Lexical Perspective on W ordnet to W ordnet Mapping (2018)

Abstract: The paper presents a feature-based model of equivalence targeted at (manual) sense linking between Princeton WordNet and plWordNet. The model incorporates insights from lexicographic and translation theories on bilingual equivalence and draws on the results of earlier synset-level mapping of nouns between Princeton WordNet and plWordNet. It takes into account all basic aspects of language such as form, meaning and function and supplements them with (parallel) corpus frequency and translatability. Three types of equivalence are distinguished, namely strong, regular and weak depending on the conformity with the proposed features. The presented solutions are language-neutral and they can be easily applied to language pairs other than Polish and English. Sense-level mapping is a more fine-grained mapping than the existing synset mappings and is thus of great potential to human and machine translation.

## introduction currently, bi-and multilingual wordnets are most commonly inter-linked on the synset level, (e.g., @xcite . synsets can be composed of one or more lexical units (lemma-pos-synset triples, also called senses; henceforth, lus), so such inter-wordnet links may be of three types: 1-to-1 sense link (between two synsets each built of a single lu); 1-to-many sense link (between two synsets, one built of a single lu, the other of more than one); and many-to-many sense link (between two multiple-lu synsets). the (large) majority of inter-linked wordnets use one simple equivalence relation to connect their synsets (ef-fectively synonymy). if, due to substantial differences between languages, such a link cannot be introduced, sometimes artificial synsets are created to provide equivalents (e.g., @xcite . when we consider 1-to-many and many-to-many sense links, the question arises whether the correspondence between all their component lus is of the same strength. basic principles of language economy state that within one language there should not exist two different forms that share identical function and meaning, so there have to be slight differences between component lus of a given synset, and even larger differences between the lus from two synsets representing two different languages (even if those synsets are linked by i-synonymy). existing research on interwordnet mapping between plwordnet @xcite and princeton wordnet @xcite , especially 1-to-many and many-to many sense links, has shown the potential for creating stronger links between some lus from a given pair of synsets @xcite ). to give an example, in the pair of synsets: {złoto n:3 , au n:1 } p l i-syn {gold n:3 , au n:1 , atomic number 79 n:1 } enzłoto n:3 p l and gold n:3 en and au n:1 p l and au n:1 en seem the best-fitted equivalents due to the agreement not only in sense, but also in register. the words from the first pair belong to the general register, while the ones from the second pair are from the specialist register. biand multilingual wordnets are used by translators who would certainly appreciate such a more detailed mapping. ## background equivalence is a popular concept used in, among others, translation studies and bilingual lexicography -see @xcite for a more detailed discussion, also regarding typologies of equivalence). the concept has many faces depending on which features of language or texts researchers focus on. for example, one may find binary oppositions such as, for instance, natural and directional equivalence, semantic and pragmatic equivalence, or full and partial equivalence, e.g. @xcite ; @xcite . when studying recent approaches to equivalence developed in the field of bilingual lexicography, one may also find a distinction between cognitive and translational equivalence @xcite . cognitive equivalents are typically general ones; they first come to the mind of a language user (even without any context) and when it comes to translation they may fit many contexts. translational equivalents, which may be extracted from corpus data, may be less obvious and sometimes they may slightly differ in their basic meaning; however, they may fit more specific contexts. in @xcite , we analysed basic equivalence types from translation and lexicographic literature and verified their relevance for synset-level wordnet mapping. we assumed that lus in the linked plwn-pwn synsets can be treated as bilingual dictionary data. we checked if pairs of lus might be treated as cognitive and translational equivalents depending on their frequency of use as equivalents in translation in a particular co-text and context. we put forward an initial proposal of sense-level mapping designed to cross-cut through cognitive and translational equivalence. in this paper we present an extended and verified version of our initial proposal with carefully defined equivalence features, equivalence types and a sense-level linking procedure supported by a number of examples. at this point, it is important to note that the term equivalence has been also used in the context of wordnets; more precisely, it was first used in the wordnet world to name a set of inter-lingual relations holding between synsets in the eurowordnet project @xcite . inter-lingual synonymy