Compositional phrase embeddings

Pytroch implementation of skip-gram model to embed phrases compositionally.

For each phrase, individual phrase's words pass through an encoder (RNN/CNN/etc) to get a phrase embedding. Then we predict surrounding words from this phrase embedding.

Allows getting an embedding for every phrase that consists of known individual words.

• Exploring phrase-compositionality in skip-gram models (Compositional Phrase Embeddings)
• Distributed Representations of Words and Phrases and their Compositionality (Negative Sampling & Subsampling of the frequent words)
• Efficient Estimation of Word Representations in Vector Space (Skip-Gram model)

and also allows using RNNs and CNNs for compositional phrase embeddings

Uses negative sampling and allennlp DL4NLP library.