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What if a machine could help you to make sense? XIP Dashboard demonstration honoured.
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The Best Demonstration Award of the Learning Analytics and Knowledge Conference went to Duygu Simsek, Simon Buckingham Shum, Anna de Liddo, Rebecca Fergusson and Ágnes Sándor for their work: XIP Dashboard – Visual Analytics of Academic Writing.
March 26th, 2014. At the firehose kick-off event of the LAK 2014 poster and demo session in Indianapolis, Indiana, USA 18 posters were presented. The conference audience chose the sessions they wanted to participate in and voted for the best one. The XIP Dashboard demonstration received the coveted award!
Duygu Simsek, doctoral student at The Knowledge Media Institute (KMi) presented the associated poster: “Learning Analytics for Scaffolding Academic Writing through Automatic Identification of Meta-discourse”.
Ágnes Sándor in the XRCE Parsing & Semantics Group has been collaborating for 5 years with KMi’s Hypermedia Discourse Group. The team uses KMi’s Cohere semantic annotation and knowledge mapping tool and the machine annotation of the corpus by the Xerox Incremental Parser (XIP). More information on this collaboration.
XIP Dashboard: visual analytics from automated rhetorical parsing of scientific metadiscourse
A key competency that we seek to build in learners is a critical mind, i.e. ability to engage with the ideas in the literature, and to identify when significant claims are being made in articles. The ability to decode such moves in texts is essential, as is the ability to make such moves in one’s own writing. Computational techniques for extracting them are becoming available, using Natural Language Processing (NLP) tuned to recognize the rhetorical signals that authors use when making a significant scholarly move.
The Xerox Incremental Parser (XIP) technology does linguistic analysis (parsing) on text. To do so it performs part-of-speech disambiguation, entity recognition and chunking to dependency grammars and extra-sentential processing. Major applications include contextual entity recognition, lexical and structural disambiguation, coreference resolution and, more globally, knowledge extraction.
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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