no code implementations • NAACL (HCINLP) 2022 • Mihai Surdeanu, John Hungerford, Yee Seng Chan, Jessica MacBride, Benjamin Gyori, Andrew Zupon, Zheng Tang, Haoling Qiu, Bonan Min, Yan Zverev, Caitlin Hilverman, Max Thomas, Walter Andrews, Keith Alcock, Zeyu Zhang, Michael Reynolds, Steven Bethard, Rebecca Sharp, Egoitz Laparra
An existing domain taxonomy for normalizing content is often assumed when discussing approaches to information extraction, yet often in real-world scenarios there is none. When one does exist, as the information needs shift, it must be continually extended.
no code implementations • EACL (AdaptNLP) 2021 • Timothy Miller, Egoitz Laparra, Steven Bethard
Advances in transfer learning and domain adaptation have raised hopes that once-challenging NLP tasks are ready to be put to use for sophisticated information extraction needs.
no code implementations • GWC 2016 • Roxane Segers, Egoitz Laparra, Marco Rospocher, Piek Vossen, German Rigau, Filip Ilievski
This paper presents the Event and Implied Situation Ontology (ESO), a resource which formalizes the pre and post situations of events and the roles of the entities affected by an event.
1 code implementation • SEMEVAL 2021 • Egoitz Laparra, Xin Su, Yiyun Zhao, {\"O}zlem Uzuner, Timothy Miller, Steven Bethard
Participants are then tested on data representing a new (target) domain.
2 code implementations • COLING 2020 • Egoitz Laparra, Steven Bethard
But creating a dataset for this complex geoparsing task is difficult and, if done manually, would require a huge amount of effort to annotate the geographical shapes of not only the geolocation described but also the reference toponyms.
no code implementations • SEMEVAL 2019 • Dongfang Xu, Egoitz Laparra, Steven Bethard
Recent studies have shown that pre-trained contextual word embeddings, which assign the same word different vectors in different contexts, improve performance in many tasks.
1 code implementation • NAACL 2019 • Rebecca Sharp, Adarsh Pyarelal, Benjamin Gyori, Keith Alcock, Egoitz Laparra, Marco A. Valenzuela-Esc{\'a}rcega, Ajay Nagesh, Vikas Yadav, John Bachman, Zheng Tang, Heather Lent, Fan Luo, Mithun Paul, Steven Bethard, Kobus Barnard, Clayton Morrison, Mihai Surdeanu
Building causal models of complicated phenomena such as food insecurity is currently a slow and labor-intensive manual process.
no code implementations • SEMEVAL 2019 • Vikas Yadav, Egoitz Laparra, Ti-Tai Wang, Mihai Surdeanu, Steven Bethard
We present the Named Entity Recognition (NER) and disambiguation model used by the University of Arizona team (UArizona) for the SemEval 2019 task 12.
no code implementations • WS 2019 • Steven Bethard, Egoitz Laparra, Sophia Wang, Yiyun Zhao, Ragheb Al-Ghezi, Aaron Lien, Laura L{\'o}pez-Hoffman
The National Environmental Policy Act (NEPA) provides a trove of data on how environmental policy decisions have been made in the United States over the last 50 years.
no code implementations • WS 2018 • Dane Bell, Egoitz Laparra, Aditya Kousik, Terron Ishihara, Mihai Surdeanu, Stephen Kobourov
This work explores the detection of individuals{'} risk of type 2 diabetes mellitus (T2DM) directly from their social media (Twitter) activity.
no code implementations • SEMEVAL 2018 • Egoitz Laparra, Dongfang Xu, Ahmed Elsayed, Steven Bethard, Martha Palmer
This paper presents the outcomes of the Parsing Time Normalization shared task held within SemEval-2018.
1 code implementation • TACL 2018 • Egoitz Laparra, Dongfang Xu, Steven Bethard
This paper presents the first model for time normalization trained on the SCATE corpus.
Ranked #1 on Timex normalization on PNT
no code implementations • 2 Feb 2017 • Egoitz Laparra, Rodrigo Agerri, Itziar Aldabe, German Rigau
In this paper we present an approach to extract ordered timelines of events, their participants, locations and times from a set of multilingual and cross-lingual data sources.
no code implementations • LREC 2016 • Roxane Segers, Marco Rospocher, Piek Vossen, Egoitz Laparra, German Rigau, Anne-Lyse Minard
This paper presents the Event and Implied Situation Ontology (ESO), a manually constructed resource which formalizes the pre and post situations of events and the roles of the entities affected by an event.
no code implementations • LREC 2016 • Maddalen Lopez de Lacalle, Egoitz Laparra, Itziar Aldabe, German Rigau
This paper presents the Predicate Matrix 1. 3, a lexical resource resulting from the integration of multiple sources of predicate information including FrameNet, VerbNet, PropBank and WordNet.
no code implementations • LREC 2014 • Maddalen Lopez de Lacalle, Egoitz Laparra, German Rigau
This paper presents the Predicate Matrix v1. 1, a new lexical resource resulting from the integration of multiple sources of predicate information including FrameNet, VerbNet, PropBank and WordNet.
no code implementations • LREC 2012 • Egoitz Laparra, German Rigau, Piek Vossen
This paper describes the connection of WordNet to a generic ontology based on DOLCE.
no code implementations • LREC 2012 • Aitor Gonzalez-Agirre, Egoitz Laparra, German Rigau
This paper describes the upgrading process of the Multilingual Central Repository (MCR).