W ord N et- QU : Development of a Lexical Database for Q uechua Varieties (2022)

Abstract: In the effort to minimize the risk of extinction of a language, linguistic resources are fundamental. Quechua, a low-resource language from South America, is a language spoken by millions but, despite several efforts in the past, still lacks the resources necessary to build high-performance computational systems. In this article, we present WordNet-QU which signifies the inclusion of Quechua in a well-known lexical database called wordnet. We propose WordNet-QU to be included as an extension to wordnet after demonstrating a manually-curated collection of multiple digital resources for lexical use in Quechua. Our work uses the synset alignment algorithm to compare Quechua to its geographically nearest high-resource language, Spanish. Altogether, we propose a total of 28,582 unique synset IDs divided according to region like so: 20510 for Southern Quechua, 5993 for Central Quechua, 1121 for Northern Quechua, and 958 for Amazonian Quechua.

## introduction and related work lexical databases and resources have been used in the past for various natural language processing (nlp) tasks ranging from information retrieval (ir) to machine translation (mt). while many recent nlp approaches rely on deep learning techniques like transformers, namely bert @xcite , where attention @xcite is used to create a semantic representation of text, more traditional approaches relied on purely linguistic and syntactic features. more often than not, recent deep-learning approaches require a large amount of data to perform better than traditional ones (e.g. on the order of millions of words for machine translation @xcite ). this makes nlp approaches with low-resource languages, languages that are measured in the thousands typically, much more difficult to solve with recent approaches thus forc-ing the use of traditional approaches to solve problems. one low-resource language from south america, called quechua, is spoken by nearly 8 million people foot_0 yet still does not have enough resources to effectively compete with other high-resource languages as has been shown in previous research @xcite @xcite . oftentimes, due to insufficient resources, scores such as bleu @xcite and accuracy are more than three times lower. this lack of resources thus drives the need for traditional techniques such as the use of lexical databases, grammars, and other linguistic cues such as tree banks and more. one such resource that has been commonly used for traditional approaches is called wordnet @xcite which was originally created in the 1990s yet is still used today, especially for low-resource languages like quechua. the need to build digital resources is greater for endangered languages like quechua and others since there is a clear desire to save the language from extinction. however, the desire is typically not supported by those agencies that are responsible for its survival. berment @xcite and others have expressed the need for further analysis and research stating that the current effort "may be insufficient to aid preservation efforts". in this work, we provide several lexical resources for quechua to increase its inclusion in wordnet @xcite . we call the collection of resources wordnet-qu which corresponds to its commonly-used languagepair symbol (qu) found in most corpora for nlp in quechua. to elaborate on its inclusion, in section 2 we provide details on how the corpus was compiled and the annotations done. then, in section 3, we cover the wordnet implementation of the corpus. finally, in section 4 we provide insight into our future downstream tasks. ## corpus details the corpus presented here made available publicly foot_1 has been created using a manually curated collection of dictionaries. the dictionaries were mostly gotten from ministry of education in peru (minedu in in order to organize the dictionaries into a format that can be used by wordnet @xcite , the corpus is structured in a format that consists of the following labels: (i) branch, (ii) variety, (iii) region, (iv) author, (v) dictionary, (vi) year, (vii) lexical entry, (viii) grammatical category, (ix) glossary entry, (x) quechua definition, (xi) quechua synonym, (xii) spanish synonym and (xiii) notes or clarifications. an example of the original dictionary as found from the ministry of education is seen in figure 1 . since there are several dialects of quechua spoken in peru (cerrón-palomino, 2021), it was important to compile the corpus by variety or region. in order to better illustrate the differences in parts of speech for dialects, we break each region's dialect into the following categories: noun, adjective, adverb and verb as shown in ## methodology in order to use and distribute wordnet-qu we had to make it compatible with wordnet @xcite . constructing a wordnet, whether from scratch or by expanding a previous one, is a labor intensive process that requires several steps and extensive use of both human labor and automated systems. since the creation of the first wordnet (princeton wordnet (pwn)) in 1995 @xcite , many other wordnets have been created for several languages. for example eurowordnet (ewn) is a multilingual wordnet project that links wordnets of multiple european languages (english, dutch, italian, spanish, german, french, czech and estonian) @xcite . in ewn, wordnets were created for each language separately and then linked through an index based on pwn. in the same way, balkanet is a multilingual wordnet project consisting of six balkan languages (bulgarian, czech, greek, romanian, serbian, and turkish). @xcite two of the most-commonly used approaches for creating a wordnet are based on what are known as the expand and merge approaches. both approaches use synsets -groups of synonyms that express the same concept in wordnet. one synset can have multiple words and one word can have multiple synsets. in the expand approach, a set of synsets from pwn, including their semantic database, are first translated into the target language and then relations are transferred from english and checked in a manual fashion as is done for scottish gaelic @xcite and the french @xcite . the merge approach builds bilingual relations from scratch, without any links to english, the main language for wordnet. both the polish wordnet @xcite and norwegian wordnet @xcite use the merge approach. model size spearman pre-trained model (wiki) 29k 0.35 wordnet-qu (wiki + wordnet corpus) 31k 0.61 our implementation is based on a few steps. the first step is to construct a wordnet for spanish because translations for quechua are more available in the high-resource language (spanish) in peru. using the main wordnet in english, we create a spanish wordnet using the expansion technique described above based on similarity alone. the abundance of on-line spanish glossaries and other relationships helped when creating the spanish wordnet. once translated, the spanish wordnet became what is known as our multi-lingual central repository (mcr) for quechua. this, in turn, facilitates the next steps which are to create and align synsets to with their corresponding concept which is validated manually by a human.