M. Teresa Martín-Valdivia

Also published as: M. Teresa Martín, Maite Martin, Maite Martín-Valdivia, Maria Teresa Martín-Valdivia, María Teresa Martín-Valdivia, Teresa Martin


2021

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SINAI at SemEval-2021 Task 5: Combining Embeddings in a BiLSTM-CRF model for Toxic Spans Detection
Flor Miriam Plaza-del-Arco | Pilar López-Úbeda | L. Alfonso Ureña-López | M. Teresa Martín-Valdivia
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

This paper describes the participation of SINAI team at Task 5: Toxic Spans Detection which consists of identifying spans that make a text toxic. Although several resources and systems have been developed so far in the context of offensive language, both annotation and tasks have mainly focused on classifying whether a text is offensive or not. However, detecting toxic spans is crucial to identify why a text is toxic and can assist human moderators to locate this type of content on social media. In order to accomplish the task, we follow a deep learning-based approach using a Bidirectional variant of a Long Short Term Memory network along with a stacked Conditional Random Field decoding layer (BiLSTM-CRF). Specifically, we test the performance of the combination of different pre-trained word embeddings for recognizing toxic entities in text. The results show that the combination of word embeddings helps in detecting offensive content. Our team ranks 29th out of 91 participants.

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Identifying professions & occupations in Health-related Social Media using Natural Language Processing
Alberto Mesa Murgado | Ana Parras Portillo | Pilar López Úbeda | Maite Martin | Alfonso Ureña-López
Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task

This paper describes the entry of the research group SINAI at SMM4H’s ProfNER task on the identification of professions and occupations in social media related with health. Specifically we have participated in Task 7a: Tweet Binary Classification to determine whether a tweet contains mentions of occupations or not, as well as in Task 7b: NER Offset Detection and Classification aimed at predicting occupations mentions and classify them discriminating by professions and working statuses.

2020

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Transfer learning applied to text classification in Spanish radiological reports
Pilar López Úbeda | Manuel Carlos Díaz-Galiano | L. Alfonso Urena Lopez | Maite Martin | Teodoro Martín-Noguerol | Antonio Luna
Proceedings of the LREC 2020 Workshop on Multilingual Biomedical Text Processing (MultilingualBIO 2020)

Pre-trained text encoders have rapidly advanced the state-of-the-art on many Natural Language Processing tasks. This paper presents the use of transfer learning methods applied to the automatic detection of codes in radiological reports in Spanish. Assigning codes to a clinical document is a popular task in NLP and in the biomedical domain. These codes can be of two types: standard classifications (e.g. ICD-10) or specific to each clinic or hospital. In this study we show a system using specific radiology clinic codes. The dataset is composed of 208,167 radiology reports labeled with 89 different codes. The corpus has been evaluated with three methods using the BERT model applied to Spanish: Multilingual BERT, BETO and XLM. The results are interesting obtaining 70% of F1-score with a pre-trained multilingual model.

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SINAI at SemEval-2020 Task 12: Offensive Language Identification Exploring Transfer Learning Models
Flor Miriam Plaza del Arco | M. Dolores Molina González | Alfonso Ureña-López | Maite Martin
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper describes the participation of SINAI team at Task 12: OffensEval 2: Multilingual Offensive Language Identification in Social Media. In particular, the participation in Sub-task A in English which consists of identifying tweets as offensive or not offensive. We preprocess the dataset according to the language characteristics used on social media. Then, we select a small set from the training set provided by the organizers and fine-tune different Transformerbased models in order to test their effectiveness. Our team ranks 20th out of 85 participants in Subtask-A using the XLNet model.

