Eugenio Martínez-Cámara

Also published as: Eugenio Martinez Camara, Eugenio Martínez Cámara


2022

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TweetNLP: Cutting-Edge Natural Language Processing for Social Media
Jose Camacho-collados | Kiamehr Rezaee | Talayeh Riahi | Asahi Ushio | Daniel Loureiro | Dimosthenis Antypas | Joanne Boisson | Luis Espinosa Anke | Fangyu Liu | Eugenio Martínez Cámara
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

In this paper we present TweetNLP, an integrated platform for Natural Language Processing (NLP) in social media. TweetNLP supports a diverse set of NLP tasks, including generic focus areas such as sentiment analysis and named entity recognition, as well as social media-specific tasks such as emoji prediction and offensive language identification. Task-specific systems are powered by reasonably-sized Transformer-based language models specialized on social media text (in particular, Twitter) which can be run without the need for dedicated hardware or cloud services. The main contributions of TweetNLP are: (1) an integrated Python library for a modern toolkit supporting social media analysis using our various task-specific models adapted to the social domain; (2) an interactive online demo for codeless experimentation using our models; and (3) a tutorial covering a wide variety of typical social media applications.

2018

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SINAI at IEST 2018: Neural Encoding of Emotional External Knowledge for Emotion Classification
Flor Miriam Plaza-del-Arco | Eugenio Martínez-Cámara | Maite Martin | L. Alfonso Ureña- López
Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

In this paper, we describe our participation in WASSA 2018 Implicit Emotion Shared Task (IEST 2018). We claim that the use of emotional external knowledge may enhance the performance and the capacity of generalization of an emotion classification system based on neural networks. Accordingly, we submitted four deep learning systems grounded in a sequence encoding layer. They mainly differ in the feature vector space and the recurrent neural network used in the sequence encoding layer. The official results show that the systems that used emotional external knowledge have a higher capacity of generalization, hence our claim holds.

2017

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A Consolidated Open Knowledge Representation for Multiple Texts
Rachel Wities | Vered Shwartz | Gabriel Stanovsky | Meni Adler | Ori Shapira | Shyam Upadhyay | Dan Roth | Eugenio Martinez Camara | Iryna Gurevych | Ido Dagan
Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics

We propose to move from Open Information Extraction (OIE) ahead to Open Knowledge Representation (OKR), aiming to represent information conveyed jointly in a set of texts in an open text-based manner. We do so by consolidating OIE extractions using 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 give semantic applications an easy handle on consolidated information across multiple texts.

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LSDSem 2017: Exploring Data Generation Methods for the Story Cloze Test
Michael Bugert | Yevgeniy Puzikov | Andreas Rücklé | Judith Eckle-Kohler | Teresa Martin | Eugenio Martínez-Cámara | Daniil Sorokin | Maxime Peyrard | Iryna Gurevych
Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics

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.

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Neural Disambiguation of Causal Lexical Markers Based on Context
Eugenio Martínez-Cámara | Vered Shwartz | Iryna Gurevych | Ido Dagan
IWCS 2017 — 12th International Conference on Computational Semantics — Short papers

2015

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SINAI: Syntactic Approach for Aspect-Based Sentiment Analysis
Salud M. Jiménez-Zafra | Eugenio Martínez-Cámara | M. Teresa Martín-Valdivia | L. Alfonso Ureña-López
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

2014

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SINAI: Voting System for Aspect Based Sentiment Analysis
Salud María Jiménez-Zafra | Eugenio Martínez-Cámara | Maite Martin | L. Alfonso Ureña-López
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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SINAI: Voting System for Twitter Sentiment Analysis
Eugenio Martínez-Cámara | Salud María Jiménez-Zafra | Maite Martin | L. Alfonso Ureña-López
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

2013

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Bilingual Experiments on an Opinion Comparable Corpus
Eugenio Martínez-Cámara | M. Teresa Martín-Valdivia | M. Dolores Molina-González | L. Alfonso Ureña-López
Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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SINAI: Machine Learning and Emotion of the Crowd for Sentiment Analysis in Microblogs
Eugenio Martínez-Cámara | Arturo Montejo-Ráez | M. Teresa Martín-Valdivia | L. Alfonso Ureña-López
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

2012

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Random Walk Weighting over SentiWordNet for Sentiment Polarity Detection on Twitter
Arturo Montejo-Ráez | Eugenio Martínez-Cámara | M. Teresa Martín-Valdivia | L. Alfonso Ureña-López
Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis