Integrating Human & Machine Document Annotation for Sensemaking
This event took place on Thursday 11 November 2010 at 14:30
Dr Simon Buckingham Shum
Simon Buckingham Shum, Ágnes Sándor, Anna De Liddo & Michelle Bachler
We report on progress made during the collaboration between KMi's Hypermedia Discourse Group and Ágnes Sándor (Xerox Research Centre Europe, Parsing & Semantics Group). This is the outcome of her 6 week OLnet Project Expert Fellowship at the OU, funded by the Hewlett Foundation, to develop Collective Intelligence for the Open Educational Resources (OER) community.
Our research investigates the overlaps and complementarities between the outputs from human analysts making sense of 120 OER project reports, using KMi's Cohere semantic annotation and knowledge mapping tool, and machine annotation of the corpus by the Xerox Incremental Parser (XIP). XIP's output is imported into Cohere to explore ways to visualize the combined human+machine output, and we present preliminary results from interviews with some of the analysts to elicit their views on XIP's annotations.
PDF verson of the slides available here.
This event took place on Thursday 11 November 2010 at 14:30
Simon Buckingham Shum, Ágnes Sándor, Anna De Liddo & Michelle Bachler
We report on progress made during the collaboration between KMi's Hypermedia Discourse Group and Ágnes Sándor (Xerox Research Centre Europe, Parsing & Semantics Group). This is the outcome of her 6 week OLnet Project Expert Fellowship at the OU, funded by the Hewlett Foundation, to develop Collective Intelligence for the Open Educational Resources (OER) community.
Our research investigates the overlaps and complementarities between the outputs from human analysts making sense of 120 OER project reports, using KMi's Cohere semantic annotation and knowledge mapping tool, and machine annotation of the corpus by the Xerox Incremental Parser (XIP). XIP's output is imported into Cohere to explore ways to visualize the combined human+machine output, and we present preliminary results from interviews with some of the analysts to elicit their views on XIP's annotations.
PDF verson of the slides available here.
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We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.
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