Papers by Aurélie Herbelot
Towards Incremental Learning of Word Embeddings Using Context Informativeness (P19-2)
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| Challenge: | In this paper, we investigate the task of learning word embeddings from very sparse data in an incremental, cognitively-plausible way. |
| Approach: | They propose a model that incorporates informativeness into a proposed model of nonce learning, using it for context selection and learning rate modulation. |
| Outcome: | The proposed model is based on a proposed model of nonce learning, and it performs well on the task of learning new words from definitions and potentially uninformative contexts. |
From Brain Space to Distributional Space: The Perilous Journeys of fMRI Decoding (P19-2)
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| Challenge: | Recent work in cognitive neuroscience has introduced models for predicting distributional word meaning representations from brain imaging data. |
| Approach: | They propose to use several alternative measures to evaluate the predicted distributional space against a corpus-derived distributional spatial space. |
| Outcome: | The proposed model performs poorly on the most common metrics, while still delivering promising results. |
Butterfly Effects in Frame Semantic Parsing: impact of data processing on model ranking (C18-1)
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| Challenge: | a common contribution to computational linguistics research is a new model for a specific task. |
| Approach: | They propose an open-source standardized processing pipeline for frame semantic parsing . they propose a standard evaluation resource that can be shared and reused for robust comparison . |
| Outcome: | The proposed model can be shared and reused for robust model comparison. |