Transformer-Based Named Entity Recognition for French Using Adversarial Adaptation to Similar Domain Corpora

Choudhry, Arjun; Gupta, Pankaj; Khatri, Inder; Gupta, Aaryan; Nicol, Maxime; Meurs, Marie-Jean; Vishwakarma, Dinesh Kumar

doi:10.48550/arxiv.2212.03692

Cited by 3 publications

(2 citation statements)

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“…The low performance of NER tools compared to our proposed solution can be explained by two reasons. Firstly, the French language is known to have less publicly available labelled data that participate in the training of the NER tools [25]. Secondly, the social network texts are often poorly formulated, with grammatical and syntax errors which make the context difficult to understand by the tools and thus failing to recognise location entities.…”

Section: Discussionmentioning

confidence: 99%

Untitled

2023

IJNLC

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Terrorism has become a worldwide plague with severe consequences for the development of nations. Besides killing innocent people daily and preventing educational activities from taking place, terrorism is also hindering economic growth. Machine Learning (ML) and Natural Language Processing (NLP) can contribute to fighting terrorism by predicting in real-time future terrorist attacks if accurate data is available. This paper is part of a research project that uses text from social networks to extract necessary information to build an adequate dataset for terrorist attack prediction. We collected a set of 3000 social network texts about terrorism in Burkina Faso and used a subset to experiment with existing NLP solutions. The experiment reveals that existing solutions have poor accuracy for location recognition, which our solution resolves. We will extend the solution to extract dates and action information to achieve the project's goal.

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Section: Discussionmentioning

confidence: 99%

Untitled

2023

IJNLC

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“…We chose spaCy because it is robust, well supported, and regularly updated, with a full pipeline. There are perhaps state-of-the-art resources for each of the components (e.g., for French NER [9]), but integration into a pipeline is at best complex and often impossible. In prior work, we had built a quote detection system for English, which is well supported by spaCy's existing English language models [10,11].…”

Section: Multilingual Nlp and The Hegemony Of Englishmentioning

confidence: 99%

Radar de Parité: An NLP system to measure gender representation in French news stories

Soumah

Rao

Eibl

et al. 2023

Proceedings of the Canadian Conference on Artificial Intelligence

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We present the Radar de Parité, an automated Natural Language Processing (NLP) system that measures the proportion of women and men quoted daily in six Canadian French-language media outlets. We outline the system's architecture and detail the challenges we overcame to address French-specific issues, in particular regarding coreference resolution, a new contribution to the NLP literature on French. We also showcase statistics covering over one year's worth of data (282,512 news articles). Our results highlight the underrepresentation of women in news stories, while also illustrating the application of modern NLP methods to measure gender representation and address societal issues.

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