SWE Toolkit. Learning Semantic Word Embeddings based on Ordinal Knowledge Constraints. A general framework to incorporate semantic knowledge into the popular data-driven learning process of word vectors. Applications including word similarity, sentence completion, etc. ACL-2015, Beijing, China
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How these set synon-anton, Hyper-Hypon extracted the ontology such as WordNet? #4
The idea is excellent!
1. Could you provide some clues how these set synon-anton,
Hyper-Hypon (e.g "../SWE.EN.KnowDB.WordNet-Book.Synon-Anton" ) extracted the ontology such as WordNet?
According to the Semantic Category Rule in your document, "A semantic category may be defined as a synset in WordNet, a hypernym in a semantic hierarchy, or an entity category in knowledge graphs." Is the synset used come from WordNet or get extracted by you?
2. Contrary to the example "similarity(Mallet, Hammer) > similarity(Mallet, Tool)" Does all words in knowledge graph need to be considered ?? e.g similarity(Mallet, Hammer) > similarity(Mallet, WordFaraway)
Thanks for your sharing!
lshowway
commented
5 years ago
The idea is excellent! 1. Could you provide some clues how these set synon-anton, Hyper-Hypon (e.g "../SWE.EN.KnowDB.WordNet-Book.Synon-Anton" ) extracted the ontology such as WordNet? According to the Semantic Category Rule in your document, "A semantic category may be defined as a synset in WordNet, a hypernym in a semantic hierarchy, or an entity category in knowledge graphs." Is the synset used come from WordNet or get extracted by you? 2. Contrary to the example "similarity(Mallet, Hammer) > similarity(Mallet, Tool)" Does all words in knowledge graph need to be considered ?? e.g similarity(Mallet, Hammer) > similarity(Mallet, WordFaraway) Thanks for your sharing!