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A great share of applications in modern information technology can benefit from large coverage, machine accessible knowledge bases. However, the bigger part of todays knowledge is provided in the form of unstructured data, mostly plain text. As an initial step to exploit such data, we present Wanderlust, an algorithm that automatically extracts semantic(More)
Semantic role labeling (SRL) is crucial to natural language understanding as it identifies the predicate-argument structure in text with semantic labels. Unfortunately, resources required to construct SRL models are expensive to obtain and simply do not exist for most languages. In this paper, we present a two-stage method to enable the construction of SRL(More)
The use of deep syntactic information such as typed dependencies has been shown to be very effective in Information Extraction. Despite this potential, the process of manually creating rule-based information extractors that operate on dependency trees is not intuitive for persons without an extensive NLP background. In this system demonstration, we present(More)
Unsupervised Relation Extraction (URE) is the task of extracting relations of a priori unknown semantic types using clustering methods on a vector space model of entity pairs and patterns. In this paper, we show that an informed feature generation technique based on dependency trees significantly improves clustering quality, as measured by the F-score, and(More)
Unsupervised Relation Extraction (URE) methods automatically discover semantic relations in text corpora of unknown content and extract for each discovered relation a set of relation instances. Due to the sparsity of the feature space, URE is vulnerable to ambiguities and underspecification in patterns. In this paper, we propose to increase the(More)
The WIKIA project maintains wikis across a diverse range of subjects from areas of popular culture. Each wiki consists of collaboratively authored content and focuses on a particular topic, including franchises such as “Star Trek”, “Star Wars” and “The Simpsons”. In this paper, we investigate the use of such wikis to create Question-Answering (QA) systems(More)