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We propose ClausIE, a novel, clause-based approach to open information extraction, which extracts relations and their arguments from natural language text. ClausIE fundamentally differs from previous approaches in that it separates the detection of ``useful'' pieces of information expressed in a sentence from their representation in terms of extractions. In(More)
We propose FINET, a system for detecting the types of named entities in short inputs—such as sentences or tweets—with respect to WordNet's super fine-grained type system. FINET generates candidate types using a sequence of multiple extrac-tors, ranging from explicitly mentioned types to implicit types, and subsequently selects the most appropriate using(More)
Markov logic is apowerful tool for handling the uncertainty that arises in real-world structured data; it has been applied successfully to anum-bero fd ata managementp roblems. In practice, the resulting ground Markov logic networks can get very large, whichposes challenges to scalable inference. In this paper, we presentt he first fully parallelized(More)
We propose CORE, a novel matrix fac-torization model that leverages contextual information for open relation extraction. Our model is based on factorization machines and integrates facts from various sources, such as knowledge bases or open information extractors, as well as the context in which these facts have been observed. We argue that integrating(More)
Word-sense recognition and disambigua-tion (WERD) is the task of identifying word phrases and their senses in natural language text. Though it is well understood how to disambiguate noun phrases, this task is much less studied for verbs and verbal phrases. We present Werdy, a framework for WERD with particular focus on verbs and verbal phrases. Our(More)
Natural language text has been the main and most comprehensive way of expressing and storing knowledge. A long standing goal in computer science is to develop systems that automatically understand textual data, making this knowledge accessible to computers and humans alike. We conceive automatic text understanding as a bottom-up approach, in which a series(More)
1 Introduction Recency content has become a critical issue in information retrieval. Efficient retrieval of fresh and relevant documents has not been fully overcome yet and it is an increasing topic of interest in academic and commercial research. So far, web search engines manage to deal reasonable well with the classical Navigational, Transactional and(More)
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