Papers by Daniel Loureiro
TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media (2022.coling-1)
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Daniel Loureiro, Aminette D’Souza, Areej Nasser Muhajab, Isabella A. White, Gabriel Wong, Luis Espinosa-Anke, Leonardo Neves, Francesco Barbieri, Jose Camacho-Collados
| Challenge: | Language models are often clean and time-invariant, and do little to no account of social media usage. |
| Approach: | They propose a benchmark to accelerate research in social media-based meaning shift. |
| Outcome: | The proposed benchmark is aimed at accelerating research in social media-based meaning shift. |
TimeLMs: Diachronic Language Models from Twitter (2022.acl-demo)
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| Challenge: | Neural language models (LMs) are a key enabler in NLP, but lack of diachronic specialization affects both the ability to generalize to future data and the reliability of experimental results. |
| Approach: | They propose to use Twitter data to develop time-specific language models that are specialized on the time variable. |
| Outcome: | The proposed models cope with trends and peaks in activity involving specific named entities or concept drift. |
Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation (P19-1)
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| Challenge: | Contextual embeddings address the problem of meaning conflation hampering word embeddables. |
| Approach: | They propose a method that creates sense-level embeddings with full-coverage of WordNet without recourse to explicit sense distributions or task-specific modelling. |
| Outcome: | The proposed method surpasses previous systems using powerful models and is robust when ignoring part-of-speech and lemma features. |
TweetNLP: Cutting-Edge Natural Language Processing for Social Media (2022.emnlp-demos)
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Jose Camacho-collados, Kiamehr Rezaee, Talayeh Riahi, Asahi Ushio, Daniel Loureiro, Dimosthenis Antypas, Joanne Boisson, Luis Espinosa Anke, Fangyu Liu, Eugenio Martínez Cámara
| Challenge: | TweetNLP is an integrated platform for natural language processing in social media. |
| Approach: | They propose a Python-based platform for natural language processing in social media that supports a variety of NLP tasks. |
| Outcome: | The proposed platform supports generic focus areas such as sentiment analysis and named entity recognition, as well as social media-specific tasks such as emoji prediction and offensive language identification. |
Don’t Neglect the Obvious: On the Role of Unambiguous Words in Word Sense Disambiguation (2020.emnlp-main)
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| Challenge: | Existing senseannotated corpora lack coverage of many instances in WordNet . however, unambiguous words make up a large portion of WordNet while being poorly covered in existing senseannnotated . |
| Approach: | They propose a method to provide annotations for most unambiguous words in a large corpus by using a dataset. |
| Outcome: | The proposed method improves on the original results on Word Sense Disambiguation (WSD) using pre-trained language models and propagation algorithms. |