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An important problem in knowledge discovery from text is the automatic extraction of semantic relations. This paper presents a supervised, semantically intensive, domain independent approach for the automatic detection of part–whole relations in text. First an algorithm is described that identifies lexico-syntactic patterns that encode part–whole relations.(More)
This paper presents an approach for detecting semantic relations in noun phrases. A learning algorithm, called semantic scattering, is used to automatically label complex nominals, genitives and adjectival noun phrases with the corresponding semantic relation. 1 Problem description This paper is about the automatic labeling of semantic relations in noun(More)
This paper addresses the automatic classification of the semantic relations expressed by the English genitives. A learning model is introduced based on the statistical analysis of the distribution of gen-itives' semantic relations on a large corpus. The semantic and contextual features of the genitive's noun phrase constituents play a key role in the(More)
The discovery of semantic relations from text becomes increasingly important for applications such as Question Answering, Information Extraction, Text Summarization, Text Understanding , and others. The semantic relations are detected by checking selectional constraints. This paper presents a method and its results for learning semantic constraints to(More)
The retrorectal space represents the anatomical site at which level we identify the embryologic reminiscents in which it can develop liquid tumors - cysts or solid tumors - neoplasia. These tumors are rare but pose a diagnostic and therapeutic interest. This paper presents the case of a young 18 years-old diagnosed incidentally at a gynecological(More)
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