Aitor García Pablos

Also published as: Aitor García-Pablos


2022

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MAPA Project: Ready-to-Go Open-Source Datasets and Deep Learning Technology to Remove Identifying Information from Text Documents
Victoria Arranz | Khalid Choukri | Montse Cuadros | Aitor García Pablos | Lucie Gianola | Cyril Grouin | Manuel Herranz | Patrick Paroubek | Pierre Zweigenbaum
Proceedings of the Workshop on Ethical and Legal Issues in Human Language Technologies and Multilingual De-Identification of Sensitive Data In Language Resources within the 13th Language Resources and Evaluation Conference

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Spanish Datasets for Sensitive Entity Detection in the Legal Domain
Ona de Gibert Bonet | Aitor García Pablos | Montse Cuadros | Maite Melero
Proceedings of the Thirteenth Language Resources and Evaluation Conference

The de-identification of sensible data, also known as automatic textual anonymisation, is essential for data sharing and reuse, both for research and commercial purposes. The first step for data anonymisation is the detection of sensible entities. In this work, we present four new datasets for named entity detection in Spanish in the legal domain. These datasets have been generated in the framework of the MAPA project, three smaller datasets have been manually annotated and one large dataset has been automatically annotated, with an estimated error rate of around 14%. In order to assess the quality of the generated datasets, we have used them to fine-tune a battery of entity-detection models, using as foundation different pre-trained language models: one multilingual, two general-domain monolingual and one in-domain monolingual. We compare the results obtained, which validate the datasets as a valuable resource to fine-tune models for the task of named entity detection. We further explore the proposed methodology by applying it to a real use case scenario.

2020

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Sensitive Data Detection and Classification in Spanish Clinical Text: Experiments with BERT
Aitor García Pablos | Naiara Perez | Montse Cuadros
Proceedings of the Twelfth Language Resources and Evaluation Conference

Massive digital data processing provides a wide range of opportunities and benefits, but at the cost of endangering personal data privacy. Anonymisation consists in removing or replacing sensitive information from data, enabling its exploitation for different purposes while preserving the privacy of individuals. Over the years, a lot of automatic anonymisation systems have been proposed; however, depending on the type of data, the target language or the availability of training documents, the task remains challenging still. The emergence of novel deep-learning models during the last two years has brought large improvements to the state of the art in the field of Natural Language Processing. These advancements have been most noticeably led by BERT, a model proposed by Google in 2018, and the shared language models pre-trained on millions of documents. In this paper, we use a BERT-based sequence labelling model to conduct a series of anonymisation experiments on several clinical datasets in Spanish. We also compare BERT with other algorithms. The experiments show that a simple BERT-based model with general-domain pre-training obtains highly competitive results without any domain specific feature engineering.

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The Multilingual Anonymisation Toolkit for Public Administrations (MAPA) Project
Ēriks Ajausks | Victoria Arranz | Laurent Bié | Aleix Cerdà-i-Cucó | Khalid Choukri | Montse Cuadros | Hans Degroote | Amando Estela | Thierry Etchegoyhen | Mercedes García-Martínez | Aitor García-Pablos | Manuel Herranz | Alejandro Kohan | Maite Melero | Mike Rosner | Roberts Rozis | Patrick Paroubek | Artūrs Vasiļevskis | Pierre Zweigenbaum
Proceedings of the 22nd Annual Conference of the European Association for Machine Translation

We describe the MAPA project, funded under the Connecting Europe Facility programme, whose goal is the development of an open-source de-identification toolkit for all official European Union languages. It will be developed since January 2020 until December 2021.

2018

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Hate Speech Dataset from a White Supremacy Forum
Ona de Gibert | Naiara Perez | Aitor García-Pablos | Montse Cuadros
Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)

Hate speech is commonly defined as any communication that disparages a target group of people based on some characteristic such as race, colour, ethnicity, gender, sexual orientation, nationality, religion, or other characteristic. Due to the massive rise of user-generated web content on social media, the amount of hate speech is also steadily increasing. Over the past years, interest in online hate speech detection and, particularly, the automation of this task has continuously grown, along with the societal impact of the phenomenon. This paper describes a hate speech dataset composed of thousands of sentences manually labelled as containing hate speech or not. The sentences have been extracted from Stormfront, a white supremacist forum. A custom annotation tool has been developed to carry out the manual labelling task which, among other things, allows the annotators to choose whether to read the context of a sentence before labelling it. The paper also provides a thoughtful qualitative and quantitative study of the resulting dataset and several baseline experiments with different classification models. The dataset is publicly available.

2016

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A Comparison of Domain-based Word Polarity Estimation using different Word Embeddings
Aitor García Pablos | Montse Cuadros | German Rigau
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

A key point in Sentiment Analysis is to determine the polarity of the sentiment implied by a certain word or expression. In basic Sentiment Analysis systems this sentiment polarity of the words is accounted and weighted in different ways to provide a degree of positivity/negativity. Currently words are also modelled as continuous dense vectors, known as word embeddings, which seem to encode interesting semantic knowledge. With regard to Sentiment Analysis, word embeddings are used as features to more complex supervised classification systems to obtain sentiment classifiers. In this paper we compare a set of existing sentiment lexicons and sentiment lexicon generation techniques. We also show a simple but effective technique to calculate a word polarity value for each word in a domain using existing continuous word embeddings generation methods. Further, we also show that word embeddings calculated on in-domain corpus capture the polarity better than the ones calculated on general-domain corpus.

2015

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V3: Unsupervised Aspect Based Sentiment Analysis for SemEval2015 Task 12
Aitor García-Pablos | Montse Cuadros | German Rigau
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

2014

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V3: Unsupervised Generation of Domain Aspect Terms for Aspect Based Sentiment Analysis
Aitor García-Pablos | Montse Cuadros | German Rigau
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)