Andrés Montoyo

Also published as: Andres Montoyo, Andrés Montoyo Guijarro


2021

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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.

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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

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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.

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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).

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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.

2019

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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.

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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.

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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.

2016

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Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Alexandra Balahur | Erik van der Goot | Piek Vossen | Andres Montoyo
Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

2015

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Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Alexandra Balahur | Erik van der Goot | Piek Vossen | Andres Montoyo
Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

2014

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Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Alexandra Balahur | Erik van der Goot | Ralf Steinberger | Andres Montoyo
Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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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)

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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)

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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)

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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

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Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Alexandra Balahur | Erik van der Goot | Andres Montoyo
Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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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

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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

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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)

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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)

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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)

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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)

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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)

2012

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Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis
Alexandra Balahur | Andres Montoyo | Patricio Martinez Barco | Ester Boldrini
Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis

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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

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Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA 2.011)
Alexandra Balahur | Ester Boldrini | Andres Montoyo | Patricio Martinez-Barco
Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA 2.011)

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Detecting Implicit Expressions of Sentiment in Text Based on Commonsense Knowledge
Alexandra Balahur | Jesús M. Hermida | Andrés Montoyo
Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA 2.011)

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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)

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Towards a Unified Approach for Opinion Question Answering and Summarization
Elena Lloret | Alexandra Balahur | Manuel Palomar | Andrés Montoyo
Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA 2.011)

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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

2010

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Going Beyond Traditional QA Systems: Challenges and Keys in Opinion Question Answering
Alexandra Balahur | Ester Boldrini | Andrés Montoyo | Patricio Martínez-Barco
Coling 2010: Posters

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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

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OpAL: Applying Opinion Mining Techniques for the Disambiguation of Sentiment Ambiguous Adjectives in SemEval-2 Task 18
Alexandra Balahur | Andrés Montoyo
Proceedings of the 5th International Workshop on Semantic Evaluation

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EmotiBlog: A Finer-Grained and More Precise Learning of Subjectivity Expression Models
Ester Boldrini | Alexandra Balahur | Patricio Martínez-Barco | Andrés Montoyo
Proceedings of the Fourth Linguistic Annotation Workshop

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A survey on the role of negation in sentiment analysis
Michael Wiegand | Alexandra Balahur | Benjamin Roth | Dietrich Klakow | Andrés Montoyo
Proceedings of the Workshop on Negation and Speculation in Natural Language Processing

2009

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A Comparative Study of Open Domain and Opinion Question Answering Systems for Factual and Opinionated Queries
Alexandra Balahur | Ester Boldrini | Andrés Montoyo | Patricio Martínez-Barco
Proceedings of the International Conference RANLP-2009

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Summarizing Threads in Blogs Using Opinion Polarity
Alexandra Balahur | Elena Lloret | Ester Boldrini | Andrés Montoyo | Manuel Palomar | Patricio Martínez-Barco
Proceedings of the Workshop on Events in Emerging Text Types

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Towards Building a Competitive Opinion Summarization System: Challenges and Keys
Elena Lloret | Alexandra Balahur | Manuel Palomar | Andrés Montoyo
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium

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Opinion and Generic Question Answering Systems: a Performance Analysis
Alexandra Balahur | Ester Boldrini | Andrés Montoyo | Patricio Martínez-Barco
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

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Summarizing Opinions in Blog Threads
Alexandra Balahur | Mijail Kabadjov | Josef Steinberger | Ralf Steinberger | Andrés Montoyo
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 2

2007

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A Language Independent Approach for Name Categorization and Discrimination
Zornitsa Kozareva | Sonia Vázquez | Andrés Montoyo
Proceedings of the Workshop on Balto-Slavonic Natural Language Processing

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UA-ZBSA: A Headline Emotion Classification through Web Information
Zornitsa Kozareva | Borja Navarro | Sonia Vázquez | Andrés Montoyo
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)

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UA-ZSA: Web Page Clustering on the basis of Name Disambiguation
Zornitsa Kozareva | Sonia Vazquez | Andres Montoyo
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)

2004

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The University of Alicante systems at Senseval-3
Sonia Vázquez | Rafael Romero | Armando Suárez | Andrés Montoyo | Iulia Nica | Antonia Martí
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

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The “Meaning” system on the English all-words task
Luís Villarejo | Lluis Màrquez | Eneko Agirre | David Martínez | Bernardo Magnini | Carlo Strapparava | Diana McCarthy | Andrés Montoyo | Armando Suárez
Proceedings of SENSEVAL-3, the Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text

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Enriching EWN with Syntagmatic Information by Means of WSD
Iulia Nica | Mª Antònia Martí | Andrés Montoyo | Sonia Vázquez
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

2001

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The University of Alicante Word Sense Disambiguation System
Andrés Montoyo | Armando Suárez
Proceedings of SENSEVAL-2 Second International Workshop on Evaluating Word Sense Disambiguation Systems