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Corpora Annotated with Negation: An Overview
Salud María Jiménez-Zafra | Roser Morante | María Teresa Martín-Valdivia | L. Alfonso Ureña-López
Computational Linguistics, Volume 46, Issue 1 - March 2020

Negation is a universal linguistic phenomenon with a great qualitative impact on natural language processing applications. The availability of corpora annotated with negation is essential to training negation processing systems. Currently, most corpora have been annotated for English, but the presence of languages other than English on the Internet, such as Chinese or Spanish, is greater every day. In this study, we present a review of the corpora annotated with negation information in several languages with the goal of evaluating what aspects of negation have been annotated and how compatible the corpora are. We conclude that it is very difficult to merge the existing corpora because we found differences in the annotation schemes used, and most importantly, in the annotation guidelines: the way in which each corpus was tokenized and the negation elements that have been annotated. Differently than for other well established tasks like semantic role labeling or parsing, for negation there is no standard annotation scheme nor guidelines, which hampers progress in its treatment.

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EmoEvent: A Multilingual Emotion Corpus based on different Events
Flor Miriam Plaza del Arco | Carlo Strapparava | L. Alfonso Urena Lopez | Maite Martin
Proceedings of the 12th Language Resources and Evaluation Conference

In recent years emotion detection in text has become more popular due to its potential applications in fields such as psychology, marketing, political science, and artificial intelligence, among others. While opinion mining is a well-established task with many standard data sets and well-defined methodologies, emotion mining has received less attention due to its complexity. In particular, the annotated gold standard resources available are not enough. In order to address this shortage, we present a multilingual emotion data set based on different events that took place in April 2019. We collected tweets from the Twitter platform. Then one of seven emotions, six Ekman’s basic emotions plus the “neutral or other emotions”, was labeled on each tweet by 3 Amazon MTurkers. A total of 8,409 in Spanish and 7,303 in English were labeled. In addition, each tweet was also labeled as offensive or no offensive. We report some linguistic statistics about the data set in order to observe the difference between English and Spanish speakers when they express emotions related to the same events. Moreover, in order to validate the effectiveness of the data set, we also propose a machine learning approach for automatically detecting emotions in tweets for both languages, English and Spanish.

2019

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SINAI at SemEval-2019 Task 3: Using affective features for emotion classification in textual conversations
Flor Miriam Plaza-del-Arco | M. Dolores Molina-González | Maite Martin | L. Alfonso Ureña-López
Proceedings of the 13th International Workshop on Semantic Evaluation

Detecting emotions in textual conversation is a challenging problem in absence of nonverbal cues typically associated with emotion, like fa- cial expression or voice modulations. How- ever, more and more users are using message platforms such as WhatsApp or Telegram. For this reason, it is important to develop systems capable of understanding human emotions in textual conversations. In this paper, we carried out different systems to analyze the emotions of textual dialogue from SemEval-2019 Task 3: EmoContext for English language. Our main contribution is the integration of emotional and sentimental features in the classification using the SVM algorithm.

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SINAI at SemEval-2019 Task 5: Ensemble learning to detect hate speech against inmigrants and women in English and Spanish tweets
Flor Miriam Plaza-del-Arco | M. Dolores Molina-González | Maite Martin | L. Alfonso Ureña-López
Proceedings of the 13th International Workshop on Semantic Evaluation

Misogyny and xenophobia are some of the most important social problems. With the in- crease in the use of social media, this feeling ofhatred towards women and immigrants can be more easily expressed, therefore it can cause harmful effects on social media users. For this reason, it is important to develop systems ca- pable of detecting hateful comments automatically. In this paper, we describe our system to analyze the hate speech in English and Spanish tweets against Immigrants and Women as part of our participation in SemEval-2019 Task 5: hatEval. Our main contribution is the integration of three individual algorithms of predic- tion in a model based on Vote ensemble classifier.

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SINAI at SemEval-2019 Task 6: Incorporating lexicon knowledge into SVM learning to identify and categorize offensive language in social media
Flor Miriam Plaza-del-Arco | M. Dolores Molina-González | Maite Martin | L. Alfonso Ureña-López
Proceedings of the 13th International Workshop on Semantic Evaluation

Offensive language has an impact across society. The use of social media has aggravated this issue among online users, causing suicides in the worst cases. For this reason, it is important to develop systems capable of identifying and detecting offensive language in text automatically. In this paper, we developed a system to classify offensive tweets as part of our participation in SemEval-2019 Task 6: OffensEval. Our main contribution is the integration of lexical features in the classification using the SVM algorithm.

