Yoan Gutiérrez

Also published as: Yoan Gutierrez, Yoan Gutiérrez Vázquez


2021

pdf bib
Active Learning for Assisted Corpus Construction: A Case Study in Knowledge Discovery from Biomedical Text
Hian Cañizares-Díaz | Alejandro Piad-Morffis | Suilan Estevez-Velarde | Yoan Gutiérrez | Yudivián Almeida Cruz | Andres Montoyo | Rafael Muñoz-Guillena
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

This paper presents an active learning approach that aims to reduce the human effort required during the annotation of natural language corpora composed of entities and semantic relations. Our approach assists human annotators by intelligently selecting the most informative sentences to annotate and then pre-annotating them with a few highly accurate entities and semantic relations. We define an uncertainty-based query strategy with a weighted density factor, using similarity metrics based on sentence embeddings. As a case study, we evaluate our approach via simulation in a biomedical corpus and estimate the potential reduction in total annotation time. Experimental results suggest that the query strategy reduces by between 35% and 40% the number of sentences that must be manually annotated to develop systems able to reach a target F1 score, while the pre-annotation strategy produces an additional 24% reduction in the total annotation time. Overall, our preliminary experiments suggest that as much as 60% of the annotation time could be saved while producing corpora that have the same usefulness for training machine learning algorithms. An open-source computational tool that implements the aforementioned strategies is presented and published online for the research community.

pdf bib
Knowledge Discovery in COVID-19 Research Literature
Ernesto L. Estevanell-Valladares | Suilan Estevez-Velarde | Alejandro Piad-Morffis | Yoan Gutierrez | Andres Montoyo | Rafael Muñoz | Yudivián Almeida Cruz
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

This paper presents the preliminary results of an ongoing project that analyzes the growing body of scientific research published around the COVID-19 pandemic. In this research, a general-purpose semantic model is used to double annotate a batch of 500 sentences that were manually selected from the CORD-19 corpus. Afterwards, a baseline text-mining pipeline is designed and evaluated via a large batch of 100,959 sentences. We present a qualitative analysis of the most interesting facts automatically extracted and highlight possible future lines of development. The preliminary results show that general-purpose semantic models are a useful tool for discovering fine-grained knowledge in large corpora of scientific documents.

2020

pdf bib
Knowledge Discovery in COVID-19 Research Literature
Alejandro Piad-Morffis | Suilan Estevez-Velarde | Ernesto Luis Estevanell-Valladares | Yoan Gutiérrez | Andrés Montoyo | Rafael Muñoz | Yudivián Almeida-Cruz
Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

This paper presents the preliminary results of an ongoing project that analyzes the growing body of scientific research published around the COVID-19 pandemic. In this research, a general-purpose semantic model is used to double annotate a batch of $500$ sentences that were manually selected by the researchers from the CORD-19 corpus. Afterwards, a baseline text-mining pipeline is designed and evaluated via a large batch of $100,959$ sentences. We present a qualitative analysis of the most interesting facts automatically extracted and highlight possible future lines of development. The preliminary results show that general-purpose semantic models are a useful tool for discovering fine-grained knowledge in large corpora of scientific documents.

pdf bib
Automatic Discovery of Heterogeneous Machine Learning Pipelines: An Application to Natural Language Processing
Suilan Estevez-Velarde | Yoan Gutiérrez | Andres Montoyo | Yudivián Almeida Cruz
Proceedings of the 28th International Conference on Computational Linguistics

This paper presents AutoGOAL, a system for automatic machine learning (AutoML) that uses heterogeneous techniques. In contrast with existing AutoML approaches, our contribution can automatically build machine learning pipelines that combine techniques and algorithms from different frameworks, including shallow classifiers, natural language processing tools, and neural networks. We define the heterogeneous AutoML optimization problem as the search for the best sequence of algorithms that transforms specific input data into the desired output. This provides a novel theoretical and practical approach to AutoML. Our proposal is experimentally evaluated in diverse machine learning problems and compared with alternative approaches, showing that it is competitive with other AutoML alternatives in standard benchmarks. Furthermore, it can be applied to novel scenarios, such as several NLP tasks, where existing alternatives cannot be directly deployed. The system is freely available and includes in-built compatibility with a large number of popular machine learning frameworks, which makes our approach useful for solving practical problems with relative ease and effort.

pdf bib
Demo Application for the AutoGOAL Framework
Suilan Estevez-Velarde | Alejandro Piad-Morffis | Yoan Gutiérrez | Andres Montoyo | Rafael Muñoz-Guillena | Yudivián Almeida Cruz
Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations

This paper introduces a web demo that showcases the main characteristics of the AutoGOAL framework. AutoGOAL is a framework in Python for automatically finding the best way to solve a given task. It has been designed mainly for automatic machine learning(AutoML) but it can be used in any scenario where several possible strategies are available to solve a given computational task. In contrast with alternative frameworks, AutoGOAL can be applied seamlessly to Natural Language Processing as well as structured classification problems. This paper presents an overview of the framework’s design and experimental evaluation in several machine learning problems, including two recent NLP challenges. The accompanying software demo is available online (https://autogoal.github.io/demo) and full source code is provided under the MIT open-source license (https://autogoal.github.io).

