Papers by Francesco Cecconi
WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) is a key intermediate task in NLP. |
| Approach: | They propose a method which uses knowledge-based approaches and neural models to produce high-quality training corpora for NER. |
| Outcome: | The proposed method improves on standard benchmarks and yields significant improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation. |
InVeRo-XL: Making Cross-Lingual Semantic Role Labeling Accessible with Intelligible Verbs and Roles (2021.emnlp-demo)
Copied to clipboard
| Challenge: | InVeRo-XL is an off-the-shelf system capable of annotating text with predicate sense and semantic role labels from 7 predicated-argument structure inventories in more than 40 languages. |
| Approach: | They propose to use RESTful API and Web interface to integrate sentence-level semantics into cross-lingual downstream tasks. |
| Outcome: | The proposed system can annotate text with predicate sense and semantic role labels from 7 predicated-argument structure inventories in more than 40 languages. |
Named Entity Recognition for Entity Linking: What Works and What’s Next (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Entity Linking (EL) systems have achieved impressive results on standard benchmarks thanks to the contextualized representations provided by recent pretrained language models. |
| Approach: | They propose to exploit Named Entity Recognition (NER) to narrow the gap between EL systems trained on high and low amounts of labeled data. |
| Outcome: | The proposed model can be exploited to narrow the gap between EL systems trained on high and low amounts of labeled data. |
AMuSE-WSD: An All-in-one Multilingual System for Easy Word Sense Disambiguation (2021.emnlp-demo)
Copied to clipboard
| Challenge: | Word Sense Disambiguation (WSD) is a task of associating a word in context with its most appropriate sense from a predefined sense inventory. |
| Approach: | They propose to use a state-of-the-art neural model to integrate WSD into real-world applications. |
| Outcome: | The proposed system offers high-quality sense information in 40 languages through a state-of-the-art neural model for WSD. |