Incorporating Domain Knowledge in Learning Word Embedding 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 highquality vectors. In this paper, we describe a novel method, called Annotation Word Embedding (AWE), to train domainspecific word embeddings from sparse texts. Our method is generic and can leverage diverse types of domain knowledge such as domain vocabulary, semantic relations and attribute specifications. Specifically, our method encodes diverse types of domain knowledge as text annotations and incorporates the annotations in word embedding. We have evaluated AWE in two cybersecurity applications: identifying malware aliases and identifying relevant Common Vulnerabilities and Exposures (CVEs). Our evaluation results have demonstrated the effectiveness of our method over stateoftheart baselines.

LowResource Text Classification Using DomainAdversarial Learning Deep learning techniques have recently shown to be successful in many natural language processing tasks forming stateoftheart systems. They require, however, a large amount of annotated data which is often missing. This paper explores the use of domainadversarial learning as a regularizer to avoid overfitting when training domain invariant features for deep, complex neural network in lowresource and zeroresource settings in new target domains or languages. In case of new languages, we show that monolingual wordvectors can be directly used for training without prealignment. Their projection into a common space can be learnt adhoc at training time reaching the final performance of pretrained multilingual wordvectors.