjectives in the so called dumbbell model @xcite . similar to relation is rather vague, but can be re-interpreted as one level hyponymy with a central adjective functioning as a hypernym of its peripheral adjectives. a subsidiary relation is member of this domain. these profound differences in synset relation structures made us look into lexical unit relations. the latter exhibit more correspondence: antonymy/antonym, cross-categorial synonymy/pertainym, derivativity/derivationally related form. therefore, in the mapping process we decided to consider both types of internal relations as well as the already existing i-relations between noun synsets, because some of the adjective relations are cross-categorial relations to nouns (e.g. cross-categorial synonymy, derivativity, member of this domain). we proposed the following set of i-relations for adjective synsets: i-synonymy, i-hypo/hypernymy, i-partial synonymy, i-inter-register synonymy, and i-crosscategorial synonymy @xcite , @xcite . the latter relation was always used together with i-hyponymy to keep the pos information, and, in the case of very general i-hyponyms, to give more specification to the meaning of a mapped adjective. the use of such a pair of relations also allowed us to make up for the difference in size between plwn and pwn. having mapped a substantial part of adjective synsets, we moved to adverbs. again, adverbs are more numerous in plwordnet than in princeton wordnet, with twice as much lemmas, almost three times more lexical units and almost four times more synsets, as illustrated on the other hand, in plwordnet many adverbs were systematically derived from adjectives @xcite , hence they have a similar set of synset relations as adjectives (apart from modifier), see ## verbs -from lexico-grammatical hierarchy to semantic fields verbs were the last category to map due to their most complex and divergent relation structures resulting from substantial differences in rendering aspect and other verbal categories in english and polish. polish lexicalises aspect and in plwn perfective and imperfective verb forms always land in separate synsets (even in the case of pure aspectual pairs e.g. czytać -'read/be reading' vs przeczytać -'have read'). this partly accounts for bigger number of verbal elements in plwordnet 3.1 in comparison to princeton wordnet 3.1 (see similarly to nouns, verb synsets are organised around hypo/hypernymy relation both in plwn and in pwn. other pwn verb relations include verb group, member of this domain (mainly topic), and, relatively sparse, entailment and cause. at the start of verb mapping, plwn 3.1 used causation, processuality, and less numerous distributivity, inchoativity and iterativity ( thus, verb mapping had to overcome (partly) non-congruent internal relation networks and specific linguistic differences. therefore, we decided to use isynonymy, i-inter register synonymy, and i-hypo/hypernymy and introduce new interlingual relations specific to verb mapping. they were based on @xcite and include: i-attenuativity (to v to a lesser ## procedures and tools the entire mapping has been performed manually by a team of trained bilingual lexicographers supervised by senior lexicographers @xcite . the actual mapping has been taking place in a custom-designed wordnet editing system called wordnetloom which allows to visualise wordnet graphs for different languages on the same screen, manipulate them, compare their fragments and establish relations @xcite . the fact that an editor can see the relation structures for both languages foot_3 , interactively explore them in any direction and depth, and make changes, e.g. by adding irelation links directly to the graphs, noticeably facilitated the mapping process. moreover, lexicographers' work has been monitored via another custom-designed tool, namely the wordnet tracker system @xcite documenting every action of a lexicographer in real time. due to the scale of the project and its financing conditions, we worked in 1+1 model (a lexicographer establishing i-links plus a supervisor checking their adequacy). the manual mapping procedure was first designed for nouns, but its basics have been kept for other parts of speech as well @xcite . it consists of three main stages: (1) recognising the sense of a source synset, (2) searching candidates for a target synset, and (3) choosing a target synset and a type of interlingual relation. in the first stage, we carefully analyse the source synset internal relation structure, gloss, examples as well as interlingual relations if there are any within the close nodes in its hyponymy tree. in the second stage, candidates for a target synset are nominated on the basis of a bilingual linguist's intuition and information found in polish-english language resources. next, candidates for a tar the results of the first stage of mapping of nouns were used as the input to an automatic prompt system developed for further stages of noun mapping @xcite . the system was based on the relaxation labeling algorithm of @xcite . it mirrors the manual mapping procedure to the extent that it compares parts of a wordnet graph and suggests the closely related fragments. next, polish-english synset pairs produced by the algorithm were filtered by the so called cascade polish-english dictionary foot_4 and pairs of synsets whose lemmas appear as dictionary equivalents were given the status of automatic prompts and presented to lexicographers as special links in the wordnetloom system. moving to the mapping of adjectives, we could not resort to the automatic prompt system developed for nouns, because relaxation labeling algorithm used there requires parallel hierarchical structures to operate. such structures are missing in the case of pwn adjectives. that made us look for other solutions. despite superficially divergent models in plwn and pwn, we searched for common points