Wisdom of Crowds vs. Wisdom of Linguists
This event took place on Wednesday 08 December 2010 at 11:30
Dr. Torsten Zesch Ubiquitous Knowledge Processing lab, TU Darmstadt, Germany
Computing the semantic relatedness between words is a pervasive task in natural language processing. So far, insufficient coverage of linguistic knowledge resources has been a major impediment for using semantic relatedness measures in large-scale applications. Recently, rapidly growing collaboratively constructed resources like Wikipedia and Wiktionary have been discovered as a new kind of semantic resource.
In the talk, I will shortly introduce these new resources and show how existing semantic relatedness measures can be adapted to the new resources. I will then compare the performance of traditional resources (Wisdom of Linguists) with that of the new resources (Wisdom of Crowds), and show under which conditions collaboratively constructed semantic resources can be used as a proxy for linguistically constructed semantic resources.
Additionally, I will introduce freely available application programming interfaces to Wikipedia and Wiktionary that have been used to conduct the experiments described in my talk.
This event took place on Wednesday 08 December 2010 at 11:30
Computing the semantic relatedness between words is a pervasive task in natural language processing. So far, insufficient coverage of linguistic knowledge resources has been a major impediment for using semantic relatedness measures in large-scale applications. Recently, rapidly growing collaboratively constructed resources like Wikipedia and Wiktionary have been discovered as a new kind of semantic resource.
In the talk, I will shortly introduce these new resources and show how existing semantic relatedness measures can be adapted to the new resources. I will then compare the performance of traditional resources (Wisdom of Linguists) with that of the new resources (Wisdom of Crowds), and show under which conditions collaboratively constructed semantic resources can be used as a proxy for linguistically constructed semantic resources.
Additionally, I will introduce freely available application programming interfaces to Wikipedia and Wiktionary that have been used to conduct the experiments described in my talk.
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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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