In recent years, the use of synthetic data, either as a complement or a substitute for original data, has emerged as a solution to challenges such as data scarcity and security risks. This paper is an initial attempt to automatically generate such data for Information Extraction tasks. We accomplished this by developing a novel synthetic data generation framework called KGAST, which leverages Knowledge Graphs and Large Language Models. In our preliminary study, we conducted simple experiments to generate synthetic versions of two datasets—a French security defense dataset and an English general domain dataset, after which we evaluated them both intrinsically and extrinsically. The results indicated that synthetic data can effectively complement original data, improving the performance of models on classes with limited training samples. This highlights KGAST’s potential as a tool for generating synthetic data for Information Extraction tasks.

OntoLex, the dominant community standard for machine-readable lexical resources in the context of RDF, Linked Data and Semantic Web technologies, is currently extended with a designated module for Frequency, Attestations and Corpus-based Information (OntoLex-FrAC). We propose a novel component for OntoLex-FrAC, addressing the incorporation of corpus queries for (a) linking dictionaries with corpus engines, (b) enabling RDF-based web services to exchange corpus queries and responses data dynamically, and (c) using conventional query languages to formalize the internal structure of collocations, word sketches, and colligations. The primary field of application of the query extension is in digital lexicography and corpus linguistics, and we present a proof-of-principle implementation in backend components of a novel platform designed to support digital lexicography for the Serbian language.

With advances in the field of Linked (Open) Data (LOD), language data on the LOD cloud has grown in number, size, and variety. With an increased volume and variety of language data, optimizations of methods for distributing, storing, and querying these data become more central. To this end, this position paper investigates use cases at the intersection of LLOD and Big Data, existing approaches to utilizing Big Data techniques within the context of linked data, and discusses the challenges and benefits of this union.

Understanding the relation between the meanings of words is an important part of comprehending natural language. Prior work has either focused on analysing lexical semantic relations in word embeddings or probing pretrained language models (PLMs), with some exceptions. Given the rarity of highly multilingual benchmarks, it is unclear to what extent PLMs capture relational knowledge and are able to transfer it across languages. To start addressing this question, we propose MultiLexBATS, a multilingual parallel dataset of lexical semantic relations adapted from BATS in 15 languages including low-resource languages, such as Bambara, Lithuanian, and Albanian. As experiment on cross-lingual transfer of relational knowledge, we test the PLMs’ ability to (1) capture analogies across languages, and (2) predict translation targets. We find considerable differences across relation types and languages with a clear preference for hypernymy and antonymy as well as romance languages.

In this article we look at how two different standards for lexical resources, TEI and OntoLex, deal with corpus citations in lexicons. We will focus on how corpus citations in retrodigitised dictionaries can be modelled using each of the two standards since this provides us with a suitably challenging use case. After looking at the structure of an example entry from a legacy dictionary, we examine the two approaches offered by the two different standards by outlining an encoding for the example entry using both of them (note that this article features the first extended discussion of how the Frequency Attestation and Corpus (FrAC) module of OntoLex deals with citations). After comparing the two approaches and looking at the advantages and disadvantages of both, we argue for a combination of both. In the last part of the article we discuss different ways of doing this, giving our preference for a strategy which makes use of RDFa.

We report on work in progress dealing with the automated generation of pronunciation information for English multiword terms (MWTs) in Wiktionary, combining information available for their single components. We describe the issues we were encountering, the building of an evaluation dataset, and our teaming with the DBnary resource maintainer. Our approach shows potential for automatically adding morphosyntactic and semantic information to the components of such MWTs.

In this paper, we describe the application of Linguistic Linked Open Data (LLOD) technology for dynamic cross-lingual querying on demand. Whereas most related research is focusing on providing a static linking, i.e., cross-lingual inference, and then storing the resulting links, we demonstrate the application of the federation capabilities of SPARQL to perform lexical linking on the fly. In the end, we provide a baseline functionality that uses the connection of two web services – a SPARQL end point for multilingual lexical data and another SPARQL end point for querying an English language knowledge graph – in order to perform querying an English language knowledge graph using foreign language labels. We argue that, for low-resource languages where substantial native knowledge graphs are lacking, this functionality can be used to lower the language barrier by allowing to formulate cross-linguistically applicable queries mediated by a multilingual dictionary.

In this paper, we provide an overview of current technologies for cross-lingual link discovery, and we discuss challenges, experiences and prospects of their application to under-resourced languages. We rst introduce the goals of cross-lingual linking and associated technologies, and in particular, the role that the Linked Data paradigm (Bizer et al., 2011) applied to language data can play in this context. We de ne under-resourced languages with a speci c focus on languages actively used on the internet, i.e., languages with a digitally versatile speaker community, but limited support in terms of language technology. We argue that languages for which considerable amounts of textual data and (at least) a bilingual word list are available, techniques for cross-lingual linking can be readily applied, and that these enable the implementation of downstream applications for under-resourced languages via the localisation and adaptation of existing technologies and resources.

Contributive resources, such as wikipedia, have proved to be valuable in Natural Language Processing or Multilingual Information Retrieval applications. This article focusses on Wiktionary, the dictionary part of the collaborative resources sponsored by the Wikimedia foundation. In this article we present a word net that has been extracted from French, English and German wiktionaries. We present the structure of this word net and discuss the specific extraction problems induced by this kind of contributive resources and the method used to overcome them. Then we show how we represent the extracted data as a Lexical Markup Framework (LMF) compatible lexical network represented in Resource Description Framework (RDF) format.

Cet article présente l’utilisation de « Jibiki » (la plateforme de développement du serveur Web Papillon) dans le cadre du projet LexALP1. Le but de ce projet est d’harmoniser la terminologie des quatre langues (français, allemand, italien et slovène) de la Convention Alpine2 de sorte que les états membres puissent coopérer efficacement. Pour cela, le projet utilise la plateforme Jibiki afin de construire une banque terminologique permettant de comparer la terminologie spécialisée de sept systèmes légaux dans quatre langues, et de l’harmoniser, optimisant ainsi la compréhension entre les états alpins sur des questions environnementales au niveau supranational. Dans cet article, nous présentons comment peut être employée la plateforme générique Jibiki afin de gérer un dictionnaire particulier.

We present the architecture of the UNL-French deconverter, which "generates" from the UNL interlingua by first "localizing" the UNL form for French, within UNL, and then applying slightly adapted but classical transfer and generation techniques, implemented in GETA's Ariane-G5 environment, supplemented by some UNL-specific tools. Online interaction can be used during deconversion to enhance output quality and is now used for development purposes. We show how interaction could be delayed and embedded in the postedition phase, which would then interact not directly with the output text, but indirectly with several components of the deconverter. Interacting online or offline can improve the quality not only of the utterance at hand, but also of the utterances processed later, as various preferences may be automatically changed to let the deconverter "learn".