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

Internships

Education

Scholarships

Publications

LSDSem 2017: Exploring Data Generation Methods for the Story Cloze Test

Author Michael Bugert, Yevgeniy Puzikov, Andreas Rücklé, Judith Eckle-Kohler, Teresa Martin, Eugenio Martínez Cámara, Daniil Sorokin, Maxime Peyrard, Iryna Gurevych
Date April 2017
Kind Inproceedings
PublisherAssociation for Computational Linguistics
Book titleProceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics (LSDSem, held in conjunction with EACL2017)
Pages56-61
LocationValencia, Spain
ISBN978-1-945626-40-1
KeyTUD-CS-2017-0040
Research Areas UKP_p_DIP, UKP_p_QAEduInf, AIPHES, UKP_reviewed, Ubiquitous Knowledge Processing, UKP_a_DLinNLP, UKP_a_LSRA
Abstract The Story Cloze test is a recent effort in providing a common test scenario for text understanding systems. As part of the LSDSem 2017 shared task, we present a system based on a deep learning architecture combined with a rich set of manually-crafted linguistic features. The system outperforms all known baselines for the task, suggesting that the chosen approach is promising. We additionally present two methods for generating further training data based on stories from the ROCStories corpus. Our system and generated data are publicly available on GitHub.
Website https://github.com/UKPLab/lsdsem2017-story-cloze
Full paper (pdf)
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