Search Results for author: Marc Verhagen

Found 21 papers, 1 papers with code

The CLAMS Platform at Work: Processing Audiovisual Data from the American Archive of Public Broadcasting

no code implementations LREC 2022 Marc Verhagen, Kelley Lynch, Kyeongmin Rim, James Pustejovsky

The Computational Linguistics Applications for Multimedia Services (CLAMS) platform provides access to computational content analysis tools for multimedia material.

Evaluating Retrieval for Multi-domain Scientific Publications

no code implementations LREC 2022 Nancy Ide, Keith Suderman, Jingxuan Tu, Marc Verhagen, Shanan Peters, Ian Ross, John Lawson, Andrew Borg, James Pustejovsky

This paper provides an overview of the xDD/LAPPS Grid framework and provides results of evaluating the AskMe retrievalengine using the BEIR benchmark datasets.

Retrieval

Exploration and Discovery of the COVID-19 Literature through Semantic Visualization

no code implementations NAACL 2021 Jingxuan Tu, Marc Verhagen, Brent Cochran, James Pustejovsky

We are developing semantic visualization techniques in order to enhance exploration and enable discovery over large datasets of complex networks of relations.

Knowledge Graphs TAG

Interchange Formats for Visualization: LIF and MMIF

no code implementations LREC 2020 Kyeongmin Rim, Kelley Lynch, Marc Verhagen, Nancy Ide, James Pustejovsky

Promoting interoperrable computational linguistics (CL) and natural language processing (NLP) application platforms and interchange-able data formats have contributed improving discoverabilty and accessbility of the openly available NLP software.

Data Visualization

Representation and Interchange of Linguistic Annotation. An In-Depth, Side-by-Side Comparison of Three Designs

no code implementations WS 2017 Richard Eckart de Castilho, Nancy Ide, Emanuele Lapponi, Stephan Oepen, Keith Suderman, Erik Velldal, Marc Verhagen

We expect that a more in-depth understanding of these choices across designs may led to increased harmonization, or at least to more informed design of future representations.

LAPPS/Galaxy: Current State and Next Steps

no code implementations WS 2016 Nancy Ide, Keith Suderman, Eric Nyberg, James Pustejovsky, Marc Verhagen

The US National Science Foundation (NSF) SI2-funded LAPPS/Galaxy project has developed an open-source platform for enabling complex analyses while hiding complexities associated with underlying infrastructure, that can be accessed through a web interface, deployed on any Unix system, or run from the cloud.

The Language Application Grid and Galaxy

no code implementations LREC 2016 Nancy Ide, Keith Suderman, James Pustejovsky, Marc Verhagen, Christopher Cieri

The NSF-SI2-funded LAPPS Grid project is a collaborative effort among Brandeis University, Vassar College, Carnegie-Mellon University (CMU), and the Linguistic Data Consortium (LDC), which has developed an open, web-based infrastructure through which resources can be easily accessed and within which tailored language services can be efficiently composed, evaluated, disseminated and consumed by researchers, developers, and students across a wide variety of disciplines.

Management

The Language Application Grid

no code implementations LREC 2014 Nancy Ide, James Pustejovsky, Christopher Cieri, Eric Nyberg, Di Wang, Keith Suderman, Marc Verhagen, Jonathan Wright

The Language Application (LAPPS) Grid project is establishing a framework that enables language service discovery, composition, and reuse and promotes sustainability, manageability, usability, and interoperability of natural language Processing (NLP) components.

Machine Translation Question Answering +1

Identification of Technology Terms in Patents

no code implementations LREC 2014 Peter Anick, Marc Verhagen, James Pustejovsky

Natural language analysis of patents holds promise for the development of tools designed to assist analysts in the monitoring of emerging technologies.

BIG-bench Machine Learning

Clinical TempEval

no code implementations19 Mar 2014 Steven Bethard, Leon Derczynski, James Pustejovsky, Marc Verhagen

We describe the Clinical TempEval task which is currently in preparation for the SemEval-2015 evaluation exercise.

Relation

ATLIS: Identifying Locational Information in Text Automatically

no code implementations LREC 2012 John Vogel, Marc Verhagen, James Pustejovsky

ATLIS (short for “ ATLIS Tags Locations in Strings”) is a tool being developed using a maximum-entropy machine learning model for automatically identifying information relating to spatial and locational information in natural language text.

The TARSQI Toolkit

no code implementations LREC 2012 Marc Verhagen, James Pustejovsky

We present and demonstrate the updated version of the TARSQI Toolkit, a suite of temporal processing modules that extract temporal information from natural language texts.

Question Answering

SemEval-2010 Task 13: TempEval-2

no code implementations Proceedings of the 5th International Workshop on Semantic Evaluation 2010 Marc Verhagen, Roser Saurí, Tommaso Caselli, James Pustejovsky

Tempeval-2 comprises evaluation tasks for time expressions, events and temporal relations, the latter of which was split up in four sub tasks, motivated by the notion that smaller subtasks would make both data preparation and temporal relation extraction easier.

Event Extraction Relation +2

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