Learning Domain-Specific Word Embeddings from Sparse Cybersecurity Texts

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 variety of NLP tasks such as Named Entity Recognition, Syntactic 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. In this paper, we describe a novel method to train domain-specific word embeddings from sparse texts. In addition to domain texts, our method also leverages diverse types of domain knowledge such as domain vocabulary and semantic relations. Specifically, we first propose a general framework to encode diverse types of domain knowledge as text annotations. Then we develop a novel Word Annotation Embedding (WAE) algorithm to incorporate diverse types of text annotations in word embedding. We have evaluated our method on two cybersecurity text corpora: a malware description corpus and a Common Vulnerability and Exposure (CVE) corpus. Our evaluation results have demonstrated the effectiveness of our method in learning domain-specific word embeddings.