Personal Information

Name
Dr. Eugenio Martínez Cámara
Position
Postdoctoral Researcher 
Affiliation
UKP-TUDA
E-Mail
camara(at)ukp.informatik.tu-darmstadt.de
Phone

+49 (6151) 16 - TBA

Fax
+49 (6151) 16 - 25295
Office
S2|02 B106
Address

TU Darmstadt - FB 20 
Hochschulstraße 10 
64289 Darmstadt
Germany

Web
ResearchGoogle Scholar profile
ORCID (0000-0002-5279-8355)
Scopus (41762106100)
ResearchId (C5539-2014)
ResearchGate
DevelopmentGitHub
ProfessionalLinkedIn

Research Interests

  • Natural Language Engineering
  • Sentiment Analysis
  • Semantic Analysis
  • Knowledge Representation
  • Text classification
  • Machine Learning

Biographical Information

Currently I am a post-doctoral researcher at the UKP-TUDA research group at the TU-Darmstadt. I hold a Bachelor in Computer Sciene and Mangement from the University of Jaén (Spain) in 2008. I received a M.S. degree in Computer Science from t he University of Jaén (Spain) in 2010. In October 2015, I successfully defended my PhD thesis, with the title  "Sentiment Analysis in Spanish", from the University of Jaén (Spain).

Employment

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Education

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Publications

A Consolidated Open Knowledge Representation for Multiple Texts

Author Rachel Wities, Vered Shwartz, Gabriel Stanowsky, Meni Adler, Ori Shapira, Shyam Upadhyay, Dan Roth, Eugenio Martínez Cámara, Iryna Gurevych, Ido Dagan
Date April 2017
Kind Inproceedings
PublisherAssociation for Computational Linguistics
AddressValencia, Spain
Book titleProceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics
Pages12-24
LocationValencia
ISBN978-1-945626-40-1
KeyTUD-CS-2017-0049
Research Areas UKP_p_DIP, UKP_reviewed, Ubiquitous Knowledge Processing, UKP_a_LSRA
Abstract We propose progressing from Open Information Extraction (OIE) to Open Knowledge Representation (OKR), aiming to represent the information conveyed jointly in a set of texts in an open text-based manner. We do so by consolidating OIE extractions based on entity and predicate coreference, while modeling information containment between coreferring elements via lexical entailment. We suggest that generating OKR structures can be a useful step in the NLP pipeline, to get semantic applications an easy handle on consolidated information across multiple texts.
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