2019

pdf bib
AutoML Strategy Based on Grammatical Evolution: A Case Study about Knowledge Discovery from Text
Suilan Estevez-Velarde | Yoan Gutiérrez | Andrés Montoyo | Yudivián Almeida-Cruz
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

The process of extracting knowledge from natural language text poses a complex problem that requires both a combination of machine learning techniques and proper feature selection. Recent advances in Automatic Machine Learning (AutoML) provide effective tools to explore large sets of algorithms, hyper-parameters and features to find out the most suitable combination of them. This paper proposes a novel AutoML strategy based on probabilistic grammatical evolution, which is evaluated on the health domain by facing the knowledge discovery challenge in Spanish text documents. Our approach achieves state-of-the-art results and provides interesting insights into the best combination of parameters and algorithms to use when dealing with this challenge. Source code is provided for the research community.

pdf bib
Demo Application for LETO: Learning Engine Through Ontologies
Suilan Estevez-Velarde | Andrés Montoyo | Yudivian Almeida-Cruz | Yoan Gutiérrez | Alejandro Piad-Morffis | Rafael Muñoz
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

The massive amount of multi-formatted information available on the Web necessitates the design of software systems that leverage this information to obtain knowledge that is valid and useful. The main challenge is to discover relevant information and continuously update, enrich and integrate knowledge from various sources of structured and unstructured data. This paper presents the Learning Engine Through Ontologies(LETO) framework, an architecture for the continuous and incremental discovery of knowledge from multiple sources of unstructured and structured data. We justify the main design decision behind LETO’s architecture and evaluate the framework’s feasibility using the Internet Movie Data Base(IMDB) and Twitter as a practical application.

pdf bib
A Neural Network Component for Knowledge-Based Semantic Representations of Text
Alejandro Piad-Morffis | Rafael Muñoz | Yoan Gutiérrez | Yudivian Almeida-Cruz | Suilan Estevez-Velarde | Andrés Montoyo
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

This paper presents Semantic Neural Networks (SNNs), a knowledge-aware component based on deep learning. SNNs can be trained to encode explicit semantic knowledge from an arbitrary knowledge base, and can subsequently be combined with other deep learning architectures. At prediction time, SNNs provide a semantic encoding extracted from the input data, which can be exploited by other neural network components to build extended representation models that can face alternative problems. The SNN architecture is defined in terms of the concepts and relations present in a knowledge base. Based on this architecture, a training procedure is developed. Finally, an experimental setup is presented to illustrate the behaviour and performance of a SNN for a specific NLP problem, in this case, opinion mining for the classification of movie reviews.

2017

pdf bib
UCSC-NLP at SemEval-2017 Task 4: Sense n-grams for Sentiment Analysis in Twitter
José Abreu | Iván Castro | Claudia Martínez | Sebastián Oliva | Yoan Gutiérrez
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes the system submitted to SemEval-2017 Task 4-A Sentiment Analysis in Twitter developed by the UCSC-NLP team. We studied how relationships between sense n-grams and sentiment polarities can contribute to this task, i.e. co-occurrences of WordNet senses in the tweet, and the polarity. Furthermore, we evaluated the effect of discarding a large set of features based on char-grams reported in preceding works. Based on these elements, we developed a SVM system, which exploring SentiWordNet as a polarity lexicon. It achieves an F1=0.624of average. Among 39 submissions to this task, we ranked 10th.

pdf bib
Opinion Mining in Social Networks versus Electoral Polls
Javi Fernández | Fernando Llopis | Yoan Gutiérrez | Patricio Martínez-Barco | Álvaro Díez
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

The recent failures of traditional poll models, like the predictions in United Kingdom with the Brexit, or in United States presidential elections with the victory of Donald Trump, have been noteworthy. With the decline of traditional poll models and the growth of the social networks, automatic tools are gaining popularity to make predictions in this context. In this paper we present our approximation and compare it with a real case: the 2017 French presidential election.

pdf bib
Natural Language Processing Technologies for Document Profiling
Antonio Guillén | Yoan Gutiérrez | Rafael Muñoz
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

Nowadays, search for documents on the Internet is becoming increasingly difficult. The reason is the amount of content published by users (articles, comments, blogs, reviews). How to facilitate that the users can find their required documents? What would be necessary to provide useful document meta-data for supporting search engines? In this article, we present a study of some Natural Language Processing (NLP) technologies that can be useful for facilitating the proper identification of documents according to the user needs. For this purpose, it is designed a document profile that will be able to represent semantic meta-data extracted from documents by using NLP technologies. The research is basically focused on the study of different NLP technologies in order to support the creation our novel document profile proposal from semantic perspectives.

