Search Results for author: Roberto Navigli

Found 119 papers, 38 papers with code

Generationary or ``How We Went beyond Word Sense Inventories and Learned to Gloss''

no code implementations EMNLP 2020 Michele Bevilacqua, Marco Maru, Roberto Navigli

Mainstream computational lexical semantics embraces the assumption that word senses can be represented as discrete items of a predefined inventory.

Decoder Word Sense Disambiguation

ConSeC: Word Sense Disambiguation as Continuous Sense Comprehension

1 code implementation EMNLP 2021 Edoardo Barba, Luigi Procopio, Roberto Navigli

Supervised systems have nowadays become the standard recipe for Word Sense Disambiguation (WSD), with Transformer-based language models as their primary ingredient.

Word Sense Disambiguation

Named Entity Recognition for Entity Linking: What Works and What’s Next

1 code implementation Findings (EMNLP) 2021 Simone Tedeschi, Simone Conia, Francesco Cecconi, Roberto Navigli

Entity Linking (EL) systems have achieved impressive results on standard benchmarks mainly thanks to the contextualized representations provided by recent pretrained language models.

Entity Disambiguation Entity Linking +3

UniteD-SRL: A Unified Dataset for Span- and Dependency-Based Multilingual and Cross-Lingual Semantic Role Labeling

1 code implementation Findings (EMNLP) 2021 Rocco Tripodi, Simone Conia, Roberto Navigli

Multilingual and cross-lingual Semantic Role Labeling (SRL) have recently garnered increasing attention as multilingual text representation techniques have become more effective and widely available.

Cross-Lingual Transfer Semantic Role Labeling

XL-AMR: Enabling Cross-Lingual AMR Parsing with Transfer Learning Techniques

1 code implementation EMNLP 2020 Rexhina Blloshmi, Rocco Tripodi, Roberto Navigli

Abstract Meaning Representation (AMR) is a popular formalism of natural language that represents the meaning of a sentence as a semantic graph.

Abstract Meaning Representation AMR Parsing +2

Universal Semantic Annotator: the First Unified API for WSD, SRL and Semantic Parsing

no code implementations LREC 2022 Riccardo Orlando, Simone Conia, Stefano Faralli, Roberto Navigli

In this paper, we present the Universal Semantic Annotator (USeA), which offers the first unified API for high-quality automatic annotations of texts in 100 languages through state-of-the-art systems for Word Sense Disambiguation, Semantic Role Labeling and Semantic Parsing.

Semantic Parsing Semantic Role Labeling +1

ID10M: Idiom Identification in 10 Languages

1 code implementation Findings (NAACL) 2022 Simone Tedeschi, Federico Martelli, Roberto Navigli

Idioms are phrases which present a figurative meaning that cannot be (completely) derived by looking at the meaning of their individual components. Identifying and understanding idioms in context is a crucial goal and a key challenge in a wide range of Natural Language Understanding tasks.

Natural Language Understanding

MultiNERD: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation)

1 code implementation Findings (NAACL) 2022 Simone Tedeschi, Roberto Navigli

Named Entity Recognition (NER) is the task of identifying named entities in texts and classifying them through specific semantic categories, a process which is crucial for a wide range of NLP applications.

Entity Linking named-entity-recognition +2

Nibbling at the Hard Core of Word Sense Disambiguation

1 code implementation ACL 2022 Marco Maru, Simone Conia, Michele Bevilacqua, Roberto Navigli

With state-of-the-art systems having finally attained estimated human performance, Word Sense Disambiguation (WSD) has now joined the array of Natural Language Processing tasks that have seemingly been solved, thanks to the vast amounts of knowledge encoded into Transformer-based pre-trained language models.

Word Sense Disambiguation

Probing for Predicate Argument Structures in Pretrained Language Models

1 code implementation ACL 2022 Simone Conia, Roberto Navigli

Thanks to the effectiveness and wide availability of modern pretrained language models (PLMs), recently proposed approaches have achieved remarkable results in dependency- and span-based, multilingual and cross-lingual Semantic Role Labeling (SRL).

