Montse Cuadros


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 12th 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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NUBes: A Corpus of Negation and Uncertainty in Spanish Clinical Texts
Salvador Lima Lopez | Naiara Perez | Montse Cuadros | German Rigau
Proceedings of the 12th Language Resources and Evaluation Conference

This paper introduces the first version of the NUBes corpus (Negation and Uncertainty annotations in Biomedical texts in Spanish). The corpus is part of an on-going research and currently consists of 29,682 sentences obtained from anonymised health records annotated with negation and uncertainty. The article includes an exhaustive comparison with similar corpora in Spanish, and presents the main annotation and design decisions. Additionally, we perform preliminary experiments using deep learning algorithms to validate the annotated dataset. As far as we know, NUBes is the largest available corpora for negation in Spanish and the first that also incorporates the annotation of speculation cues, scopes, and events.

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HitzalMed: Anonymisation of Clinical Text in Spanish
Salvador Lima Lopez | Naiara Perez | Laura García-Sardiña | Montse Cuadros
Proceedings of the 12th Language Resources and Evaluation Conference

HitzalMed is a web-framed tool that performs automatic detection of sensitive information in clinical texts using machine learning algorithms reported to be competitive for the task. Moreover, once sensitive information is detected, different anonymisation techniques are implemented that are configurable by the user –for instance, substitution, where sensitive items are replaced by same category text in an effort to generate a new document that looks as natural as the original one. The tool is able to get data from different document formats and outputs downloadable anonymised data. This paper presents the anonymisation and substitution technology and the demonstrator which is publicly available at https://snlt.vicomtech.org/hitzalmed.

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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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Biomedical term normalization of EHRs with UMLS
Naiara Perez-Miguel | Montse Cuadros | German Rigau
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 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.

2017

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Multilingual CALL Framework for Automatic Language Exercise Generation from Free Text
Naiara Perez | Montse Cuadros
Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics

This paper describes a web-based application to design and answer exercises for language learning. It is available in Basque, Spanish, English, and French. Based on open-source Natural Language Processing (NLP) technology such as word embedding models and word sense disambiguation, the application enables users to automatic create easily and in real time three types of exercises, namely, Fill-in-the-Gaps, Multiple Choice, and Shuffled Sentences questionnaires. These are generated from texts of the users’ own choice, so they can train their language skills with content of their particular interest.

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)

2012

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Highlighting relevant concepts from Topic Signatures
Montse Cuadros | Lluís Padró | German Rigau
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

This paper presents deepKnowNet, a new fully automatic method for building highly dense and accurate knowledge bases from existing semantic resources. Basically, the method applies a knowledge-based Word Sense Disambiguation algorithm to assign the most appropriate WordNet sense to large sets of topically related words acquired from the web, named TSWEB. This Word Sense Disambiguation algorithm is the personalized PageRank algorithm implemented in UKB. This new method improves by automatic means the current content of WordNet by creating large volumes of new and accurate semantic relations between synsets. KnowNet was our first attempt towards the acquisition of large volumes of semantic relations. However, KnowNet had some limitations that have been overcomed with deepKnowNet. deepKnowNet disambiguates the first hundred words of all Topic Signatures from the web (TSWEB). In this case, the method highlights the most relevant word senses of each Topic Signature and filter out the ones that are not so related to the topic. In fact, the knowledge it contains outperforms any other resource when is empirically evaluated in a common framework based on a similarity task annotated with human judgements.