2014

pdf bib
GPLSI: Supervised Sentiment Analysis in Twitter using Skipgrams
Javi Fernández | Yoan Gutiérrez | Jose Manuel Gómez | Patricio Martínez-Barco
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

pdf bib
UMCC_DLSI_SemSim: Multilingual System for Measuring Semantic Textual Similarity
Alexander Chávez | Héctor Dávila | Yoan Gutiérrez | Antonio Fernández-Orquín | Andrés Montoyo | Rafael Muñoz
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

pdf bib
UMCC_DLSI: A Probabilistic Automata for Aspect Based Sentiment Analysis
Yenier Castañeda | Armando Collazo | Elvis Crego | Jorge L. Garcia | Yoan Gutiérrez | David Tomás | Andrés Montoyo | Rafael Muñoz
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

pdf bib
UMCC_DLSI: Sentiment Analysis in Twitter using Polirity Lexicons and Tweet Similarity
Pedro Aniel Sánchez-Mirabal | Yarelis Ruano Torres | Suilen Hernández Alvarado | Yoan Gutiérrez | Andrés Montoyo | Rafael Muñoz
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

pdf bib
UO_UA: Using Latent Semantic Analysis to Build a Domain-Dependent Sentiment Resource
Reynier Ortega Bueno | Adrian Fonseca Bruzón | Carlos Muñiz Cuza | Yoan Gutiérrez | Andrés Montoyo
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

2013

pdf bib
UMCC_DLSI: Textual Similarity based on Lexical-Semantic features
Alexander Chávez | Héctor Dávila | Yoan Gutiérrez | Armando Collazo | José I. Abreu | Antonio Fernández Orquín | Andrés Montoyo | Rafael Muñoz
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity

pdf bib
UMCC_DLSI-(EPS): Paraphrases Detection Based on Semantic Distance
Héctor Dávila | Antonio Fernández Orquín | Alexander Chávez | Yoan Gutiérrez | Armando Collazo | José I. Abreu | Andrés Montoyo | Rafael Muñoz
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

pdf bib
UMCC_DLSI: Reinforcing a Ranking Algorithm with Sense Frequencies and Multidimensional Semantic Resources to solve Multilingual Word Sense Disambiguation
Yoan Gutiérrez | Yenier Castañeda | Andy González | Rainel Estrada | Dennys D. Piug | Jose I. Abreu | Roger Pérez | Antonio Fernández Orquín | Andrés Montoyo | Rafael Muñoz | Franc Camara
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

pdf bib
UMCC_DLSI-(SA): Using a ranking algorithm and informal features to solve Sentiment Analysis in Twitter
Yoan Gutiérrez | Andy González | Roger Pérez | José I. Abreu | Antonio Fernández Orquín | Alejandro Mosquera | Andrés Montoyo | Rafael Muñoz | Franc Camara
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

pdf bib
SSA-UO: Unsupervised Sentiment Analysis in Twitter
Reynier Ortega Bueno | Adrian Fonseca Bruzón | Yoan Gutiérrez | Andrés Montoyo
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

pdf bib
UMCC_DLSI: Semantic and Lexical features for detection and classification Drugs in biomedical texts
Armando Collazo | Alberto Ceballo | Dennys D. Puig | Yoan Gutiérrez | José I. Abreu | Roger Pérez | Antonio Fernández Orquín | Andrés Montoyo | Rafael Muñoz | Franc Camara
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

pdf bib
RA-SR: Using a ranking algorithm to automatically building resources for subjectivity analysis over annotated corpora
Yoan Gutiérrez | Andy González | Antonio Fernández | Andrés Montoyo | Rafael Muñoz
Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

2012

pdf bib
UMCC_DLSI: Multidimensional Lexical-Semantic Textual Similarity
Antonio Fernández | Yoan Gutiérrez | Héctor Dávila | Alexander Chávez | Andy González | Rainel Estrada | Yenier Castañeda | Sonia Vázquez | Andrés Montoyo | Rafael Muñoz
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

2011

pdf bib
Improving WSD using ISR-WN with Relevant Semantic Trees and SemCor Senses Frequency
Yoan Gutiérrez | Sonia Vázquez | Andrés Montoyo
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

pdf bib
Sentiment Classification Using Semantic Features Extracted from WordNet-based Resources
Yoan Gutiérrez | Sonia Vázquez | Andrés Montoyo
Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA 2.011)

2010

pdf bib
UMCC-DLSI: Integrative Resource for Disambiguation Task
Yoan Gutiérrez Vázquez | Antonio Fernandez Orquín | Andrés Montoyo Guijarro | Sonia Vázquez Pérez
Proceedings of the 5th International Workshop on Semantic Evaluation