Semantic Role Labeling

SRL4E – Semantic Role Labeling for Emotions: A Unified Evaluation Framework

1 code implementation ACL 2022 Cesare Campagnano, Simone Conia, Roberto Navigli

In the field of sentiment analysis, several studies have highlighted that a single sentence may express multiple, sometimes contrasting, sentiments and emotions, each with its own experiencer, target and/or cause.

Semantic Role Labeling Sentence +1

ExtEnD: Extractive Entity Disambiguation

1 code implementation ACL 2022 Edoardo Barba, Luigi Procopio, Roberto Navigli

Local models for Entity Disambiguation (ED) have today become extremely powerful, in most part thanks to the advent of large pre-trained language models.

Entity Disambiguation

Invited Talk: Generationary or: “How We Went beyond Sense Inventories and Learned to Gloss”

no code implementations COLING (MWE) 2020 Roberto Navigli

In this talk I present Generationary, an approach that goes beyond the mainstream assumption that word senses can be represented as discrete items of a predefined inventory, and put forward a unified model which produces contextualized definitions for arbitrary lexical items, from words to phrases and even sentences.

Decoder Word Sense Disambiguation

Integrating Personalized PageRank into Neural Word Sense Disambiguation

1 code implementation EMNLP 2021 Ahmed El Sheikh, Michele Bevilacqua, Roberto Navigli

Neural Word Sense Disambiguation (WSD) has recently been shown to benefit from the incorporation of pre-existing knowledge, such as that coming from the WordNet graph.

Word Sense Disambiguation

Reducing Disambiguation Biases in NMT by Leveraging Explicit Word Sense Information

no code implementations NAACL 2022 Niccolò Campolungo, Tommaso Pasini, Denis Emelin, Roberto Navigli

Recent studies have shed some light on a common pitfall of Neural Machine Translation (NMT) models, stemming from their struggle to disambiguate polysemous words without lapsing into their most frequently occurring senses in the training corpus. In this paper, we first provide a novel approach for automatically creating high-precision sense-annotated parallel corpora, and then put forward a specifically tailored fine-tuning strategy for exploiting these sense annotations during training without introducing any additional requirement at inference time. The use of explicit senses proved to be beneficial to reduce the disambiguation bias of a baseline NMT model, while, at the same time, leading our system to attain higher BLEU scores than its vanilla counterpart in 3 language pairs.

Machine Translation NMT +1

InVeRo-XL: Making Cross-Lingual Semantic Role Labeling Accessible with Intelligible Verbs and Roles

no code implementations EMNLP (ACL) 2021 Simone Conia, Riccardo Orlando, Fabrizio Brignone, Francesco Cecconi, Roberto Navigli

Notwithstanding the growing interest in cross-lingual techniques for Natural Language Processing, there has been a surprisingly small number of efforts aimed at the development of easy-to-use tools for cross-lingual Semantic Role Labeling.

Semantic Role Labeling Sentence

AMuSE-WSD: An All-in-one Multilingual System for Easy Word Sense Disambiguation

no code implementations EMNLP (ACL) 2021 Riccardo Orlando, Simone Conia, Fabrizio Brignone, Francesco Cecconi, Roberto Navigli

Over the past few years, Word Sense Disambiguation (WSD) has received renewed interest: recently proposed systems have shown the remarkable effectiveness of deep learning techniques in this task, especially when aided by modern pretrained language models.

Word Sense Disambiguation

SPRING Goes Online: End-to-End AMR Parsing and Generation

no code implementations EMNLP (ACL) 2021 Rexhina Blloshmi, Michele Bevilacqua, Edoardo Fabiano, Valentina Caruso, Roberto Navigli

In this paper we present SPRING Online Services, a Web interface and RESTful APIs for our state-of-the-art AMR parsing and generation system, SPRING (Symmetric PaRsIng aNd Generation).

AMR Parsing

GeneSis: A Generative Approach to Substitutes in Context

1 code implementation EMNLP 2021 Caterina Lacerra, Rocco Tripodi, Roberto Navigli

The lexical substitution task aims at generating a list of suitable replacements for a target word in context, ideally keeping the meaning of the modified text unchanged.

