Unsupervised Relation Extraction of In-Domain Data from Focused Crawls

Abstract

This thesis proposal approaches unsupervised relation extraction from web data, which is collected by crawling only those parts of the web that are from the same domain as a relatively small reference corpus. The first part of this proposal is concerned with the efficient discovery of web documents for a particular domain and in a particular language. We create a combined, focused web crawling system that automatically collects relevant documents and minimizes the amount of irrelevant web content. The collected web data is semantically processed in order to acquire rich in-domain knowledge. Here, we focus on fully unsupervised relation extraction by employing the extended distributional hypothesis. We use distributional similarities between two pairs of nominals based on dependency paths as context and vice versa for identifying relational structure. We apply our system for the domain of educational sciences by focusing primarily on crawling scientific educational publications in the web. We are able to produce promising initial results on relation identification and we will discuss future directions.

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