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- W2253068693 abstract "The present and future information needs of the society rely on the ability of computers to understand and manage knowledge. The lack of this mechanism explains the problems of knowledge driven systems to effectively perform tasks as question answering and machine reading. One of the biggest bottlenecks is the automatic knowledge acquisition problem. In the actual stage of development, it seems obvious that only semisupervised or unsupervised techniques can scale to deal with large corpora of natural language like the Web. The trend has evolved from populating a predefined ontology to expressing knowledge through either unconstrained relations or propositions. The arrival of new deep language processing technologies let us think that we can annotate large collections of text with accurate predicates that can be used to extracting knowledge from text without tying it to any predefined logical schema. On the other hand, it is not clear which tasks can harness this knowledge and how it can be done. This master’s thesis proposes a new method of knowledge capture and textual inference based on three cornerstones: (1) First, we develop a procedure to turn plain text into a graph based representation taking advantage of existing tools. (2) Second, we develop a proposition extraction system. (3) Lastly, we study an unsupervised method for correction of appositive dependencies, as an example of the textual inferences that the generated proposition store enables. In addition, we generate two useful resources for future tasks of natural language processing: A corpus of 7 million documents represented as semantically enriched graphs and a proposition store of semantic classes with 8 million instances of entity-class relations." @default.
- W2253068693 created "2016-06-24" @default.
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- W2253068693 date "2014-02-27" @default.
- W2253068693 modified "2023-09-24" @default.
- W2253068693 title "Knowledge capture and textual inference" @default.
- W2253068693 hasPublicationYear "2014" @default.
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