From LIMA to DeepLIMA: following a new path of interoperability
This work provides a multilingual NLP tool with improved language coverage for researchers and developers, though it is incremental as it builds upon an existing framework.
The authors extended the LIMA framework by integrating deep neural network modules for text analysis, training models on over 60 languages using datasets like Universal Dependencies 2.5, WikiNer, and CoNLL-03, while maintaining existing rule-based and statistical components. This approach enhances language support and promotes interoperability through normalized models and data.
In this article, we describe the architecture of the LIMA (Libre Multilingual Analyzer) framework and its recent evolution with the addition of new text analysis modules based on deep neural networks. We extended the functionality of LIMA in terms of the number of supported languages while preserving existing configurable architecture and the availability of previously developed rule-based and statistical analysis components. Models were trained for more than 60 languages on the Universal Dependencies 2.5 corpora, WikiNer corpora, and CoNLL-03 dataset. Universal Dependencies allowed us to increase the number of supported languages and to generate models that could be integrated into other platforms. This integration of ubiquitous Deep Learning Natural Language Processing models and the use of standard annotated collections using Universal Dependencies can be viewed as a new path of interoperability, through the normalization of models and data, that are complementary to a more standard technical interoperability, implemented in LIMA through services available in Docker containers on Docker Hub.