Learning Domain-Specific Word Embeddings from Sparse Cybersecurity Texts

Understand

Word embedding is a Natural Language Processing (NLP) technique that automatically maps words from a vocabulary to vectors of real numbers in an embedding space.

• It has been widely used in recent years to boost the performance of a vari-ety of NLP tasks such as Named Entity Recognition, Syntac-tic Parsing and Sentiment Analysis.
• Classic word embedding methods such as Word2Vec and GloVe work well when they are given a large text corpus.
• When the input texts are sparse as in many specialized domains (e.g., cybersecurity), these methods often fail to produce high-quality vectors.