Tech Reports
Tech Report kmi-04-12 Abstract
ESpotter: Adaptive Named Entity Recognition for Web Browsing
Techreport ID: kmi-04-12
Date: 2004
Author(s): Jianhan Zhu, Victoria Uren, Enrico Motta
Web users are facing information overload problems, i.e., it is hard for them to find desired information on the web. Hence the growing interest in named entity recognition (NER) for discovering relevant information on users behalf. We present a browser plug-in called ESpotter which adapts lexicons and patterns to a domain hierarchy consisting of domains on the web and user preferences for accurate and efficient NER. Mappings are created from domain independent types to domain specific types. Entities are highlighted according to their types, and users are assisted by navigational functionalities between these highlighted entities.
Future Internet
KnowledgeManagementMultimedia &
Information SystemsNarrative
HypermediaNew Media SystemsSemantic Web &
Knowledge ServicesSocial Software
Multimedia and Information Systems is...

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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