Unsupervised conceptualization and semantic text indexing for information extraction

Abstract

The goal of my thesis is the extension of the Distributional Hypothesis [13] from the word to the concept level. This will be achieved by creating data-driven methods to create and apply conceptualizations, taxonomic semantic models that are grounded in the input corpus. Such conceptualizations can be used to disambiguate all words in the corpus, so that we can extract richer relations and create a dense graph of semantic relations between concepts. These relations will reduce sparsity issues, a common problem for contextualization techniques. By extending our conceptualization with named entities and multi-word entities (MWE), we can create a Linked Open Data knowledge base that is linked to existing knowledge bases like Freebase.