FENICE: Factuality Evaluation of summarization based on Natural language Inference and Claim Extraction

no code implementations4 Mar 2024 Alessandro Scirè, Karim Ghonim, Roberto Navigli

To address these shortcomings, we propose Factuality Evaluation of summarization based on Natural language Inference and Claim Extraction (FENICE), a more interpretable and efficient factuality-oriented metric.

Natural Language Inference Text Summarization

Code-Switching with Word Senses for Pretraining in Neural Machine Translation

no code implementations21 Oct 2023 Vivek Iyer, Edoardo Barba, Alexandra Birch, Jeff Z. Pan, Roberto Navigli

Lexical ambiguity is a significant and pervasive challenge in Neural Machine Translation (NMT), with many state-of-the-art (SOTA) NMT systems struggling to handle polysemous words (Campolungo et al., 2022).

Denoising Machine Translation +2

Exploring Non-Verbal Predicates in Semantic Role Labeling: Challenges and Opportunities

no code implementations4 Jul 2023 Riccardo Orlando, Simone Conia, Roberto Navigli

Although we have witnessed impressive progress in Semantic Role Labeling (SRL), most of the research in the area is carried out assuming that the majority of predicates are verbs.

Semantic Role Labeling Transfer Learning

Incorporating Graph Information in Transformer-based AMR Parsing

1 code implementation23 Jun 2023 Pavlo Vasylenko, Pere-Lluís Huguet Cabot, Abelardo Carlos Martínez Lorenzo, Roberto Navigli

Abstract Meaning Representation (AMR) is a Semantic Parsing formalism that aims at providing a semantic graph abstraction representing a given text.

Ranked #3 on AMR Parsing on LDC2020T02 (using extra training data)

Abstract Meaning Representation AMR Parsing +2

AMRs Assemble! Learning to Ensemble with Autoregressive Models for AMR Parsing

1 code implementation19 Jun 2023 Abelardo Carlos Martínez Lorenzo, Pere-Lluís Huguet Cabot, Roberto Navigli

In this paper, we examine the current state-of-the-art in AMR parsing, which relies on ensemble strategies by merging multiple graph predictions.

AMR Parsing

RED$^{\rm FM}$: a Filtered and Multilingual Relation Extraction Dataset

1 code implementation16 Jun 2023 Pere-Lluís Huguet Cabot, Simone Tedeschi, Axel-Cyrille Ngonga Ngomo, Roberto Navigli

Relation Extraction (RE) is a task that identifies relationships between entities in a text, enabling the acquisition of relational facts and bridging the gap between natural language and structured knowledge.

Relation Relation Extraction

Echoes from Alexandria: A Large Resource for Multilingual Book Summarization

1 code implementation7 Jun 2023 Alessandro Scirè, Simone Conia, Simone Ciciliano, Roberto Navigli

In recent years, research in text summarization has mainly focused on the news domain, where texts are typically short and have strong layout features.

Book summarization Text Summarization

What's the Meaning of Superhuman Performance in Today's NLU?

no code implementations15 May 2023 Simone Tedeschi, Johan Bos, Thierry Declerck, Jan Hajic, Daniel Hershcovich, Eduard H. Hovy, Alexander Koller, Simon Krek, Steven Schockaert, Rico Sennrich, Ekaterina Shutova, Roberto Navigli

In the last five years, there has been a significant focus in Natural Language Processing (NLP) on developing larger Pretrained Language Models (PLMs) and introducing benchmarks such as SuperGLUE and SQuAD to measure their abilities in language understanding, reasoning, and reading comprehension.

Position Reading Comprehension

Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures

1 code implementation2 Dec 2022 Simone Conia, Edoardo Barba, Alessandro Scirè, Roberto Navigli

One of the common traits of past and present approaches for Semantic Role Labeling (SRL) is that they rely upon discrete labels drawn from a predefined linguistic inventory to classify predicate senses and their arguments.

Semantic Role Labeling

Focusing on Context is NICE: Improving Overshadowed Entity Disambiguation

no code implementations12 Oct 2022 Vera Provatorova, Simone Tedeschi, Svitlana Vakulenko, Roberto Navigli, Evangelos Kanoulas

Entity disambiguation (ED) is the task of mapping an ambiguous entity mention to the corresponding entry in a structured knowledge base.

