Search Results for author: Fausto Giunchiglia

Found 33 papers, 10 papers with code

Is this Enough?-Evaluation of Malayalam Wordnet

no code implementations EACL (DravidianLangTech) 2021 Nandu Chandran Nair, Maria-chiara Giangregorio, Fausto Giunchiglia

Quality of a product is the degree to which a product meets the customer’s expectation, which must also be valid for the case of lexical semantic resources.

ZiNet: Linking Chinese Characters Spanning Three Thousand Years

1 code implementation Findings (ACL) 2022 Yang Chi, Fausto Giunchiglia, Daqian Shi, Xiaolei Diao, Chuntao Li, Hao Xu

In addition, powered by the knowledge of radical systems in ZiNet, this paper introduces glyph similarity measurement between ancient Chinese characters, which could capture similar glyph pairs that are potentially related in origins or semantics.

A Taxonomic Classification of WordNet Polysemy Types

no code implementations GWC 2016 Abed Alhakim Freihat, Fausto Giunchiglia, Biswanath Dutta

WordNet represents polysemous terms by capturing the different meanings of these terms at the lexical level, but without giving emphasis on the polysemy types such terms belong to.


The SIGMORPHON 2022 Shared Task on Morpheme Segmentation

1 code implementation15 Jun 2022 Khuyagbaatar Batsuren, Gábor Bella, Aryaman Arora, Viktor Martinović, Kyle Gorman, Zdeněk Žabokrtský, Amarsanaa Ganbold, Šárka Dohnalová, Magda Ševčíková, Kateřina Pelegrinová, Fausto Giunchiglia, Ryan Cotterell, Ekaterina Vylomova

The SIGMORPHON 2022 shared task on morpheme segmentation challenged systems to decompose a word into a sequence of morphemes and covered most types of morphology: compounds, derivations, and inflections.

Concept-level Debugging of Part-Prototype Networks

1 code implementation31 May 2022 Andrea Bontempelli, Stefano Teso, Fausto Giunchiglia, Andrea Passerini

Part-prototype Networks (ProtoPNets) are concept-based classifiers designed to achieve the same performance as black-box models without compromising transparency.

UniMorph 4.0: Universal Morphology

no code implementations7 May 2022 Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa, Nizar Habash, Witold Kieraś, 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, Mohit Raj, Matt Coler, Jaime Rafael Montoya Samame, Delio Siticonatzi Camaiteri, Benoît Sagot, 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 Karahóǧa, 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 White, Rowan Hall Maudslay, Josef Valvoda, Ran Zmigrod, Paula Czarnowska, Irene Nikkarinen, Aelita Salchak, Brijesh Bhatt, Christopher Straughn, Zoey Liu, Jonathan North Washington, Yuval Pinter, Duygu Ataman, Marcin Wolinski, Totok Suhardijanto, Anna Yablonskaya, Niklas Stoehr, Hossep Dolatian, Zahroh Nuriah, Shyam Ratan, Francis M. Tyers, Edoardo M. Ponti, Grant Aiton, Aryaman Arora, Richard J. Hatcher, Ritesh Kumar, Jeremiah Young, Daria Rodionova, Anastasia Yemelina, Taras Andrushko, Igor Marchenko, Polina Mashkovtseva, Alexandra Serova, Emily Prud'hommeaux, Maria Nepomniashchaya, Fausto Giunchiglia, Eleanor Chodroff, Mans Hulden, Miikka Silfverberg, Arya D. McCarthy, David Yarowsky, Ryan Cotterell, Reut Tsarfaty, Ekaterina Vylomova

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.

Morphological Inflection

Using Linguistic Typology to Enrich Multilingual Lexicons: the Case of Lexical Gaps in Kinship

1 code implementation11 Apr 2022 Temuulen Khishigsuren, Gábor Bella, Khuyagbaatar Batsuren, Abed Alhakim Freihat, Nandu Chandran Nair, Amarsanaa Ganbold, Hadi Khalilia, Yamini Chandrashekar, Fausto Giunchiglia

We capture the phenomenon of diversity through the notions of lexical gap and language-specific word and use a systematic method to infer gaps semi-automatically on a large scale.