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Detecting Anorexia in Spanish Tweets
Pilar López Úbeda | Flor Miriam Plaza del Arco | Manuel Carlos Díaz Galiano | L. Alfonso Urena Lopez | Maite Martin
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

Mental health is one of the main concerns of today’s society. Early detection of symptoms can greatly help people with mental disorders. People are using social networks more and more to express emotions, sentiments and mental states. Thus, the treatment of this information using NLP technologies can be applied to the automatic detection of mental problems such as eating disorders. However, the first step to solving the problem should be to provide a corpus in order to evaluate our systems. In this paper, we specifically focus on detecting anorexia messages on Twitter. Firstly, we have generated a new corpus of tweets extracted from different accounts including anorexia and non-anorexia messages in Spanish. The corpus is called SAD: Spanish Anorexia Detection corpus. In order to validate the effectiveness of the SAD corpus, we also propose several machine learning approaches for automatically detecting anorexia symptoms in the corpus. The good results obtained show that the application of textual classification methods is a promising option for developing this kind of system demonstrating that these tools could be used by professionals to help in the early detection of mental problems.

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Using Snomed to recognize and index chemical and drug mentions.
Pilar López Úbeda | Manuel Carlos Díaz Galiano | L. Alfonso Urena Lopez | Maite Martin
Proceedings of The 5th Workshop on BioNLP Open Shared Tasks

In this paper we describe a new named entity extraction system. Our work proposes a system for the identification and annotation of drug names in Spanish biomedical texts based on machine learning and deep learning models. Subsequently, a standardized code using Snomed is assigned to these drugs, for this purpose, Natural Language Processing tools and techniques have been used, and a dictionary of different sources of information has been built. The results are promising, we obtain 78% in F1 score on the first sub-track and in the second task we map with Snomed correctly 72% of the found entities.

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Using Machine Learning and Deep Learning Methods to Find Mentions of Adverse Drug Reactions in Social Media
Pilar López Úbeda | Manuel Carlos Díaz Galiano | Maite Martin | L. Alfonso Urena Lopez
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task

Over time the use of social networks is becoming very popular platforms for sharing health related information. Social Media Mining for Health Applications (SMM4H) provides tasks such as those described in this document to help manage information in the health domain. This document shows the first participation of the SINAI group. We study approaches based on machine learning and deep learning to extract adverse drug reaction mentions from Twitter. The results obtained in the tasks are encouraging, we are close to the average of all participants and even above in some cases.

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.

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A review of Spanish corpora annotated with negation
Salud María Jiménez-Zafra | Roser Morante | Maite Martin | L. Alfonso Ureña-López
Proceedings of the 27th International Conference on Computational Linguistics

The availability of corpora annotated with negation information is essential to develop negation processing systems in any language. However, there is a lack of these corpora even for languages like English, and when there are corpora available they are small and the annotations are not always compatible across corpora. In this paper we review the existing corpora annotated with negation in Spanish with the purpose of first, gathering the information to make it available for other researchers and, second, analyzing how compatible are the corpora and how has the linguistic phenomenon been addressed. Our final aim is to develop a supervised negation processing system for Spanish, for which we need training and test data. Our analysis shows that it will not be possible to merge the small corpora existing for Spanish due to lack of compatibility in the annotations.

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SINAI at SemEval-2018 Task 1: Emotion Recognition in Tweets
Flor Miriam Plaza-del-Arco | Salud María Jiménez-Zafra | Maite Martin | L. Alfonso Ureña-López
Proceedings of The 12th International Workshop on Semantic Evaluation

Emotion classification is a new task that combines several disciplines including Artificial Intelligence and Psychology, although Natural Language Processing is perhaps the most challenging area. In this paper, we describe our participation in SemEval-2018 Task1: Affect in Tweets. In particular, we have participated in EI-oc, EI-reg and E-c subtasks for English and Spanish languages.

