- Negative examples are also needed to train a binary classifier.
- Skip-gram with negative sampling (SGNS) needs more negative samples for training.
- A parameter 'k' is used to keep the ratio of positive and negative samples.

Example continued....

- The figure below shows an example of training data, apricot being the target word and 'tablespoon ,of ,jam and a' being the context word.
- Context window is $ L = \pm 2 $.              

San Jose State University

- A corpus of text is taken as input for learning the skip-gram embeddings by the learning algorithm.
- A vocabulary of size N is also chosen.
- For each word skip-gram algorithm learns two embeddings target embedding and context embedding.

Learning skip-gram embeddings


An on-going but a helpful book resource about NLP
https://web.stanford.edu/~jurafsky/slp3/

Speech and Language Processing (3rd ed. draft) 

Skip-Gram Training data

From the given set of positive and negative training examples and initial weight for embeddings the goal of the learning algorithm is to learn to maximize the similarity of target word and context word from the positive examples and to minimize the similarity of target word and context word from the negative examples.

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Related

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