Entity Disambiguation

Entity Disambiguation with Entity Definitions

1 code implementation11 Oct 2022 Luigi Procopio, Simone Conia, Edoardo Barba, Roberto Navigli

Local models have recently attained astounding performances in Entity Disambiguation (ED), with generative and extractive formulations being the most promising research directions.

Entity Disambiguation

Cross-lingual AMR Aligner: Paying Attention to Cross-Attention

1 code implementation15 Jun 2022 Abelardo Carlos Martínez Lorenzo, Pere-Lluís Huguet Cabot, Roberto Navigli

This paper introduces a novel aligner for Abstract Meaning Representation (AMR) graphs that can scale cross-lingually, and is thus capable of aligning units and spans in sentences of different languages.

Abstract Meaning Representation Semantic Parsing

REBEL: Relation Extraction By End-to-end Language generation

1 code implementation Findings (EMNLP) 2021 Pere-Lluis Huguet Cabot, Roberto Navigli

Extracting relation triplets from raw text is a crucial task in Information Extraction, enabling multiple applications such as populating or validating knowledge bases, factchecking, and other downstream tasks.

 Ranked #1 on Joint Entity and Relation Extraction on DocRED (using extra training data)

Entity Linking Joint Entity and Relation Extraction +3

SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC)

1 code implementation SEMEVAL 2021 Federico Martelli, Najla Kalach, Gabriele Tola, Roberto Navigli

We illustrate our task, as well as the construction of our manually-created dataset including five languages, namely Arabic, Chinese, English, French and Russian, and the results of the participating systems.

Binary Classification Task 2

AAA: Fair Evaluation for Abuse Detection Systems Wanted

1 code implementation ACM Web Science 2021 Agostina Calabrese, Michele Bevilacqua, Björn Ross, Rocco Tripodi, Roberto Navigli

In this work, we introduce Adversarial Attacks against Abuse (AAA), a new evaluation strategy and associated metric that better captures a model’s performance on certain classes of hard-to-classify microposts, and for example penalises systems which are biased on low-level lexical features.

Abusive Language Hate Speech Detection +1

Unifying Cross-Lingual Semantic Role Labeling with Heterogeneous Linguistic Resources

1 code implementation NAACL 2021 Simone Conia, Andrea Bacciu, Roberto Navigli

While cross-lingual techniques are finding increasing success in a wide range of Natural Language Processing tasks, their application to Semantic Role Labeling (SRL) has been strongly limited by the fact that each language adopts its own linguistic formalism, from PropBank for English to AnCora for Spanish and PDT-Vallex for Czech, inter alia.

Semantic Role Labeling Sentence +2

ESC: Redesigning WSD with Extractive Sense Comprehension

1 code implementation NAACL 2021 Edoardo Barba, Tommaso Pasini, Roberto Navigli

By means of an extensive array of experiments, we show that ESC unleashes the full potential of our model, leading it to outdo all of its competitors and to set a new state of the art on the English WSD task.

Multi-Label Classification Sentence +1

One SPRING to Rule Them Both: Symmetric AMR Semantic Parsing and Generation without a Complex Pipeline

1 code implementation Proceedings of the AAAI Conference on Artificial Intelligence 2021 Michele Bevilacqua, Rexhina Blloshmi, Roberto Navigli

In Text-to-AMR parsing, current state-of-the-art semantic parsers use cumbersome pipelines integrating several different modules or components, and exploit graph recategorization, i. e., a set of content-specific heuristics that are developed on the basis of the training set.

AMR Parsing AMR-to-Text Generation +2

Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration

1 code implementation EACL 2021 Simone Conia, Roberto Navigli

Recent studies treat Word Sense Disambiguation (WSD) as a single-label classification problem in which one is asked to choose only the best-fitting sense for a target word, given its context.

Multi-Label Classification Word Sense Disambiguation

Conception: Multilingually-Enhanced, Human-Readable Concept Vector Representations

1 code implementation COLING 2020 Simone Conia, Roberto Navigli

To date, the most successful word, word sense, and concept modelling techniques have used large corpora and knowledge resources to produce dense vector representations that capture semantic similarities in a relatively low-dimensional space.

