This paper describes a robust finite-state based parser applied to French. The non-deterministic tokeniser includes a finite-state automaton for simple tokens and a lexical transducer for encoding a wide variety of multiword expressions. The lexicon attaches morpho-syntactic tags to each token and alternative clause boundaries inbetween. The parser can parse technical manuals with high accuracy: in a test sample 95% of both functional and part-of-speech tags were correct. The average number of parses per sentence is low, more than 92% of sentences produce four or less than four parses, including the correct one.
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Finite-State Based Reductionist Parsing for French.
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| Jean-Pierre Chanod, Pasi Tapanainen |
| Andréas Kornai (ed): Extended Finite State Models of Language. Cambridge University Press. |
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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