Using GloVe vectors in Gensim

Natural Language Processing (NLP) is a messy and difficult affair to handle. Word embeddings/representations – ever since they came in with great work of Mikolov et al, they have been revolutionary to say the least. The concept itself is very intuitive and motivates deeper understanding fora wide range of applications. The main advantage of the distributed representations is that similar words are close in the vector space, which makes generalization to novel patterns easier and model estimation more robust. Distributed vector representation is showed to be useful in many natural language processing applications such as Named Entity Recognition (NER), Word Sense Disambiguation (WSD), parsing, tagging and machine translation.