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Part of Speech Tagging for French Social Media Data
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| Farhad Nooralahzadeh, Caroline Brun, Claude Roux |
| Coling, Dublin, Ireland, August 23-29, 2014. |
In the context of Social Media Analytics, Natural Language Processing tools face new challenges on on-line conversational text, such as microblogs, chat, or text messages, because of the specificity of the language used in these channels. This work addresses the problem of Part-Of-Speech tagging (initially for French but also for English) on noisy language usage from the popular social media services like Twitter, Facebook and forums. We employ a linear-chain conditional random fields (CRFs) model, enriched with several morphological, orthographic, lexical and large-scale word clustering features. Our experiments used different feature configurations to train the model. We achieved a higher tagging performance with these features, compared to baseline results on French social media bank. Moreover, experiments on English social media content show that our model improves over previous works on these data.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
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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