was defined as a simple equivalence relation "which only holds if there is 1-to-1 mapping between synsets". the remaining types of interlingual relations were called complex equivalence relations and allowed to obtain between oneto-many and many-to-many synset pairs. since many of eurowordnet wordnets relied on translation approaches, many of the senses can be translational equivalents. in designing a strategy for mapping synsets between princeton wordnet and plwordnet, @xcite built on this proposal in the set of i-relations. currently, the overall size of plwordnet amounts to 217,426 synsets, 282,749 senses (lexical units) and 190,555 lemmas and these numbers are constantly growing. the synset mapping between plwordnet and princeton wordnet encompasses 230,185 links, with 43,740 inter-lingual synonymy links. the number of polish synsets with at least one inter-lingual relation is 177,634 (only i-synonymy is unique to a synset pair). the majority of i-links form noun links, 122,811 instances covering about 92% of polish noun synsets (132,380 in total), the next are adjective links: 45,282 instances, covering 96% (46,721 in total), and last come adverb links with 9,541 instances, covering 84% of polish adverb synsets (11,256 in total). at the present stage there are no interlingual links between verbal synsets (27,069), but we are working on the mapping procedure for them. looking from the princeton wordnet direction, we have mapped 80% of noun synsets (72,621), 43% of adjective synsets (7,905), and 47% of adverb synsets (1,737). since nouns are the most stable semantic category, we have decided to make them the starting point for our procedure for sense mapping. other categories may require category-specific treatment which is outside the scope of the present paper. ## equivalence features in this section we discuss a set of features that will determine the strength of equivalence holding between (particular) lus from the mapped polish and english synsets. each feature will be followed by a short definition and examples. first, we will look at formal features, such as grammatical category, number, countability and gender. next, we will delve into semantic and pragmatic ones, such as sense, lexicalisation (of concepts), register, collocations, co-text and context. finally, we will consider translatability based on dictionary listing and translation equivalences extracted from the polish-english parallel corpus paralela foot_0 @xcite . ## equivalence types relying on equivalence features described in the previous section, we will define three equivalence types of a variable strength: strong, regular and weak (implied). the categorisation to a given type will be based on values of features a bilingual pair of lus will agree in. the types will be later reflected in three kinds of links between lus. some features will be agreed across all types, while some other feature will differ. summing up the discussion in section 3.1, there will always be an agreement in grammatical category (only nounto noun pairs are taken into consideration) and in most cases in number, countability and gender. instances of pluralia and singularia tantum as well as count-to-mass mappings will be dealt on an individual basis -the agreement will not always have to hold. cases of lexicalised natural gender in polish will be treated in a similar way. ## linking procedure having defined the equivalence features and types, below we put forward a linking procedure for lexicographers. in the procedure we lead lexicographers from simpler to more complex features and from wordnet data to dictionary and corpora data. we believe that there is no need for a lexicographer to verify each feature separately, but that they can be analysed in groups or pairs on the basis of the data provided by a specific resource. we will illustrate the linking procedure on the example of the pair of synsets {centrala n:2 } linked via i-synonymy to {headquarters n:1 , central office n:1 , main office n:1 , home office n:2 , home base n:2 }. formal features that is number, countability and gender should be verified first. gender is not relevant here, since we do not deal with an animate noun. on the other hand, we have an instance of a pluralia tantum in the english synset: headquarters n:1 . the remaining lexical units are regular countable nouns. next, we move to semantic (and partly pragmatic) features starting from the data provided in wordnets that is relations, glosses, qualifiers and examples. the key relations are hypernyms and hyponyms, as well as their i-synonyms or i-hypernyms. the polish synset {centrala n:2 } has {ośrodek n:2 , ...} -'center' as its hypernym, which is an i-synonym of the english {centre n:4 , ...}. it is glossed as: " siedziba centrali, główny ośrodek czegoś" -' the headquarters' seat, main centre of something'. it has general register and the usage example is the following: "pożar centrali mleczarskiej w miejscowości obok było widać z daleka." -'the fire in the dairy center in the nearby place could be seen from the distance.' the english synset {headquarters n:1 , ...