in the relation structure both at synset and lexical unit level. as a consequence, two rule-based algorithms were designed that produced automatic prompts for the first stage of adjective mapping @xcite . the first one relied on synset relations exclusively, the second one on synset and lexical unit relations. both also took advantage of i-synonymy relations between noun synsets (provided the latter were internally linked to adjectives). pairs of polish-english lemmas (from the pairs of adjective synsets generated by the algorithms) were automatically verified by the cascade dictionary. those recorded in the dictionary were presented to lexicographers in the form of prompts for manual mapping, @xcite . the procedures and relations developed for adjectives also found its use in the mapping of adverbs @xcite . in plwn many adverbs were automatically derived from adjectives. that allowed to generate automatic prompts for adverb mapping on the basis of adjective mapping. it consisted in copying interlingual relations established for adjective synsets to adverb synsets provided that the latter were systematically derived from the former in plwn. another necessary condition was that target pwn adverbs were also derived from pwn adjectives already linked by an interlingual relation to plwn adjectives. next, the prompts were verified by lexicographers and manual links were established. at the same time, adjective links were critically evaluated and modified, when necessary. in the overwhelming majority of cases automatic prompts were valid and a manual relation was established. more work was required for cases of one-to-many mapping (e.g. one synset serving as a hypernym for several other wordnet's synsets), while the most difficult cases constituted adverbs that were not derived from plwn adjectives holding i-relations to pwn adjectives. these required independent search for target synsets. as for verb mapping, no automatic prompt system was designed. although both plwn and pwn verb relation networks are hierarchical, these hierarchies are based on non-congruent prerequisites. moreover, verb synsets are also linked via non-hierarchical relations, different in both wordnets. these differences combined with linguistic differences between polish and english aspect morphology enforced a fully manual mapping procedure. the main focus in the procedure was put on providing as close meaning correspondence as possible. this was achieved by finding the most suitable plwn -pwn synset pair and choosing the i-relation that most adequately captures the meaning relation. the selection of i-relations was hierarchical. i-synonymy and i-inter register synonymy were prioritized. for verbs, we have exceptionally allowed for double synonymy in the case of pure aspectual pairs of verbs in polish linked to aspectually unmarked english verbs (creating 2 -to -1 mapping). verb-specific relations were selected if i-synonymy and i-inter register synonymy relations could not be established and the prefix of the plwn verb carried a relevant facet of meaning. i-hyponymy relation was treated a 'last resort' relation, as it provided the most general meaning correspondence. in the cases in which pwn lacked a relevant verb synset, but a noun or adjective synset which would be used in a copula-construction in english was present, i-cross-categorial relations coupled with i-hyponymy relation linking plwn and pwn verb synsets were selected. ## result: a bilingual network and its applications the result of bidirectional mapping of plword-net and princeton wordnet is a large polish-english wordnet with almost 300k of unique interlingual relations. the counts of all types of i-relations are shown in the quality of the created resource has been confirmed by a number of applications, fostered by its open wordnet licence foot_6 . these include language learning and teaching (e.g. the creation of didactic tools such as cloud-net word cloud generator foot_7 ), dictionary making and machine translation (a component for pons, glosbe, kamusigold, ling.pl, ba-belnet, open multilingual wordnet, google translate), semi-automatic mapping of a number of domain thesauri as well as sumo ontology on plwn, bilingual word sense disambiguation (sherlock holmes corpus?), multilingual wordnet construction and contrastive studies (open multingual wordnet, the yiddish project foot_8 , foot_9 ). ## conclusion and further works the created resource is unique not only because of its scale and method of construction, but mainly due to the fact that it uses a rich network of interlingual relations which had not been done before. such approach has its pluses and minuses. it shows the complexity of a bilingual lexicon, yet it does not offer that many simple equivalents (often very much wanted by dictionary users). this is also partly due to the fact that wordnet mapping is synset mapping. however, we saw a significant potential for future development of the created bilingual resource. thus, we have started a project @xcite on converting the synset level mapping to an interlingual mapping between lexical units based on the concept of translational equivalence @xcite . three types of equivalence links were identified: strong, regular and weak, in addition to the lack of equivalence. the recognition of a type of equivalence was based on the manual verification of values of equivalence features, cf @xcite . in a pilot study, equivalence links were manually described for ≈10 000 bilingual pairs of senses (lexical units) coming mostly from noun synsets linked by isynonymy. on average, only 1-2 strong equivalence links were identified for a pair of synsets @xcite . as a result, a precise bilingual sense-level dictionary that can be used in translation, but also in many bilingual wordnet application was developed. we plan to expand this mapping both to remaining noun pairs and to other parts of speech.