What if a machine could help you to make sense? XIP Dashboard demonstration honoured.
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 were presented 18 posters and then the conference audience chooser the sessions they wanted to participate in – and voted for the best one. And at the end of the day 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”.
The Xerox Research Centre Europe collaborates since 5 years with KMi's Hypermedia Discourse Group and Ágnes Sándor, XRCE Parsing & Semantics 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.