From LIMA to DeepLIMA: following a new path of interoperability

TL;DR

This paper presents DeepLIMA, an evolution of the LIMA framework, integrating deep neural network modules for multilingual text analysis, expanding language support, and promoting interoperability through standardized models and data.

Contribution

The paper introduces DeepLIMA, enhancing LIMA with deep learning modules, increased language support, and standardized interoperability using Universal Dependencies.

Findings

Supported over 60 languages with new models

Integrated deep learning modules into LIMA

Enabled interoperability through standardized models

Abstract

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.