Using Constraints on a general Knowledge lexical networK for domain-specific semantic relation extraction and modeling

Abstract : We introduce a pattern-based approach applied to the semantic relation retrieval and semantic modeling. Our method relies upon the use of a general knowledge lexical semantic network built, shaped, and handled by crowd-sourcing and GWAPs (games with a purpose). Implementing constraints on semantic relations available in the network increases the efficiency of the relation extraction process but also opens a semantic modeling perspective. In terms of (mostly horizontal) relation extraction, we tested our method on radiology reports in French. Our results show the interest of using a general knowledge lexical semantic network for the domain specific textual analysis as well as the interest of implementing series of constraints on semantic relations for the relation retrieval. We recently turned to the analysis of cooking recipes that stand for examples of domain specific instructional texts. Thus, in addition to the semantic relation discovery, we are building a method for the semantic modeling and conceptualization of cooking instructions. Its first results are presented below. Today, our results are available for French but we target extending the lexical network coverage to other languages in the next few years.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01471663
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Nadia Bebeshina-Clairet, Lionel Ramadier, Mathieu Lafourcade. Using Constraints on a general Knowledge lexical networK for domain-specific semantic relation extraction and modeling. DIALOGUE, Jun 2016, Moscou, Russia. ⟨lirmm-01471663⟩

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