As advanced technologies such as data mining become part of the everyday workflow of document reviews in litigations, keyword-search still appears to serve as a cornerstone approach in responsive (or privilege) review. Keywords are conceptually easy to understand and help culling documents at the early stages of the review. But developing proper keywords to minimize the risk of under/overinclusiveness can lead to complex strategies. To cope with the burden of designing search terms, we propose to use semantic search in a dynamic manner. This paper describes a system leveraging semantic models in a smart review environment in order to support knowledge workers in eDiscovery.
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Experimenting Word Embeddings in Assisting Legal Review
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| Ngoc-Phuoc-An Vo, Fabien Guillot, Caroline Privault |
| ICAIL 2017 The 16th International Conference on Artificial Intelligence and Law, King's College London, London, UK, 12-16 June, 2017 |
@inproceedings{vo2017experimenting, title={Experimenting word embeddings in assisting legal review}, author={Vo, Ngoc Phuoc An and Privault, Caroline and Guillot, Fabien}, booktitle={Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law}, pages={189–198}, year={2017} }
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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