Unsupervised Learning of Relation Detection Patterns

Edgar Gonzàlez i Pellicer


18 Maig 2012

11:00h - Presentació


Information extraction is the natural language processing area whose goal is to obtain structured data from the relevant information contained in textual fragments.

Information extraction requires a significant amount of linguistic knowledge. The specificity of such knowledge supposes a drawback on the portability of the systems, as a change of language, domain or style demands a costly human effort. Machine learning techniques have been applied for decades so as to overcome this portability bottleneck---progressively reducing the amount of involved human supervision. However, as the availability of large document collections increases, completely unsupervised approaches become necessary in order to mine the knowledge contained in them.

The proposal of this thesis is to incorporate clustering techniques into pattern learning for information extraction, in order to further reduce the elements of supervision involved in the process. In particular, the work focuses on the problem of relation detection. The achievement of this ultimate goal has required, first, considering the different strategies in which this combination could be carried out; second, developing or adapting clustering algorithms suitable to our needs; and third, devising pattern learning procedures which incorporated clustering information.

By the end of this thesis, we had been able to develop and implement an approach for learning of relation detection patterns which, using clustering techniques and minimal human supervision, is competitive and even outperforms other comparable approaches in the state of the art.

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