We evaluate with traditional approaches and conduct a human evaluation study with cybersecurity professionals and threat hunters.
The goal of this work is to improve the performance of a neural named entity recognition system by adding input features that indicate a word is part of a name included in a gazetteer.
We introduce the reinforcement quantum annealing (RQA) scheme in which an intelligent agent interacts with a quantum annealer that plays the stochastic environment role of learning automata and tries to iteratively find better Ising Hamiltonians for the given problem of interest.
Understanding and extracting of information from large documents, such as business opportunities, academic articles, medical documents and technical reports, poses challenges not present in short documents.
It uses multiple knowledge representations like, vector spaces and knowledge graphs in a 'VKG structure' to store incoming intelligence.
We present a family of novel methods for embedding knowledge graphs into real-valued tensors.
Judging the veracity of a sentence making one or more claims is an important and challenging problem with many dimensions.
KGCleaner is a framework to identify and correct errors in data produced and delivered by an information extraction system.
In scientific disciplines where research findings have a strong impact on society, reducing the amount of time it takes to understand, synthesize and exploit the research is invaluable.
Understanding large, structured documents like scholarly articles, requests for proposals or business reports is a complex and difficult task.
We describe the systems developed by the UMBC team for 2018 SemEval Task 8, SecureNLP (Semantic Extraction from CybersecUrity REports using Natural Language Processing).
no code implementations • • Benjamin Van Durme, Tom Lippincott, Kevin Duh, Deana Burchfield, Adam Poliak, Cash Costello, Tim Finin, Scott Miller, James Mayfield, Philipp Koehn, Craig Harman, Dawn Lawrie, Ch May, ler, Max Thomas, Annabelle Carrell, Julianne Chaloux, Tongfei Chen, Alex Comerford, Mark Dredze, Benjamin Glass, Shudong Hao, Patrick Martin, Pushpendre Rastogi, Rashmi Sankepally, Travis Wolfe, Ying-Ying Tran, Ted Zhang
It combines a multitude of analytics together with a flexible environment for customizing the workflow for different users.
A key contribution of our research is modeling the logical and semantic structure of an electronic document.
Knowledge graphs and vector space models are robust knowledge representation techniques with individual strengths and weaknesses.
Most work on building knowledge bases has focused on collecting entities and facts from as large a collection of documents as possible.