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Machine Learning Applied to Legal Practice

Published: on January 23, 2019 by Romain Vial

Dilitrust is a contract analytics and management solution powered by artificial intelligence. Dilitrust helps companies manage and make the most of their contract portfolio by identifying relevant information and data to manage key contractual commitments during the whole life of the contract. Our technology rests on a combination of specifically trained Natural Language Processing (NLP) algorithms and advanced machine learning techniques.

Traitement Automatique du Langage sur du texte

Published: on April 19, 2019 by Alexis Agahi

A travers cette présentation vous verrez les différents mécanismes de traitement automatique du langage (ou NLP en anglais) appliqués au texte. Nous ferons un état de l’art sur la comprehension et l’extraction d’informations en commençant par les concepts simples, puis leur évolutions majeures avec le Deep Learning, pour finir avec les dernières publications fin 2018 avec l’apprentissage non supervisé pour la production de modèles de langage, qualifié par certains, de revolution dans le domaine.

Question Answering: Zero-shot learning for contract analysis

Published: on January 31, 2022 by Ahmed Touila

At Dilitrust we are working on analyzing legal documents in general and contracts in particular for a variety of clients and activity areas.

What Makes A Great Software Engineer?

Published: on February 22, 2023 by Alexis Agahi

Is it your technical skills, your expertise, your social soft skills or your teamwork that make you a great software engineer? The next decade might completely change the perspective considering all significant progress that has been made in the machine learning field. So will the future of software engineers depend on our ability to describe a problem for a machine? Does it mean that we need to think differently about our team and expertise?

Comment les machines comprennent-elles le langage humain ?

Published: on February 23, 2023 by Hicham El Boukkouri & Ahmed Touila

Le Traitement Automatique du Langage (TAL) alimente de nombreuses technologies modernes telles que les chatbots ou la détection de spam. À la base du TAL se trouve le besoin de comprendre du texte—les octets bruts ne permettent pas d’extraire un réel sens. Cela justifie alors l’utilisation de représentations sophistiquées que nous introduisons ici: comptage (ex. TFIDF), vecteurs statiques (ex. word2vec) et réseaux de neurones (ex. Transformers).

The future of Artificial Intelligence is contrastive

Published: on February 24, 2023 by Ahmed Touila

Applying AI to real world problems in a supervised fashion requires large set of data covering all aspects of the problem we are solving. In reality, it’s impossible to build such dataset thus giving our model limited understanding of the problem leading to absurd predictions. The solution is Contrastive Learning which learns similarities and differences by contrasting samples against each other therefore limiting the need for human supervision.

Measure carbon emissions of ML projects, in python

Published: on February 24, 2023 by Amine Saboni

The environmental cost of ML has been increasing in recent years, due to its ever-increasing adoption in many software applications, as well as their size and complexity. Many parameters impact the carbon cost of a python program, such as the hardware used and the location of its execution. In this talk, we present the internals of Code Carbon, a library developed to better estimate the impact of ML projects, throughout their global life cycle.

Clustering-based Automatic Construction of Legal Entity Knowledge Base from Contracts

Published in BigData 2020, 2020 by Fuqi Song and Eric de la Clergerie

In this paper, we propose a clustering-based approach to automatically generate a reliable knowledge base of legal entities from given contracts without any supplemental references.

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Classification of Contract-Amendment Relationships

Published in ICAIL 2021, 2021 by Fuqi Song

In this paper, we propose an approach based on machine learning (ML) and Natural Language Processing (NLP) to detect the amendment relationship between two documents.

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Réseau neuronal et procédé de traitement d’image, d’extraction et de recombinaison automatique d’informations

Published in INPI, 2022 by Eric de la Clergerie & Raphael Chekroun

L’invention concerne un réseau neuronal de segmentation sémantique d’un document de texte complexe.

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Information Extraction in Legal Documents

Layout Analysis and Document Structure

Multilingual Knowledge Transfer