Modern Natural Language Processing (NLP) makes intensive use of deep learning methods because of the accuracy they offer for a variety of applications. Due to the significant environmental impact of deep learning, costbenefit analysis including carbon footprint as well as accuracy measures has been suggested to better document the use of NLP methods for research or deployment. In this paper, we review the tools that are available to measure energy use and CO 2 emissions of NLP methods. We describe the scope of the measures provided and compare the use of six tools (carbon tracker, experiment impact tracker, green algorithms, ML CO2 impact, energy usage and cumulator) on named entity recognition experiments performed on different computational set-ups (local server vs. computing facility). Based on these findings, we propose actionable recommendations to accurately measure the environmental impact of NLP experiments.

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The identification of lexical semantic relations is of the utmost importance to enhance reasoning capacities of Natural Language Processing and Information Retrieval systems. Within this context, successful results have been achieved based on the distributional hypothesis and/or the paradigmatic assumption. However, both strategies solely rely on the input words to predict the lexical semantic relation. In this paper, we make the hypothesis that the decision process should not only rely on the input words but also on their K closest neighbors in some semantic space. For that purpose, we present different binary and multi-task classification strategies that include two distinct attention mechanisms based on PageRank. Evaluation results over four gold-standard datasets show that average improvements of 10.6% for binary and 8% for multitask classification can be achieved over baseline approaches in terms of F1. The code and the datasets are available upon demand.

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