Connecting Multilingual Wordnets: Strategies for Improving ILI Classification in O de N et (2023)

Abstract: The Open Multilingual Wordnet (OMW) is an open source project that was launched with the goal to make it easy to use wordnets in multiple languages without having to pay expensive proprietary licensing costs. As OMW evolved, the interlingual indicator (ILI)1 was used to allow semantically equivalent synsets in different languages to be linked to each other. OdeNet2 is the German language wordnet which forms part of the OMW project. This paper analyses the shortcomings of the initial ILI classification in OdeNet and the consequent methods used to improve this classification.

## introduction a wordnet is a lexical database of semantic relationships between words in a specific language. the first wordnet was created for the english language at princeton university (also known as the princeton wordnet, @xcite ). as the usefulness of wordnets as lexical resources became apparent, the princeton wordnet (pwn) was expanded and some wordnets were constructed from scratch in other languages. the princeton wordnet is distributed in electronic format as part of nltk (natural language processing toolkit) and can be accessed with a corresponding python library 3 . nltk offers translations for synsets (groupings of synonyms) in various languages, although these translations are incomplete; meaning that not every synset in english has an equivalent translation in another language. there are also wordnets in other languages which were developed completely independent of the pwn, such as germanet @xcite . many of these wordnets contain high quality data 1 the next version was called cili (collaborative interlingual index), https://www.luismc.com/omw/ili 2 https://github.com/ hdasprachtechnologie/odenet 3 https://www.nltk.org which were constructed manually in a resourceintensive and time-consuming manner. therefore, these wordnets are commercially licensed and not free to use, except for research and teaching. an example of a large wordnet built independently from pwn and available on open-source licence is plwordnet @xcite . omw is an open source project that was launched with the goal to make it easy to use wordnets in multiple languages with cc-by-sa-4.0 opensource licenses that include commercial and private use @xcite . omw has the added benefit of connecting equivalent synsets in different languages by means of the ili @xcite . the english version of omw called @xcite is basically a copy of the pwn with some enhancements and additions, most notably the addition of an ili for each synset. many of the omw wordnets in other languages were developed by using the already existing translations in nltk. these translations were extracted and packaged into new wordnets. consequently, the equivalent synsets in the resulting wordnets were linked to each other via the ili. goodman and @xcite developed the wn python library that can be used to access the wordnets that form part of the omw project. in listing 1 we see how the translated lemmas of a synset in pwn can be accessed with nltk python library. listing 2 on the other hand shows how to access these same synsets through the ili or by searching directly for it in the other language. though nltk offers translations in many languages, german is so far not included. this means that a german wordnet for omw could not easily be constructed with the exiting nltk translations as a base, as was the case with many of the other languages. therefore, an initiative was launched to create an open source german wordnet (odenet) which could form part of the omw project. odenet was constructed from open source linguistic resources in combination with some manual and semi-manual corrections. since odenet was constructed independently of existing resources in nltk, it was not as easy to connect equivalent synsets in omw via ili. as an initial implementation, google translate foot_0 was used in combination with statistical methods as described by @xcite . however, this implementation has some shortcomings, including: ## problem description a significant problem in using machine translation to connect equivalent synsets in different languages occurs, when translating homographs (words with similar spelling but different meanings) and polysemes. this is particularly noticeable when a word translated from a source language is a homograph or polyseme in the target language. as an example, we take the german word unterlegscheibe from odenet. the corresponding english translation is washer. searching for washer in ewn, we find three synsets containing the word: our aim is to select the correct synset in ewn so that we can take the corresponding ili and assign it to the synset in german. for somebody with knowledge of german, it is evident that the second synset in the list is the correct corresponding synset in ewn (i.e. we want to take the ili from this synset and also use it in the corresponding odenet synset). it is difficult to do this assignment automatically, because of the missing context. the usage of machine translation with google translate together with some statistical methods in the current odenet implementation @xcite also resulted in many of the synsets having duplicate ilis, because the assignment of ilis to synsets in odenet was not restricted to one ili per synset. an example: the synsets odenet-4330-n (anzahl, zahl) and odenet-688-n (summe, gesamtmenge) both referred to i35594 (measure, amount, quantity). furthermore, @xcite used automatic methods for assigning the correct pos to synsets. however, they were only able to assign the correct pos to synsets in 93% of the cases. often, multi-word lexemes were involved in problematic cases, as for example postmortal, nach dem tod, post mortem was categorized as pos "n", although it is pos "a". ## results the most noticeable difference after applying the algorithm is the complete elimination of duplicate ilis. the number of synsets with unique ilis has increased to 19,547 and all duplicate ilis have been removed. the algorithm identified 361 synsets with possible pos errors. after manual evaluation, 325 of these synsets indeed ended up having a wrong pos. this means a successful identification of 90% of synsets with the wrong pos. of the 36 false positives, most proposed an adjective for a noun or a verb and in many cases colloquial language was involved, such as in the case of odenet-19938-n (tüftelei, getüftel). ## concluding remarks odenet is an open-source wordnet that was automatically compiled from an open thesaurus and connected to the multilingual wordnets in the omw initiative by machine-translating synsets. the result of the machine translation was partly incorrect because the translation context was missing. further, duplicate interlingual indicators (ilis) were assigned in odenet. additionally, there was a need to correct the automatically assigned pos. in this paper, we described a solution for these problems by matching ilis to odenet synsets, taking the english definitions into account. the results have shown that the algorithm is very effective in reducing duplication and improving the correctness of ili classification. the algorithm can potentially be improved by providing the ili classification weight function, as described in section 3.2, with more context information. at the moment, we use synonyms to provide context, and these synonyms could be augmented with the hypernyms of the candidate synsets under evaluation. this should lead to higher classification accuracy, but is left for future research. although this algorithm was applied to improve the ili classification for odenet, it can be used for any other language in theory. the success of the resulting classification will be dependent on factors such as how synonym-rich the language is and also how good the machine translation support is. with some minor adjustments to the algorithm, we propose that it will also be possible to connect other lexical resources using the proposed method. for example, two thesauri, developed in two different languages independently of each other, could be merged into a bilingual resource. since languages evolve independently of each other, it often happens that not all words in one language have a perfect equivalent in another language. it can happen that some semantic meaning is lost or added in the translation process. even though you will mostly get the best possible match by applying an algorithm such as described in this paper, there can still be an extent of fuzziness or loss/addition of meaning. currently, the omw framework is modelled in such as way that a synset in one language can map to a single synset in another language via the ili. this structure makes it difficult to model fuzzy matching or loss/addition of semantic meaning. this topic may be of interest for future research.