2017

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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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Out-of-domain FrameNet Semantic Role Labeling
Silvana Hartmann | Ilia Kuznetsov | Teresa Martin | Iryna Gurevych
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Domain dependence of NLP systems is one of the major obstacles to their application in large-scale text analysis, also restricting the applicability of FrameNet semantic role labeling (SRL) systems. Yet, current FrameNet SRL systems are still only evaluated on a single in-domain test set. For the first time, we study the domain dependence of FrameNet SRL on a wide range of benchmark sets. We create a novel test set for FrameNet SRL based on user-generated web text and find that the major bottleneck for out-of-domain FrameNet SRL is the frame identification step. To address this problem, we develop a simple, yet efficient system based on distributed word representations. Our system closely approaches the state-of-the-art in-domain while outperforming the best available frame identification system out-of-domain. We publish our system and test data for research purposes.

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SINAI at SemEval-2017 Task 4: User based classification
Salud María Jiménez-Zafra | Arturo Montejo-Ráez | Maite Martin | L. Alfonso Ureña-López
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This document describes our participation in SemEval-2017 Task 4: Sentiment Analysis in Twitter. We have only reported results for subtask B - English, determining the polarity towards a topic on a two point scale (positive or negative sentiment). Our main contribution is the integration of user information in the classification process. A SVM model is trained with Word2Vec vectors from user’s tweets extracted from his timeline. The obtained results show that user-specific classifiers trained on tweets from user timeline can introduce noise as they are error prone because they are classified by an imperfect system. This encourages us to explore further integration of user information for author-based Sentiment Analysis.

2016

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Domain Adaptation of Polarity Lexicon combining Term Frequency and Bootstrapping
Salud María Jiménez-Zafra | Maite Martin | M. Dolores Molina-Gonzalez | L. Alfonso Ureña-López
Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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Call for Discussion: Building a New Standard Dataset for Relation Extraction Tasks
Teresa Martin | Fiete Botschen | Ajay Nagesh | Andrew McCallum
Proceedings of the 5th Workshop on Automated Knowledge Base Construction

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Problematic Cases in the Annotation of Negation in Spanish
Salud María Jiménez-Zafra | Maite Martin | L. Alfonso Ureña-López | Toni Martí | Mariona Taulé
Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics (ExProM)

This paper presents the main sources of disagreement found during the annotation of the Spanish SFU Review Corpus with negation (SFU ReviewSP -NEG). Negation detection is a challenge in most of the task related to NLP, so the availability of corpora annotated with this phenomenon is essential in order to advance in tasks related to this area. A thorough analysis of the problems found during the annotation could help in the study of this phenomenon.

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)

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A Multi-lingual Annotated Dataset for Aspect-Oriented Opinion Mining
Salud M. Jiménez Zafra | Giacomo Berardi | Andrea Esuli | Diego Marcheggiani | María Teresa Martín-Valdivia | Alejandro Moreo Fernández
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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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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)

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Open Book: a tool for helping ASD users’ semantic comprehension
Eduard Barbu | Maria Teresa Martín-Valdivia | Luis Alfonso Ureña-López
Proceedings of the Workshop on Natural Language Processing for Improving Textual Accessibility

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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

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

2011

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Bilingual Experiments with an Arabic-English Corpus for Opinion Mining
Mohammed Rushdi-Saleh | M. Teresa Martín-Valdivia | L. Alfonso Ureña-López | José M. Perea-Ortega
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

2007

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Combining Lexical-Syntactic Information with Machine Learning for Recognizing Textual Entailment
Arturo Montejo-Ráez | Jose Manuel Perea | Fernando Martínez-Santiago | Miguel Ángel García-Cumbreras | Maite Martín-Valdivia | Alfonso Ureña-López
Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing

2004

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The University of Jaén Word Sense Disambiguation system
Manuel García-Vega | Miguel García-Cumbreras | M. Teresa Martín-Valdivia | L. Alfonso Urena-López
Proceedings of SENSEVAL-3, the Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text

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The R2D2 team at Senseval-3
Sonia Vázquez | Rafael Romero | Armando Suárez | Andrés Montoyo | Manuel García | M. Teresa Martín | M. Ángel García | L. Alfonso Urena
Proceedings of SENSEVAL-3, the Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text