Machine Translation Translation

Language Diversity: Visible to Humans, Exploitable by Machines

no code implementations ACL 2022 Gábor Bella, Erdenebileg Byambadorj, Yamini Chandrashekar, Khuyagbaatar Batsuren, Danish Ashgar Cheema, Fausto Giunchiglia

The Universal Knowledge Core (UKC) is a large multilingual lexical database with a focus on language diversity and covering over a thousand languages.

Visual Ground Truth Construction as Faceted Classification

no code implementations17 Feb 2022 Fausto Giunchiglia, Mayukh Bagchi, Xiaolei Diao

Recent work in Machine Learning and Computer Vision has provided evidence of systematic design flaws in the development of major object recognition benchmark datasets.

Classification Object Recognition

Object Recognition as Classification via Visual Properties

no code implementations20 Dec 2021 Fausto Giunchiglia, Mayukh Bagchi

We base our work on the teleosemantic modelling of concepts as abilities implementing the distinct functions of recognition and classification.

Classification Object Recognition

Toward a Unified Framework for Debugging Concept-based Models

no code implementations23 Sep 2021 Andrea Bontempelli, Fausto Giunchiglia, Andrea Passerini, Stefano Teso

In this paper, we tackle interactive debugging of "gray-box" concept-based models (CBMs).

Streaming and Learning the Personal Context

no code implementations18 Aug 2021 Fausto Giunchiglia, Marcelo Rodas Britez, Andrea Bontempelli, Xiaoyue Li

The representation of the personal context is complex and essential to improve the help machines can give to humans for making sense of the world, and the help humans can give to machines to improve their efficiency.

Interactive Label Cleaning with Example-based Explanations

1 code implementation NeurIPS 2021 Stefano Teso, Andrea Bontempelli, Fausto Giunchiglia, Andrea Passerini

We tackle sequential learning under label noise in applications where a human supervisor can be queried to relabel suspicious examples.

Classifying concepts via visual properties

no code implementations19 May 2021 Fausto Giunchiglia, Mayukh Bagchi

We assume that substances in the world are represented by two types of concepts, namely substance concepts and classification concepts, the former instrumental to (visual) perception, the latter to (language based) classification.


iTelos -- Purpose Driven Knowledge Graph Generation

no code implementations19 May 2021 Fausto Giunchiglia, Simone Bocca, Mattia Fumagalli, Mayukh Bagchi, Alessio Zamboni

When building a new application we are more and more confronted with the need of reusing and integrating pre-existing knowledge, e. g., ontologies, schemas, data of any kind, from multiple sources.

Graph Generation Knowledge Graphs

Stratified Data Integration

no code implementations19 May 2021 Fausto Giunchiglia, Alessio Zamboni, Mayukh Bagchi, Simone Bocca

We propose a novel approach to the problem of semantic heterogeneity where data are organized into a set of stratified and independent representation layers, namely: conceptual(where a set of unique alinguistic identifiers are connected inside a graph codifying their meaning), language(where sets of synonyms, possibly from multiple languages, annotate concepts), knowledge(in the form of a graph where nodes are entity types and links are properties), and data(in the form of a graph of entities populating the previous knowledge graph).

Towards Visual Semantics

no code implementations26 Apr 2021 Fausto Giunchiglia, Luca Erculiani, Andrea Passerini

In this paper we provide a theory and an algorithm for how to build substance concepts which are in a one-to-one correspondence with classifications concepts, thus paving the way to the seamless integration between natural language descriptions and visual perception.

General Classification

Towards Algorithmic Transparency: A Diversity Perspective

no code implementations12 Apr 2021 Fausto Giunchiglia, Jahna Otterbacher, Styliani Kleanthous, Khuyagbaatar Batsuren, Veronika Bogin, Tsvi Kuflik, Avital Shulner Tal

As the role of algorithmic systems and processes increases in society, so does the risk of bias, which can result in discrimination against individuals and social groups.


