In this paper, we describe the system used for our first participation at the CoNLL 2018 shared task. The submitted system largely reused the state of the art parser from CoNLL 2017 (https://github.com/tdozat/Parser-v2). We enhanced this system for morphological features predictions, and we used all available resources to provide accurate models for low-resource languages. We ranked 5th of 27 participants in MLAS for building morphology aware dependency trees, 2nd for morphological features only, and 3rd for tagging (UPOS) and parsing (LAS) low-resource languages.

Le système présenté permet la construction automatisée d’une base de connaissances sur des personnes et des organisations à partir d’une collection de documents. Il s’appuie sur de l’apprentissage distant pour l’extraction d’hypothèses de relations entre mentions d’entités qu’il consolide avec des informations orientées graphe.

The increasing amount of available textual information makes necessary the use of Natural Language Processing (NLP) tools. These tools have to be used on large collections of documents in different languages. But NLP is a complex task that relies on many processes and resources. As a consequence, NLP tools must be both configurable and efficient: specific software architectures must be designed for this purpose. We present in this paper the LIMA multilingual analysis platform, developed at CEA LIST. This configurable platform has been designed to develop NLP based industrial applications while keeping enough flexibility to integrate various processes and resources. This design makes LIMA a linguistic analyzer that can handle languages as different as French, English, German, Arabic or Chinese. Beyond its architecture principles and its capabilities as a linguistic analyzer, LIMA also offers a set of tools dedicated to the test and the evaluation of linguistic modules and to the production and the management of new linguistic resources.