Papers by Francesco Periti
Analyzing Semantic Change through Lexical Replacements (2024.acl-long)
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| Challenge: | Modern language models can contextualize words based on their surrounding contexts, but semantic change can compromise this capability. |
| Approach: | They propose a replacement schema where a target word is replaced with lexical replacements of varying relatedness . they leverage the replacement schema as a basis for a novel interpretable model for semantic change . |
| Outcome: | The proposed model is the first to evaluate LLaMa for semantic change detection . it shows that lexical replacements can detect unexpected contexts . |
Automatically Generated Definitions and their utility for Modeling Word Meaning (2024.emnlp-main)
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| Challenge: | Modern language models generate semantic representations for words based on context and context based models. |
| Approach: | They propose to use dictionary-like sense definitions to generate sentence embeddings . they evaluate the quality of the generated definitions on existing English benchmarks based on the results of their study . |
| Outcome: | The proposed model sets new state-of-the-art results on lexical semantics tasks compared to baselines . |
A Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic Change (2024.naacl-long)
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| Challenge: | Contextualized embeddings are the preferred tool for modeling Lexical Semantic Change (LSC) current evaluations focus on a specific task known as Graded Change Detection (GCD) however, performance comparisons between different approaches are often misleading due to diverse settings. |
| Approach: | They evaluate the performance of contextualized embeddings for Lexical Semantic Change (LSC) they break the problem into Word-in-Context (WiC) and Word Sense Induction (WSI) tasks . |
| Outcome: | The proposed model outperforms other models on eight available benchmarks for Lexical Semantic Change (LSC) while comparable to GPT-4. |
(Chat)GPT v BERT Dawn of Justice for Semantic Change Detection (2024.findings-eacl)
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| Challenge: | In the universe of Natural Language Processing, Transformer-based language models like BERT and (Chat)GPT have emerged as lexical superheroes with great power to solve open research problems. |
| Approach: | They propose to use (Chat)GPT to solve two diachronic extensions of the Word-in-Context task: TempoWiC and HistoWic. |
| Outcome: | The proposed technology performs significantly worse than the foundational GPT version of (Chat)GPT for studying semantic change. |
Explaining novel senses using definition generation with open language models (2025.findings-emnlp)
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| Challenge: | We apply definition generators based on open-weights large language models to create explanations of novel senses. |
| Approach: | They apply open-weights large language models to create explanations of novel senses using target word usages as input. |
| Outcome: | The proposed definition generators perform on par with decoder-only models. |
Computational modeling of semantic change (2024.eacl-tutorials)
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| Challenge: | Languages change constantly over time, influenced by social, technological, cultural and political factors that affect how people express themselves. |
| Approach: | They propose to categorise the types of change, the causes and the mechanisms underlying the different types of changes using large diachronic corpora and evaluation benchmarks. |
| Outcome: | In historical linguistics, tools and methods have been developed to analyse the process . they include categorisations of types of change, causes and mechanisms . but traditional methods, while informative, are often based on small, carefully curated samples. |
TRoTR: A Framework for Evaluating the Re-contextualization of Text Reuse (2024.emnlp-main)
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| Challenge: | Existing methods for detecting text reuse focus on recontextualization . current approaches focus on text reuse across a diachronic corpus . |
| Approach: | They propose a framework that relies on topic relatedness for evaluating the diachronic change of context in which text is reused. |
| Outcome: | The proposed framework evaluates biblical text reuse human-annotated with topic relatedness . it exhibits greater sensitivity to textual similarity than topic relatedity, the authors show . |
SenseRel: A Sense-Level Benchmark for Denotational and Connotational Meaning Relations (2026.acl-long)
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Pierluigi Cassotti, Naomi Baes, Stefano De Pascale, Jáder Martins Camboim de Sá, Francesco Periti, Nick Haslam, Dirk Geeraerts, Nina Tahmasebi
| Challenge: | Polysemy enables a single word to convey multiple related meanings . a word's sense is extended to new contexts and concepts, a process called semantic change is gradual . |
| Approach: | They propose a benchmark for modeling semantic relations between word senses . they use a model that distinguishes denotational and connotationally related aspects of meaning . |
| Outcome: | The proposed model is able to distinguish between denotational and connotationalist aspects of meaning . it is compared with models with GPT-4o, Llama 3.1, and DeepSeek . |
Definition Generation for Word Meaning Modeling: Monolingual, Multilingual, and Cross-Lingual Perspectives (2025.emnlp-main)
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| Challenge: | Recent advances in text generation have opened up new opportunities for word meaning modeling. |
| Approach: | They extend definition generation task beyond English to a suite of 22 languages . they use Llama-based models to evaluate models in monolingual, multilingual, cross-lingual settings . |
| Outcome: | The proposed model outperforms pretrained models in monolingual, multilingual, and cross-lingual settings. |