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Tech Report kmi-04-08 Abstract


Indexing Student Essays Paragraphs Using LSA Over an Integrated Ontological Space
Techreport ID: kmi-04-08
Date: 2004
Author(s): Gaston G Burek, Maria Vargas-Vera, Emanuela Moreale
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A full understanding of text is out of reach of current human language technology. However, a shallow Natural Language Processing (NLP) approach can be used to provide automated help in the evaluation of essays. The main idea of this paper is that Latent Semantic Indexing (LSA) can be used in conjunction with ontologies and First order Logic (FOL) to locate segments relevant to a question in a student essay. Our test bed, in a first instance, is a set of ontologies such the AKT reference ontology (describing academic life), Newspaper and a Koala ontology (concerning koalas' habitat).

Publication(s):

This paper will be published in the workshop (eLearning for Computational Linguistics and Computational Linguistics for eLearning International Workshop in Association with COLING 2004) proceedings within The 20th International Conference on Computational Linguistics, Geneva, August 28th, 2004.
 
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Multimedia and Information Systems is...


Multimedia and Information Systems
Our research is centred around the theme of Multimedia Information Retrieval, ie, Video Search Engines, Image Databases, Spoken Document Retrieval, Music Retrieval, Query Languages and Query Mediation.

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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