Gábor Bella

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14ranked-venue papers

4as first author

4since 2021 · last 2022

0000-0002-3868-1740ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 12 · 3 first-author · 4 since 2021Databases, data management, data science and information retrieval · 2 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-authorTheory of computation · 1Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence 2 papers		Information extraction and text analysis · 54% Knowledge representation and reasoning · 46%
Interdisciplinary, comprehensive, and emerging computing 1 paper		Computational social science and digital humanities · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Natural language and speech › Information extraction and text analysis › lexical resources lexical resource construction	0.4	1	2019	CogNet: A Large-Scale Cognate Database · ACL (1) 2019
Knowledge, reasoning and agents › Knowledge representation and reasoning › ontology lexical knowledge base	0.3	1	2017	Understanding and Exploiting Language Diversity · IJCAI 2017
Natural language and speech › Information extraction and text analysis lexical semantics	0.3	1	2017	Understanding and Exploiting Language Diversity · IJCAI 2017
Knowledge, reasoning and agents › Knowledge representation and reasoning ontology	0.3	1	2017	Understanding and Exploiting Language Diversity · IJCAI 2017
Computational social science and digital humanities › computational linguistics cognate identification	0.1	1	2019	CogNet: A Large-Scale Cognate Database · ACL (1) 2019
Computational social science and digital humanities historical linguistics	0.1	1	2019	CogNet: A Large-Scale Cognate Database · ACL (1) 2019

