Papers by Corentin Masson

2 papers
Evaluating Topic Model on Asymmetric and Multi-Domain Financial Corpus (2024.lrec-main)

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Challenge: Recent research attempts to quantify the exposure of market assets to various risks from text and how assets react if the risk materializes itself.
Approach: They propose two new metrics to evaluate the behavior of different types of topic models with respect to pitfalls previously mentioned about document risk distribution extraction.
Outcome: The proposed models can be used to extract unbiased risk information from financial domain data and correct coherence imbalances.
NLP Analytics in Finance with DoRe: A French 250M Tokens Corpus of Corporate Annual Reports (2020.lrec-1)

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Challenge: Recent advances in neural computing and word embeddings for semantic processing open many new applications areas which had been left unaddressed due to inadequate language understanding capacity.
Approach: They propose a French and dialectal French corpus for NLP analytics in finance, regulation and investment.
Outcome: The proposed corpus is designed to be as modular as possible to allow for maximum reuse in different tasks pertaining to Economics, Finance and Investment.

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