A SVM-based Compound-word Recognition Method in Information Security

With the emergence of mobile Internet, Internet of things and cloud computing, the domain of information security is in a rapid development. As a result, a constant stream of compound-words describing new concepts and new technologies has arisen. However, the existing dictionary does not collect those new compound-words in time, so it cannot identify them correctly. In order to solve this problem, this paper presents a SVM-based compound-word recognition method in information security. The method is based on the outputs of the existing word segmentation system. It constructs adjacent atom-word digraph according to the statistical co-occurrence features and lexical rules. Next, it produces compound-word candidate set through deep traverse the digraph by the longest match principle. It further filters the candidate set by using a SVM classifier with the help of domain contrast corpus and computer dictionary. We use this method to identify new compound-words from 2200 vulnerability description texts. It achieves a precision of 82.25% and recall of 77.44%. The results show that our method is able to effectively identify new compound-words in information security from large scale of corpus.