The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jul. 01, 2025

Filed:

Aug. 08, 2022
Applicant:

Nec Laboratories America, Inc., Princeton, NJ (US);

Inventors:

Xiao Yu, Princeton, NJ (US);

Yanchi Liu, Monmouth Junction, NJ (US);

Haifeng Chen, West Windsor, NJ (US);

Yufei Li, Dallas, TX (US);

Assignee:

NEC Corporation, Tokyo, JP;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 40/295 (2020.01); G06F 21/57 (2013.01); G06F 40/40 (2020.01);
U.S. Cl.
CPC ...
G06F 40/295 (2020.01); G06F 21/577 (2013.01); G06F 40/40 (2020.01); G06F 2221/034 (2013.01);
Abstract

Systems and methods are provided for adapting a pretrained language model to perform cybersecurity-specific named entity recognition and relation extraction. The method includes introducing a pretrained language model and a corpus of security text to a model adaptor, and generating a fine-tuned language model through unsupervised training utilizing the security text corpus. The method further includes combining a joint extraction model from a head for joint extraction with the fine-tuned language model to form an adapted joint extraction model that can perform entity and relation label prediction. The method further includes applying distant labels to security text in the corpus of security text to produce security text with distant labels, and performing Distant Supervision Training for joint extraction on the adapted joint extraction model using the security text to transform the adapted joint extraction model into a Security Language Model for name-entity recognition (NER) and relation extraction (RE).


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