Methods — techniques the papers use, named apart from their topics

wordnet-based computation · 0.8

Year	Publication	Venue	Position
2022	Metonymy as a Universal Cognitive Phenomenon: Evidence from Multilingual Lexicons Temuulen Khishigsuren, Gábor Bella, Thomas Brochhagen, Daariimaa Marav, Fausto Giunchiglia, Khuyagbaatar Batsuren	CogSci	2
2022	UniMorph 4.0: Universal Morphology abstract The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation, and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements on several fronts that were made in the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 66 new languages, including 24 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g., missing gender and macrons information. We have amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet. Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa, Nizar Habash, Witold Kieras, Gábor Bella, Brian Leonard, Garrett Nicolai, Kyle Gorman, Yustinus Ghanggo Ate, Maria Ryskina, Sabrina J. Mielke, Elena Budianskaya, Charbel El-Khaissi, Tiago Pimentel, Michael Gasser, William Lane 0002, Mohit Raj, Matt Coler, Jaime Rafael Montoya Samame, Delio Siticonatzi Camaiteri, Esaú Zumaeta Rojas, Didier López Francis, Arturo Oncevay, Juan López Bautista, Gema Celeste Silva Villegas, Lucas Torroba Hennigen, Adam Ek, David Guriel, Peter Dirix, Jean-Philippe Bernardy, Andrey Scherbakov, Aziyana Bayyr-ool, Antonios Anastasopoulos, Roberto Zariquiey, Karina Sheifer, Sofya Ganieva, Hilaria Cruz, Ritván Karahóga, Stella Markantonatou, George Pavlidis, Matvey Plugaryov, Elena Klyachko, Ali Salehi, Candy Angulo, Jatayu Baxi, Andrew Krizhanovsky, Natalia Krizhanovskaya, Elizabeth Salesky, Clara Vania, Sardana Ivanova, Jennifer C. White, Rowan Hall Maudslay, Josef Valvoda, Ran Zmigrod, Paula Czarnowska, Irene Nikkarinen, Aelita Salchak, Brijesh Bhatt, Christopher Straughn, Zoey Liu, Jonathan Washington, Yuval Pinter, Duygu Ataman, Marcin Wolinski, Totok Suhardijanto, Anna Yablonskaya, Niklas Stoehr, Hossep Dolatian, Zahroh Nuriah, Shyam Ratan, Francis M. Tyers, Edoardo Maria Ponti, Grant Aiton, Aryaman Arora, Richard J. Hatcher, Ritesh Kumar 0002, Jeremiah Young, Daria Rodionova, Anastasia Yemelina, Taras Andrushko, Igor Marchenko, Polina Mashkovtseva, Alexandra Serova, Emily Tucker Prud'hommeaux, Maria Nepomniashchaya, Fausto Giunchiglia, Eleanor Chodroff, Mans Hulden, Miikka Silfverberg, Arya McCarthy, David Yarowsky, Ryan Cotterell, Reut Tsarfaty, Ekaterina Vylomova	LREC	6
2022	Using Linguistic Typology to Enrich Multilingual Lexicons: the Case of Lexical Gaps in Kinship abstract This paper describes a method to enrich lexical resources with content relating to linguistic diversity, based on knowledge from the field of lexical typology. We capture the phenomenon of diversity through the notion of lexical gap and use a systematic method to infer gaps semi-automatically on a large scale, which we demonstrate on the kinship domain. The resulting free diversity-aware terminological resource consists of 198 concepts, 1,911 words, and 37,370 gaps in 699 languages. We see great potential in the use of resources such as ours for the improvement of a variety of cross-lingual NLP tasks, which we illustrate through an application in the evaluation of machine translation systems. Temuulen Khishigsuren, Gábor Bella, Khuyagbaatar Batsuren, Abed Alhakim Freihat, Nandu Chandran Nair, Amarsanaa Ganbold, Hadi Khalilia, Yamini Chandrashekar, Fausto Giunchiglia	LREC	2
2022	IndoUKC: A Concept-Centered Indian Multilingual Lexical Resource abstract We introduce the IndoUKC, a new multilingual lexical database comprised of eighteen Indian languages, with a focus on formally capturing words and word meanings specific to Indian languages and cultures. The IndoUKC reuses content from the existing IndoWordNet resource while providing a new model for the cross-lingual mapping of lexical meanings that allows for a richer, diversity-aware representation. Accordingly, beyond a thorough syntactic and semantic cleaning, the IndoWordNet lexical content has been thoroughly remodeled in order to allow a more precise expression of language-specific meaning. The resulting database is made available both for browsing through a graphical web interface and for download through the LiveLanguage data catalogue. Nandu Chandran Nair, Rajendran Sankara Velayuthan, Yamini Chandrashekar, Gábor Bella, Fausto Giunchiglia	LREC	4
2020	Exploring the Language of Data abstract We set out to uncover the unique grammatical properties of an important yet so far underresearched type of natural language text: that of short labels typically found within structured datasets.We show that such labels obey a specific type of abbreviated grammar that we call the Language of Data, with properties significantly different from the kinds of text typically addressed in computational linguistics and NLP, such as 'standard' written language or social media messages.We analyse orthography, parts of speech, and syntax over a large, bilingual, hand-annotated corpus of data labels collected from a variety of domains.We perform experiments on tokenisation, part-of-speech tagging, and named entity recognition over real-world structured data, demonstrating that models adapted to the Language of Data outperform those trained on standard text.These observations point in a new direction to be explored as future research, in order to develop new NLP tools and models dedicated to the Language of Data. Gábor Bella, Linda Gremes, Fausto Giunchiglia	COLING	1
2020	Cross-Border Medical Research Using Multi-Layered and Distributed Knowledge abstract As medical research becomes ever finer-grained, experiments require healthcare data in quantities that single countries cannot provide.Cross-jurisdictional data collection remains, however, extremely challenging due to the diverging legal, professional, linguistic, normative, and technological contexts of the participating countries.Medical data heterogeneity, in particular, is still a largely unsolved problem on the international level, due to the complexity of data combined with strict precision and data protection constraints.We propose a scalable solution based on a novel knowledge architecture and the corresponding knowledge graph integration methodology.Medical knowledge that drives the scalable integration process is divided into multiple functional layers and is maintained in a distributed manner across participating countries.We successfully applied the approach in the context of a research experiment across Scotland and Italy, and are currently adapting it within other initiatives of Europe-wide health data interoperability. Using Multi-Layered and Distributed Knowledge Gábor Bella, Liz Elliot, Subhashis Das, Stephen Pavis, Ettore Turra, David Stuart Robertson 0001, Fausto Giunchiglia	ECAI	1
