: Latvian WordNet is a resource where word senses are connected based on their semantic relationships. The manual construction of a high-quality core Latvian WordNet is currently underway. However, text processing tasks require broad coverage, therefore, this work aims to extend the wordnet by automatically linking additional word senses in the Latvian online dictionary T¯ezaurs.lv and aligning them to the English-language Princeton WordNet (PWN). Our method only needs translation data, sense definitions and usage examples to compare it to PWN using pretrained word embeddings and sBERT. As a result, 57 927 interlanguage links were found that can potentially be added to Latvian WordNet, with an accuracy of 80% for nouns, 56% for verbs, 67% for adjectives and 66% for adverbs.
Arabic language lacks semantic datasets and sense inventories. The most common semantically-labeled dataset for Arabic is the ArabGlossBERT, a relatively small dataset that consists of 167K context-gloss pairs (about 60K positive and 107K negative pairs), collected from Arabic dictionaries. This paper presents an enrichment to the ArabGlossBERT dataset, by augmenting it using (Arabic-English-Arabic) machine back-translation. Augmentation increased the dataset size to 352K pairs (149K positive and 203K negative pairs). We measure the impact of augmentation using different data configurations to fine-tune BERT on target sense verification (TSV) task. Overall, the accuracy ranges between 78% to 84% for different data configurations. Although our approach performed at par with the baseline, we did observe some improvements for some POS tags in some experiments. Furthermore, our fine-tuned models are trained on a larger dataset covering larger vocabulary and contexts. We provide an in-depth analysis of the accuracy for each part-of-speech (POS).
In this paper we describe a new methodology to expand the Abui Wordnet through data collected using the Rapid Word Collection (RWC) method – based on SIL’s Semantic Domains. Using a multilingual sense-intersection algorithm, we created a ranked list of concept suggestions for each domain, and then used the ranked list as a filter to link the Abui RWC data to wordnet. This used translations from both SIL’s Semantic Domain’s structure and example words, both available through SIL’s Fieldworks software and the RWC project. We release both the new mapping of the SIL Semantic Domains to wordnet and an expansion of the Abui Wordnet.
Recent advances in Word Sense Disambiguation suggest neural language models can be successfully improved by incorporating knowledge base structure. Such class of models are called hybrid solutions. We propose a method of improving hybrid WSD models by harnessing data augmentation techniques and bilingual training. The data augmentation consist of structure augmentation using interlingual connections between wordnets and text data augmentation based on multilingual glosses and usage examples. We utilise language-agnostic neural model trained both with SemCor and Princeton WordNet gloss and example corpora, as well as with Polish WordNet glosses and usage examples. This augmentation technique proves to make well-known hybrid WSD architecture to be competitive, when compared to current State-of-the-Art models, even more complex.
GermaNet is a large lexical-semantic net that relates German nouns, verbs, and adjectives semantically. The word net has been manually constructed over the last 25 years and hence presents a high-quality, valuable resource for German. While GermaNet is maintained in a Postgres database, all its content can be exported as an XML-based serialisation. Recently, this XML representation has been converted into RDF, largely by staying close to GermaNet’s principle of arrangement where lexunits that share the same meaning are grouped together into so-called synsets. With each lexical unit and synset now globally addressable via a unique resource identifier, it has become much easier to link together GermaNet entries with other lexical and semantic resources. In terms of semantic interoperability, however, the RDF variant of GermaNet leaves much to be desired. In this paper, we describe yet another conversion from GermaNet’s XML representation to RDF. The new conversion makes use of the OntoLex-Lemon ontology, and therefore, presents a decisive step toward a GermaNet representation with a much higher level of semantic interoperability, and which makes it possible to use GermaNet with other wordnets that already support this conceptualisation of lexica.
