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Views: 5759 Artificial Intelligence - All in One
See how PoolParty's taxonomy management methodology (https://www.poolparty.biz) is now supported even more efficiently by PoolParty's latest release. We demonstrate how PoolParty 5.2 makes use of deep text mining including corpus analysis and co-occurrence analysis. We show an example based on UNESCO world heritage sites and demonstrate how automatic classification can be extended step-by-step. An immediate feedback is given by PoolParty's faceted GraphSearch. Initial taxonomies can be built by using PoolParty's linked data harvester to fetch data from DBpedia.
Views: 964 PoolParty Semantic Suite
Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 7080 nptelhrd
An animation of the word sense disambiguation algorithm described in Navigli & Lapata (2010). The algorithm is trying to disambiguate the senses of the words in the sentence "Drink the milk." The algorithm starts off by creating two graphs: one large graph (not shown) of the entirety of WordNet, where each vertex is a synset and each edge is a semantic relation between synsets; and a "disambiguation" subgraph (depicted here) containing only the vertices for the synsets of "drink" and "milk". Then it does a depth-first search starting from each of these synsets in the original WordNet graph, looking for any of the other synsets. Once it finds them, it adds the path and the intermediate vertices to the disambiguation graph. Once the search is over, the degree of each vertex is calculated. The synsets for "drink" and "milk" with the highest degree are selected as the correct ones.
Views: 1095 Tristan Miller