Topological Regularization for Graph Neural Networks Augmentation

no code implementations3 Apr 2021 Rui Song, Fausto Giunchiglia, Ke Zhao, Hao Xu

The complexity and non-Euclidean structure of graph data hinder the development of data augmentation methods similar to those in computer vision.

Data Augmentation Representation Learning

Human-in-the-loop Handling of Knowledge Drift

1 code implementation27 Mar 2021 Andrea Bontempelli, Fausto Giunchiglia, Andrea Passerini, Stefano Teso

Motivated by this, we introduce TRCKD, a novel approach that combines automated drift detection and adaptation with an interactive stage in which the user is asked to disambiguate between different kinds of KD.

Exploring the Language of Data

no code implementations COLING 2020 G{\'a}bor Bella, Linda Gremes, Fausto Giunchiglia

We set out to uncover the unique grammatical properties of an important yet so far under-researched type of natural language text: that of short labels typically found within structured datasets.

named-entity-recognition Named Entity Recognition +1

Multi-Modal Subjective Context Modelling and Recognition

no code implementations19 Nov 2020 Qiang Shen, Stefano Teso, Wanyi Zhang, Hao Xu, Fausto Giunchiglia

Second, existing models typically assume that context is objective, whereas in most applications context is best viewed from the user's perspective.

Learning in the Wild with Incremental Skeptical Gaussian Processes

1 code implementation2 Nov 2020 Andrea Bontempelli, Stefano Teso, Fausto Giunchiglia, Andrea Passerini

The ability to learn from human supervision is fundamental for personal assistants and other interactive applications of AI.

Gaussian Processes

A Major Wordnet for a Minority Language: Scottish Gaelic

no code implementations LREC 2020 G{\'a}bor Bella, Fiona McNeill, Rody Gorman, Caoimhin O Donnaile, Kirsty MacDonald, Ch, Yamini rashekar, Abed Alhakim Freihat, Fausto Giunchiglia

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.

Natural Language Processing

Continual egocentric object recognition

1 code implementation6 Dec 2019 Luca Erculiani, Fausto Giunchiglia, Andrea Passerini

We present a framework capable of tackilng the problem of continual object recognition in a setting which resembles that under whichhumans see and learn.

Active Learning Object Recognition

CogNet: A Large-Scale Cognate Database

1 code implementation ACL 2019 Khuyagbaatar Batsuren, Gabor Bella, Fausto Giunchiglia

This paper introduces CogNet, a new, large-scale lexical database that provides cognates -words of common origin and meaning- across languages.

TrentoTeam at SemEval-2017 Task 3: An application of Grice Maxims in Ranking Community Question Answers

no code implementations SEMEVAL 2017 Mohammed R. H. Qwaider, Abed Alhakim Freihat, Fausto Giunchiglia

In this paper we present the Tren-toTeam system which participated to thetask 3 at SemEval-2017 (Nakov et al., 2017). We concentrated our work onapplying Grice Maxims(used in manystate-of-the-art Machine learning applica-tions(Vogel et al., 2013; Kheirabadiand Aghagolzadeh, 2012; Dale and Re-iter, 1995; Franke, 2011)) to ranking an-swers of a question by answers relevancy. Particularly, we created a ranker systembased on relevancy scores, assigned by 3main components: Named entity recogni-tion, similarity score, sentiment analysis. Our system obtained a comparable resultsto Machine learning systems.

Named Entity Recognition Sentiment Analysis

Compositional Learning of Relation Path Embedding for Knowledge Base Completion

no code implementations22 Nov 2016 Xixun Lin, Yanchun Liang, Fausto Giunchiglia, Xiaoyue Feng, Renchu Guan

In this paper, we study the problem of how to better embed entities and relations of knowledge bases into different low-dimensional spaces by taking full advantage of the additional semantics of relation paths, and we propose a compositional learning model of relation path embedding (RPE).

Knowledge Base Completion

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