Word Sense Disambiguation Word Similarity

Bridging the Gap in Multilingual Semantic Role Labeling: a Language-Agnostic Approach

1 code implementation COLING 2020 Simone Conia, Roberto Navigli

Recent research indicates that taking advantage of complex syntactic features leads to favorable results in Semantic Role Labeling.

Semantic Role Labeling

InVeRo: Making Semantic Role Labeling Accessible with Intelligible Verbs and Roles

no code implementations EMNLP 2020 Simone Conia, Fabrizio Brignone, Davide Zanfardino, Roberto Navigli

Semantic Role Labeling (SRL) is deeply dependent on complex linguistic resources and sophisticated neural models, which makes the task difficult to approach for non-experts.

Semantic Role Labeling

Personalized PageRank with Syntagmatic Information for Multilingual Word Sense Disambiguation

no code implementations ACL 2020 Federico Scozzafava, Marco Maru, Fabrizio Brignone, Giovanni Torrisi, Roberto Navigli

Exploiting syntagmatic information is an encouraging research focus to be pursued in an effort to close the gap between knowledge-based and supervised Word Sense Disambiguation (WSD) performance.

Word Sense Disambiguation

Fatality Killed the Cat or: BabelPic, a Multimodal Dataset for Non-Concrete Concepts

no code implementations ACL 2020 Agostina Calabrese, Michele Bevilacqua, Roberto Navigli

Thanks to the wealth of high-quality annotated images available in popular repositories such as ImageNet, multimodal language-vision research is in full bloom.

Sense-Annotated Corpora for Word Sense Disambiguation in Multiple Languages and Domains

no code implementations LREC 2020 Bianca Scarlini, Tommaso Pasini, Roberto Navigli

This limits the range of action of deep-learning approaches, which today are at the base of any NLP task and are hungry for data.

Word Sense Disambiguation

Building Semantic Grams of Human Knowledge

no code implementations LREC 2020 Valentina Leone, Giovanni Siragusa, Luigi di Caro, Roberto Navigli

Word senses are typically defined with textual definitions for human consumption and, in computational lexicons, put in context via lexical-semantic relations such as synonymy, antonymy, hypernymy, etc.

Semantic Similarity Semantic Textual Similarity +1

Knowledge Graphs

2 code implementations4 Mar 2020 Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Melo, Claudio Gutierrez, José Emilio Labra Gayo, Sabrina Kirrane, Sebastian Neumaier, Axel Polleres, Roberto Navigli, Axel-Cyrille Ngonga Ngomo, Sabbir M. Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, Antoine Zimmermann

In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data.

Knowledge Graphs

SyntagNet: Challenging Supervised Word Sense Disambiguation with Lexical-Semantic Combinations

no code implementations IJCNLP 2019 Marco Maru, Federico Scozzafava, Federico Martelli, Roberto Navigli

Current research in knowledge-based Word Sense Disambiguation (WSD) indicates that performances depend heavily on the Lexical Knowledge Base (LKB) employed.

Word Sense Disambiguation

VerbAtlas: a Novel Large-Scale Verbal Semantic Resource and Its Application to Semantic Role Labeling

no code implementations IJCNLP 2019 Andrea Di Fabio, Simone Conia, Roberto Navigli

We present VerbAtlas, a new, hand-crafted lexical-semantic resource whose goal is to bring together all verbal synsets from WordNet into semantically-coherent frames.

Semantic Role Labeling

Game Theory Meets Embeddings: a Unified Framework for Word Sense Disambiguation

no code implementations IJCNLP 2019 Rocco Tripodi, Roberto Navigli

They represent ambiguous words as the players of a non cooperative game and their senses as the strategies that the players can select in order to play the games.

Word Sense Disambiguation

Quasi Bidirectional Encoder Representations from Transformers for Word Sense Disambiguation

no code implementations RANLP 2019 Michele Bevilacqua, Roberto Navigli

While contextualized embeddings have produced performance breakthroughs in many Natural Language Processing (NLP) tasks, Word Sense Disambiguation (WSD) has not benefited from them yet.