} has {office n:1 , business office n:1 } as its hypernym. it is attributed with the following gloss and example: "(usually plural) the office that serves as the administrative center of an enterprise; "many companies have their headquarters in new york." there is no information about the register provided. next, in order to gather still more information about semantics and pragmatics as well as translatability of pairs of particular lus, lexicographers are asked to consult external resources such as dictionaries and encyclopedias as well as a polish-english parallel corpus paralela. looking up centrala in a couple of polish-english dictionaries (see ...), we find that its most frequent equivalents are headquarters, head office and central office. interestingly, head office does not appear in princeton wordnet at all. looking up headquarters in english-polish dictionaries, we obtain centrala and siedziba główna (the latter term appearing in the gloss of the polish synset); checking central office, we get siedziba główna and centrala. in the next step, we check the frequency of the pairs centralaheadquarters and centrala central office in the paralela corpus and we learn that the pair centrala and headquarters gets 40 hits, while centralacentral office gets only 3 hits. in the last step, we analyse the most frequent contexts of occurrence of centralaheadquarters and we get a couple of typical shared cotexts and collocations (examples given in section 3.2.) on the basis of the whole discussed data, we want to argue that the lexical units centrala n:2 -headquarters n:1 form a pair of strong equivalents, centrala n:2 -central office n:1 are regular equivalents, while centrala n:2 -main office n:1 , home office n:2 , home base n:2 should be treated as weak equivalents. ## conclusions the strategy for sense-level mapping between princeton wordnet and plwordnet nouns put forward in this paper is a new initiative in the wordnet world. it offers a possibility for fine-grained mapping that is of great potential especially for human and machine translation. it is illustrated with examples from the polish-english language pair, but the set of features described in this paper are language-neutral and they can be easily extended to wordnets of other languages of the indo-european family. as for (non)-indo-european language pairs, it is necessary to analyse whether the two languages share all the features that will be taken into account. also, the strategy may be extended to other grammatical categories such as adjectives and adverbs, which are already partially mapped on the synset level, and, eventually, to verbs after some mapping between verb synsets is accomplished. it may well be that additional features will need to be introduced while some of the ones proposed for nouns might be dismissed as irrelevant. the proposed strategy is designed for manual mapping, but we plan to develop an automatic system of prompts that will support lexicographers' work. the new system will be an extension of an earlier system of automatic prompts for mapping of noun synsets and based on a modification of the relaxation labelling algorithm of @xcite joined with lemma-pair checking and filtering by a large polish-english cascade dictionary @xcite and translation probabilities from bilingual corpora. as regards future avenues, this study may be continued in a number of possible ways. firstly, the strategy of sense-level mapping described in this paper should be further tested on a structured and balanced sample of concrete and abstract nouns representing the whole variety of semantic domains (lexicographers' files). we plan to extract the lists of polish-english lexical unit pairs from the polish-english pairs of synsets linked by i-synonymy, i-partial synonymy and i-hyponymy (both polish-english and english-polish). the reason for that is that pairs linked by these relations are most likely to yield strong and regular equivalents. we will (proportionally) explore all three possible types of pairing, that is 1-to-1 sense match, 1-to-many sense match and many-to-many sense match. secondly, in order to pinpoint any translation tendencies, the next step should be to calculate translation probabilities for pairs of equivalents, preferably in both directions, extracted from parallel corpora (e.g. paralela). this would enable the verification of the degree to which sense-level mapping is reflected in translated texts found in a parallel corpus. obviously enough, translation probabilities should be interpreted with caution given the limitations of any parallel corpus used (its size, structure, representativeness, balance, scope of annotation, etc.). at this point, it is also important to note that searching through parallel corpora is problematic when one deals with polysemous lexical units. the lack of word-sense disambiguation (or, in other words, semantic tagging of bilingual corpus data) means that when we consult a parallel corpus, we search for language forms rather than senses; that is why translation probabilities should be calculated in a way reflecting polysemy of lexical units. all this should enable one to further test, verify and improve the linking procedure proposed in this paper, which can be useful for anyone interested in applying it for sense-level mapping of wordnets representing languages other than polish and english.