Title
Learning Domain-Specific Word Embeddings from Sparse Cybersecurity Texts.
Abstract
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. In this pa-per, we describe a novel method to train domain-specificword 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. Specifi-cally, we first propose a general framework to encode diverse types of domain knowledge as text annotations. Then we de-velop a novel Word Annotation Embedding (WAE) algorithm to incorporate diverse types of text annotations in word em-bedding. We have evaluated our method on two cybersecurity text corpora: a malware description corpus and a Common Vulnerability and Exposure (CVE) corpus. Our evaluation re-sults have demonstrated the effectiveness of our method in learning domain-specific word embeddings.
Year
Venue
Field
2017
arXiv: Computation and Language
Computer science,Computer security,Artificial intelligence,Natural language processing,Word embedding,Domain knowledge,Sentiment analysis,Text corpus,Word2vec,Parsing,Named-entity recognition,Vocabulary,Machine learning
DocType
Volume
Citations 
Journal
abs/1709.07470
0
PageRank 
References 
Authors
0.34
21
3
Name
Order
Citations
PageRank
arpita roy1144.39
Youngja Park221924.84
Shimei Pan368464.41