This paper shows how taking document structure into account helps to improve the performance of linguistic parsing. We restrict our study to one specific structure in a single markup language : lists in HTML documents. First we establish a typology of lists based on a corpus study. Then, after describing a transformation process that creates documents with uniform list markup, we show how the list tags can be incorporated into a parsing system, and how they enhance performance on every level of parsing.
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Linguistic Parsing of Lists in Structured Documents
at
| Salah Ait-Mokhtar, Veronika Lux, Eva Banik |
| EACL Workshop on NLP and XML, Budapest, Hungrary, April 12-17, 2003. |
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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