Graph Generation with a Focusing Lexicon

Mayanka Chandra Shekar, Joseph A. Cottam

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The major part of graph generation is deriving connections (links) between entities (nodes). In this paper, a novel technique RAKE-IDF is introduced for key-phrase extraction across multiple documents. There are two main parts to this work: First is a Contextual Lexicon Generation generation step (in this case, White Collar Crime Lexicon) and second is a text-to-graph generation process. The text-graph generation is an ensemble model of state of the art semantic role labeling models with our key-phrase extraction technique: RAKE-IDF. These two parts are combined by augmenting standard graph with lexicon-generation derived elements to ensure interesting components are represented.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
EditorsChaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4928-4931
Number of pages4
ISBN (Electronic)9781728108582
DOIs
StatePublished - Dec 2019
Event2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States
Duration: Dec 9 2019Dec 12 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

Conference

Conference2019 IEEE International Conference on Big Data, Big Data 2019
Country/TerritoryUnited States
CityLos Angeles
Period12/9/1912/12/19

Funding

We thank the DARPA Modeling Adversarial Activity (MAA) program for funding this project under contracts HR0011728117, HR001178235, and HR0011729374.

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