Word Sense Disambiguation

Just ``OneSeC'' for Producing Multilingual Sense-Annotated Data

no code implementations ACL 2019 Bianca Scarlini, Tommaso Pasini, Roberto Navigli

The well-known problem of knowledge acquisition is one of the biggest issues in Word Sense Disambiguation (WSD), where annotated data are still scarce in English and almost absent in other languages.

Word Sense Disambiguation

LSTMEmbed: Learning Word and Sense Representations from a Large Semantically Annotated Corpus with Long Short-Term Memories

no code implementations ACL 2019 Ignacio Iacobacci, Roberto Navigli

While word embeddings are now a de facto standard representation of words in most NLP tasks, recently the attention has been shifting towards vector representations which capture the different meanings, i. e., senses, of words.

Word Embeddings

Huge Automatically Extracted Training Sets for Multilingual Word Sense Disambiguation

no code implementations12 May 2018 Tommaso Pasini, Francesco Maria Elia, Roberto Navigli

We release to the community six large-scale sense-annotated datasets in multiple language to pave the way for supervised multilingual Word Sense Disambiguation.

Word Sense Disambiguation

SemEval-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity

no code implementations SEMEVAL 2017 Jose Camacho-Collados, Mohammad Taher Pilehvar, Nigel Collier, Roberto Navigli

This paper introduces a new task on Multilingual and Cross-lingual SemanticThis paper introduces a new task on Multilingual and Cross-lingual Semantic Word Similarity which measures the semantic similarity of word pairs within and across five languages: English, Farsi, German, Italian and Spanish.

Information Retrieval Machine Translation +9

EuroSense: Automatic Harvesting of Multilingual Sense Annotations from Parallel Text

no code implementations ACL 2017 Claudio Delli Bovi, Jose Camacho-Collados, Aless Raganato, ro, Roberto Navigli

Parallel corpora are widely used in a variety of Natural Language Processing tasks, from Machine Translation to cross-lingual Word Sense Disambiguation, where parallel sentences can be exploited to automatically generate high-quality sense annotations on a large scale.

Entity Linking Machine Translation +2

BabelDomains: Large-Scale Domain Labeling of Lexical Resources

no code implementations EACL 2017 Jose Camacho-Collados, Roberto Navigli

In this paper we present BabelDomains, a unified resource which provides lexical items with information about domains of knowledge.

Clustering Domain Adaptation +4

Embedding Words and Senses Together via Joint Knowledge-Enhanced Training

no code implementations CONLL 2017 Massimiliano Mancini, Jose Camacho-Collados, Ignacio Iacobacci, Roberto Navigli

Word embeddings are widely used in Natural Language Processing, mainly due to their success in capturing semantic information from massive corpora.

Word Embeddings

Semantic Representations of Word Senses and Concepts

no code implementations2 Aug 2016 José Camacho-Collados, Ignacio Iacobacci, Roberto Navigli, Mohammad Taher Pilehvar

Representing the semantics of linguistic items in a machine-interpretable form has been a major goal of Natural Language Processing since its earliest days.

Multilinguality at Your Fingertips : BabelNet, Babelfy and Beyond !

no code implementations JEPTALNRECITAL 2015 Roberto Navigli

Multilinguality is a key feature of today{'}s Web, and it is this feature that we leverage and exploit in our research work at the Sapienza University of Rome{'}s Linguistic Computing Laboratory, which I am going to overview and showcase in this talk.

Entity Linking Semantic Similarity +2

Annotating the MASC Corpus with BabelNet

no code implementations LREC 2014 Andrea Moro, Roberto Navigli, Francesco Maria Tucci, Rebecca J. Passonneau

Finally, we estimate the quality of our annotations using both manually-tagged named entities and word senses, obtaining an accuracy of roughly 70{\%} for both named entities and word sense annotations.

Entity Linking Reading Comprehension +2

A New Method for Evaluating Automatically Learned Terminological Taxonomies

no code implementations LREC 2012 Paola Velardi, Roberto Navigli, Stefano Faralli, Juana Maria Ruiz Martinez

Our method assigns a similarity value B{\textasciicircum}i{\_}(l, r) to the learned (l) and reference (r) taxonomy for each cut i of the corresponding anonymised hierarchies, starting from the topmost nodes down to the leaf concepts.

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