Thomson Reuters Enterprise Centre Gmbh

Thomson Reuters Global Resources

Other

Refinitiv US Organization LLC

Thornson Reuters Global Resources Unlimited Company

Blake Howald

Frank Schilder

Berk Ekmekci

Ravi Kumar Kondadadi

Eleanor Hagerman

Spencer Bradley Torene

Jochen Lothar Leidner

Isabelle Moulinier

Jason A Thomas

Dezhao Song

Lucas Carstens

James Tuttle

Zach Seid

Hannah Lensing

Casey Fallin

William Garcia

Nathan Maynes

John Ohle

Matt Machado

Andrew Mushett

Krzysztof Szymanski

Chris Schultz

Andrew Nystrom

Aspects of the present disclosure provide systems, methods, apparatus, and computer-readable storage media for determining semantic relationships between hashtags in social media messages, particularly for use in generating a hierarchical ontology of hashtags based on co-occurrence frequency and diversity metrics. For example, natural language processing may be performed on a plurality of social media messages to extract hashtags from the social media messages. Co-occurrence frequency counts for at least two hashtags and other hashtags, in addition to an ensemble score based on a combination of one or more diversity metrics, may be determined. A hierarchical ontology may be generated based on the co-occurrence frequency counts and the ensemble scores for the at least two hashtags. Such a hierarchical ontology may group hashtags into communities of common topics that are ordered based on ensemble scores.

System and method for automated hashtag hierarchical ontology generation from social media data

The present disclosure includes systems, apparatuses, and methods for event identification. In some aspects, a method includes receiving data including text and performing natural language processing on the received data to generate processed data that indicates one or more sentences. The method also includes generating, based on a first keyword set, a second keyword set having more keywords than the first keyword set. The method further includes, for each of the first and second keyword sets: detecting one or more keywords and one or more entities included in the processed data, determining one or more matched pairs based on the detected keywords and entities, and extracting a sentence, such as a single sentence or multiple sentences, from a document based on the one or more sentences indicated by the processed data. The method may also include outputting at least one extracted sentence.

Systems and methods for identifying an event in data

Computer implemented methods are provided for facilitating social engineering campaigns. The methods generally include maintaining at least one database that includes metadata for a plurality of fictional personas, metadata for a plurality of potential targets, information for a plurality of social engineering campaigns, such as a plurality of campaign scenarios, and information regarding social engineering successes with respect to at least one fictional persona, at least one potential target, at least one social engineering campaign, or a combination thereof. The method further includes receiving a selection of a campaign scenario from the plurality of campaign scenarios, receiving a selection of at least one of the plurality of potential targets, and selecting at least one of the plurality of fictional personas for use in a social engineering campaign based on an expected effectiveness of the given fictional personas with respect to the selected targets.

System and method for conducting social engineering red team campaigns

The present invention relates to a computer-based system for identifying supply chain risks and generating supply chain graphs representing an interconnected network of entities. An industrial graph database application is configured to account for direct and indirect (transitive) supplier risk and importance, based on a weighted set of measures: criticality, replaceability, centrality and distance. A graph-based model serves as an interactive and visual supply chain risk and importance explorer. A supply network is induced from textual data by applying text mining techniques to news stories and used to populate the supply chain/graph database.

Risk identification engine and supply chain graph generator

In some aspects, a method includes extracting sentences from data corresponding to documents. Each extracted sentence includes at least one matched pair (a keyword from a first or second keyword set and an entity from an entity set). The method includes ordering the plurality of extracted sentences based on a distance between a respective keyword and a respective entity in each extracted sentence. The method includes identifying a first type and a second type of extracted sentences from the ordered plurality of extracted sentences. Sentences having the first type include keywords of the first keyword set. Sentences having the second type include keywords of the second keyword set. The method includes generating an extracted summary including at least one sentence having the first type and at least one sentence having the second type, intermixed based on a predetermined order rule set. The method includes outputting the extracted summary.

Systems and methods for event summarization from data

The present invention relates to a system, method and medium for associating portions of advocational documents with portions of tribunal decisions in view of common or similar characteristics that are identified between the associated entities. In addition, the associated advocational document portions are imparted with certain characteristics resulting from such an association, such as inheriting the topic of the associated tribunal decision portion or inheriting general characteristics of the decision such as judge or jurisdiction. This allows for the subsequent retrieval of advocational documents in view of various criteria associated with a decision or portion thereof.

System and method for implementation and operation of strategic linkages

The present invention relates to a system and method for bootstrapping templates for use in natural language sentence generation. More specifically, the present invention relates to identifying a set of candidate sentences from a large corpus based on a set of original templates by using a similarity measure. The set of candidate sentences are then processed or cleaned to generate a set of templates for use in natural language sentence generation.

Template bootstrapping for domain-adaptable natural language generation

A method includes receiving a corpus comprising a set of pre-segmented texts. The method further includes creating a plurality of modified pre-segmented texts for the set of pre-segmented texts by extracting a set of semantic terms for each pre-segmented text within the set of pre-segmented texts and applying at least one domain tag for each pre-segmented text within the set of pre-segmented texts. The method further includes clustering the plurality of modified pre-segmented texts into one or more conceptual units, wherein each of the one or more conceptual units is associated with one or more templates, wherein each of the one or more templates corresponds to one of the plurality of modified pre-segmented texts.

Systems and methods for natural language generation

The present invention provides a method and system directed to predicting implicit rhetorical relations between two spans of text, e.g., in a large annotated corpus, such as the Penn Discourse Treebank ('PDTB'), Rhetorical Structure Theory corpus, and the Discourse Graph Bank, and particularly directed to determining a rhetorical relation in the absence of an explicit discourse marker. Surface level features may be used to capture pragmatic information encoded in the absent marker. In one manner a simplified feature set based only on raw text and semantic dependencies is used to improve performance for all relations. By using surface level features to predict implicit rhetorical relations for the large annotated corpus the invention approaches a theoretical maximum performance, suggesting that more data will not necessarily improve performance based on these and similarly situated features.

Method and system for simplifying implicit rhetorical relation prediction in large scale annotated corpus

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Northfield, MN, United States of America

Blake Howald