Papers by Erwan Moreau
Multilingual Word Segmentation: Training Many Language-Specific Tokenizers Smoothly Thanks to the Universal Dependencies Corpus (L18-1)
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| Challenge: | Towards language scalability, major progress has been achieved in multilingual language technology in recent years. |
| Approach: | They propose a tokenizer that can be trained from any Universal Dependencies corpus dataset . they argue that tokenization should be seen as a supervised task and scalability requires a software engineering process across languages. |
| Outcome: | The proposed tokenizer can be trained from any dataset in the corpus UD2 . the proposed software tool relies on elephant to perform the training . |
An Evaluation Method for Diachronic Word Sense Induction (2020.findings-emnlp)
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| Challenge: | Existing methods to detect semantic shifts across time are based on time-stamped annotated biomedical data . dynamic behaviour of words contributes to semantic ambiguity, which is a challenge in many NLP tasks. |
| Approach: | They propose an evaluation method based on large-scale time-stamped biomedical data . they propose a model which represents the temporal dimension of the task . |
| Outcome: | The proposed method is applied to two recent DWSI systems . it provides an in-depth analysis of the models . |
Improving Diachronic Word Sense Induction with a Nonparametric Bayesian method (2023.findings-acl)
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| Challenge: | Existing models for dichronic word Sense Induction (DWSI) are not fully explored or compared against in the current state of the art. |
| Approach: | They propose two new models for Diachronic Word Sense Induction based on topic modelling techniques. |
| Outcome: | The proposed models outperform the state-of-the-art models on a time-stamped dataset from the biomedical domain. |