2010

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Wikicorpus: A Word-Sense Disambiguated Multilingual Wikipedia Corpus
Samuel Reese | Gemma Boleda | Montse Cuadros | Lluís Padró | German Rigau
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This article presents a new freely available trilingual corpus (Catalan, Spanish, English) that contains large portions of the Wikipedia and has been automatically enriched with linguistic information. To our knowledge, this is the largest such corpus that is freely available to the community: In its present version, it contains over 750 million words. The corpora have been annotated with lemma and part of speech information using the open source library FreeLing. Also, they have been sense annotated with the state of the art Word Sense Disambiguation algorithm UKB. As UKB assigns WordNet senses, and WordNet has been aligned across languages via the InterLingual Index, this sort of annotation opens the way to massive explorations in lexical semantics that were not possible before. We present a first attempt at creating a trilingual lexical resource from the sense-tagged Wikipedia corpora, namely, WikiNet. Moreover, we present two by-products of the project that are of use for the NLP community: An open source Java-based parser for Wikipedia pages developed for the construction of the corpus, and the integration of the WSD algorithm UKB in FreeLing.

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Exploring Knowledge Bases for Similarity
Eneko Agirre | Montse Cuadros | German Rigau | Aitor Soroa
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Graph-based similarity over WordNet has been previously shown to perform very well on word similarity. This paper presents a study of the performance of such a graph-based algorithm when using different relations and versions of Wordnet. The graph algorithm is based on Personalized PageRank, a random-walk based algorithm which computes the probability of a random-walk initiated in the target word to reach any synset following the relations in WordNet (Haveliwala, 2002). Similarity is computed as the cosine of the probability distributions for each word over WordNet. The best combination of relations includes all relations in WordNet 3.0, included disambiguated glosses, and automatically disambiguated topic signatures called KnowNets. All relations are part of the official release of WordNet, except KnowNets, which have been derived automatically. The results over the WordSim 353 dataset show that using the adequate relations the performance improves over previously published WordNet-based results on the WordSim353 dataset (Finkelstein et al., 2002). The similarity software and some graphs used in this paper are publicly available at http://ixa2.si.ehu.es/ukb.

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Language Technology Challenges of a ‘Small’ Language (Catalan)
Maite Melero | Gemma Boleda | Montse Cuadros | Cristina España-Bonet | Lluís Padró | Martí Quixal | Carlos Rodríguez | Roser Saurí
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

In this paper, we present a brief snapshot of the state of affairs in computational processing of Catalan and the initiatives that are starting to take place in an effort to bring the field a step forward, by making a better and more efficient use of the already existing resources and tools, by bridging the gap between research and market, and by establishing periodical meeting points for the community. In particular, we present the results of the First Workshop on the Computational Processing of Catalan, which succeeded in putting together a fair representation of the research in the area, and received attention from both the industry and the administration. Aside from facilitating communication among researchers and between developers and users, the Workshop provided the organizers with valuable information about existing resources, tools, developers and providers. This information has allowed us to go a step further by setting up a “harvesting” procedure which will hopefully build the seed of a portal-catalogue-observatory of language resources and technologies in Catalan.

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Integrating a Large Domain Ontology of Species into WordNet
Montse Cuadros | Egoitz Laparra | German Rigau | Piek Vossen | Wauter Bosma
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

With the proliferation of applications sharing information represented in multiple ontologies, the development of automatic methods for robust and accurate ontology matching will be crucial to their success. Connecting and merging already existing semantic networks is perhaps one of the most challenging task related to knowledge engineering. This paper presents a new approach for aligning automatically a very large domain ontology of Species to WordNet in the framework of the KYOTO project. The approach relies on the use of knowledge-based Word Sense Disambiguation algorithm which accurately assigns WordNet synsets to the concepts represented in Species 2000.

2008

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KnowNet: A Proposal for Building Highly Connected and Dense Knowledge Bases from the Web
Montse Cuadros | German Rigau
Semantics in Text Processing. STEP 2008 Conference Proceedings

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KnowNet: Building a Large Net of Knowledge from the Web
Montse Cuadros | German Rigau
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

2007

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SemEval-2007 Task 16: Evaluation of Wide Coverage Knowledge Resources
Montse Cuadros | German Rigau
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)

2006

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Quality Assessment of Large Scale Knowledge Resources
Montse Cuadros | German Rigau
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing