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That said: LexVec gives quite good results on word-relatedness for using only distributional knowledge, and only from Wikipedia at that. Adding ConceptNet might
by rspeer 10y ago
That said: LexVec gives quite good results on word-relatedness for using only distributional knowledge, and only from Wikipedia at that. Adding ConceptNet might give something that is more likely to be state-of-the-art.
- glup 10y ago...And just distributional knowledge makes it easy to train new models on domain-specific corpora, or new languages. Is it possible to do the same with ConceptNet? I generally find that expert-derived ontologies suffer from bad coverage of low frequency items, rigidly discrete relationships, and are usually limited to a single language. That said, they're vastly better than nothing for a lot of tasks (same goes for WordNet).
- rspeer 10y agoYou can retrain your distributional knowledge and keep your lexical knowledge. Moving to a new domain shouldn't mean you have to forget everything about what words mean and hope you manage to learn it again. The whole idea of Numberbatch is that a combination of distributional and lexical knowledge is much better than either one alone. BTW, ConceptNet is only partially expert-derived (much of it is crowd-sourced), aims not to be rigid like WordNet is, and is in a whole lot of languages. "Retraining" ConceptNet itself is a bit of a chore, but you can do it. That is, you can get the source [1], add or remove sources of data, and rebuild it. Meanwhile, if you wanted to retrain word2vec's Google News skip-gram vectors, you would have to get a machine learning job at Google. [1] https://github.com/commonsense/conceptnet5 https://github.com/commonsense/conceptnet5