- For those of you who aren’t familiar with them, word embeddings are essentially dense vector representations of words.
- Word embeddings can be trained and used to derive similarities and relations between words.
- Relations between words according to word embeddings
- Word2vec represents every word as an independent vector, even though many words are morphologically similar, just like our two examples above.
- If your model hasn’t encountered a word before, it will have no idea how to interpret it or how to build a vector for it.
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@KirkDBorne: “#DeepLearning techniques for #NLProc tasks: #abdsc #BigData #DataScience #MachineLearning”
In recent times deep learning techniques have become more and more prevalent in NLP tasks; just take a look at the list of accepted papers at this year’s NAAC…
The challenges of word embeddings