Papers by Bianca Scarlini
With More Contexts Comes Better Performance: Contextualized Sense Embeddings for All-Round Word Sense Disambiguation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Contextualized word embeddings have been used effectively across several tasks in Natural Language Processing, but it is difficult to link them to structured sources of knowledge. |
| Approach: | They propose a semi-supervised approach to producing sense embeddings for the lexical meanings within a lexicon that is comparable to that of contextualized word vectors. |
| Outcome: | The proposed approach outperforms state-of-the-art models in the English Word Sense Disambiguation task and in the multilingual one while training on sense-annotated data in English only. |
CluBERT: A Cluster-Based Approach for Learning Sense Distributions in Multiple Languages (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods to induce word senses from raw sentences lack reliable and high-coverage distributions. |
| Approach: | They propose an automatic and multilingual approach to inducing word senses from a corpus of raw sentences using an annotated corpus. |
| Outcome: | The proposed method outperforms all other methods on English and other languages. |
Sense-Annotated Corpora for Word Sense Disambiguation in Multiple Languages and Domains (2020.lrec-1)
Copied to clipboard
| Challenge: | Word Sense Disambiguation (WSD) is a field of NLP where data is usually tied to a specific language. |
| Approach: | They propose to release five large datasets annotated with word-senses in five different languages and 5 datasets in English for a different semantic domain. |
| Outcome: | The study shows that supervised models trained on the data achieve higher performance than those trained on other corpora. |
EPIC: Multi-Perspective Annotation of a Corpus of Irony (2023.acl-long)
Copied to clipboard
Simona Frenda, Alessandro Pedrani, Valerio Basile, Soda Marem Lo, Alessandra Teresa Cignarella, Raffaella Panizzon, Cristina Marco, Bianca Scarlini, Viviana Patti, Cristina Bosco, Davide Bernardi
| Challenge: | EPIC is the first annotated corpus for irony analysis based on data perspectivism . a recent trend in natural language processing (NLP) postulates that the disagreement among annotators in a language resource is a valuable source of knowledge, rather than noise that ought to be minimized or discarded. |
| Approach: | They propose to annotate an English perspectivist irony corpus based on data perspectivism . they validate the model by creating perspective-aware models that encode the perspectives of annotators grouped according to their demographic characteristics. |
| Outcome: | The proposed model can capture different perspectives on irony among different groups of annotators, and is more confident than non-perspectivist models. |
Just “OneSeC” for Producing Multilingual Sense-Annotated Data (P19-1)
Copied to clipboard
| Challenge: | Word Sense Disambiguation (WSD) is one of the most affected research areas . annotated data are scarce in English and almost absent in other languages . |
| Approach: | They propose a language-independent method for the automatic extraction of thousands of sentences in which a target word is tagged with its meaning. |
| Outcome: | The proposed method outperforms existing methods on multilingual and domain-specific settings. |