@Article{cmc.2020.010855,
AUTHOR = {Yongjin Hu, Yuanbo Guo, Junxiu Liu, Han Zhang},
TITLE = {A Hybrid Method of Coreference Resolution in Information Security},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {2},
PAGES = {1297--1315},
URL = {http://www.techscience.com/cmc/v64n2/39361},
ISSN = {1546-2226},
ABSTRACT = {In the field of information security, a gap exists in the study of coreference
resolution of entities. A hybrid method is proposed to solve the problem of coreference
resolution in information security. The work consists of two parts: the first extracts all 
candidates (including noun phrases, pronouns, entities, and nested phrases) from a given 
document and classifies them; the second is coreference resolution of the selected 
candidates. In the first part, a method combining rules with a deep learning model 
(Dictionary BiLSTM-Attention-CRF, or DBAC) is proposed to extract all candidates in 
the text and classify them. In the DBAC model, the domain dictionary matching 
mechanism is introduced, and new features of words and their contexts are obtained 
according to the domain dictionary. In this way, full use can be made of the entities and 
entity-type information contained in the domain dictionary, which can help solve the 
recognition problem of both rare and long entities. In the second part, candidates are 
divided into pronoun candidates and noun phrase candidates according to the part of 
speech, and the coreference resolution of pronoun candidates is solved by making rules 
and coreference resolution of noun phrase candidates by machine learning. Finally, a 
dataset is created with which to evaluate our methods using information security data. 
The experimental results show that the proposed model exhibits better performance than 
the other baseline models.},
DOI = {10.32604/cmc.2020.010855}
}