2020	Ontology-Driven Cross-Domain Transfer Learning abstract The aim of transfer learning is to reuse learnt knowledge across different contexts. In the particular case of cross-domain transfer (also known as domain adaptation), reuse happens across different but related knowledge domains. While there have been promising first results in combining learning with symbolic knowledge to improve cross-domain transfer results, the singular ability of ontologies for providing classificatory knowledge has not been fully exploited so far by the machine learning community. We show that ontologies, if properly designed, are able to support transfer learning by improving generalization and discrimination across classes. We propose an architecture based on direct attribute prediction for combining ontologies with a transfer learning framework, as well as an ontology-based solution for cross-domain generalization based on the integration of top-level and domain ontologies. We validate the solution on an experiment over an image classification task, demonstrating the system’s improved classification performance. Mattia Fumagalli, Gábor Bella, Samuele Conti, Fausto Giunchiglia	FOIS	2
2020	A Major Wordnet for a Minority Language: Scottish Gaelic abstract We present a new wordnet resource for Scottish Gaelic, a Celtic minority language spoken by about 60,000 speakers, most of whom live in Northwestern Scotland. The wordnet contains over 15 thousand word senses and was constructed by merging ten thousand new, high-quality translations, provided and validated by language experts, with an existing wordnet derived from Wiktionary. This new, considerably extended wordnet—currently among the 30 largest in the world—targets multiple communities: language speakers and learners; linguists; computer scientists solving problems related to natural language processing. By publishing it as a freely downloadable resource, we hope to contribute to the long-term preservation of Scottish Gaelic as a living language, both offline and on the Web. Gábor Bella, Fiona McNeill, Rody Gorman, Caoimhin O. Donnaile, Kirsty MacDonald, Yamini Chandrashekar, Abed Alhakim Freihat, Fausto Giunchiglia	LREC	1
2019	CogNet: A Large-Scale Cognate Database abstract This paper introduces CogNet, a new, large-scale lexical database that provides cognates-words of common origin and meaning-across languages.The database currently contains 3.1 million cognate pairs across 338 languages using 35 writing systems.The paper also describes the automated method by which cognates were computed from publicly available wordnets, with an accuracy evaluated to 94%.Finally, statistics and early insights about the cognate data are presented, hinting at a possible future exploitation of the resource 1 by various fields of lingustics. Khuyagbaatar Batsuren, Gábor Bella, Fausto Giunchiglia	ACL (1)	2
2019	Towards Understanding Classification and Identification Mattia Fumagalli, Gábor Bella, Fausto Giunchiglia	PRICAI (1)	2
2018	Using Crowd Agreement for Wordnet Localization Amarsanaa Ganbold, Altangerel Chagnaa, Gábor Bella	LREC	3
2017	Understanding and Exploiting Language Diversity abstract The main goal of this paper is to describe a general approach to the problem of understanding linguistic phenomena, as they appear in lexical semantics, through the analysis of large scale resources, while exploiting these results to improve the quality of the resources themselves. The main contributions are: the approach itself, a formal quantitative measure of language diversity; a set of formal quantitative measures of resource incompleteness and a large scale resource, called the Universal Knowledge Core (UKC) built following the methodology proposed. As a concrete example of an application, we provide an algorithm for distinguishing polysemes from homonyms, as stored in the UKC. Fausto Giunchiglia, Khuyagbaatar Batsuren, Gábor Bella	IJCAI	3
2017	Language and domain aware lightweight ontology matching Gábor Bella, Fausto Giunchiglia, Fiona McNeill	J. Web Semant.	1
2005	Injecting information into atomic units of text abstract This paper presents a new approach to text processing, based on textemes. These are atomic text units generalising the concepts of character and glyph by merging them in a common data structure, together with an arbitrary number of user-defined properties. In the first part, we give a survey of the notions of character and glyph and their relation with Natural Language Processing models, some visual text representation issues and strategies adopted by file formats (SVG, PDF, DVI) and software (Uniscribe, Pango). In the second part we show applications of textemes in various text processing issues: ligatures, variant glyphs and other OpenType-related properties, hyphenation, color and other presentation attributes, Arabic form and morphology, CJK spacing, metadata, etc. Finally we describe how the Omega typesetting system implements texteme processing as an example of a generalised approach to input character stream parsing, internal representation of text, and modular typographic transformations. In the data flow from input to output, whether in memory or through serializations in auxiliary data files, textemes progressively accumulate information that is used by Omega’s paragraph builder engine and included in the output DVI file. We show how this additional information increases efficiency of conversions to other file formats such as PDF or SVG. We conclude this paper by presenting interesting potential applications of texteme methods in document engineering. Yannis Haralambous, Gábor Bella	ACM Symposium on Document Engineering	2