Laura Plaza

• Computer Science
• Professor (Assistant) at National University of Distance Education

In this article we present UNED-ACCESS 2024, a bilingual dataset that consists of 1003 multiple-choice questions of university entrance level exams in Spanish and English. Questions are originally formulated in Span-ish and manually translated into English, and have not ever been publicly released, ensuring minimal contamination when evaluating Lar...

In this article we present UNED-ACCESS 2024, a bilingual dataset that consists of 1003 multiple-choice questions of university entrance level exams in Spanish and English. Questions are originally formulated in Spanish and translated manually into English, and have not ever been publicly released. A selection of current open-source and proprietary...

With the rise of social networks, there has been a marked increase in offensive content targeting women, ranging from overt acts of hatred to subtler, often overlooked forms of sexism. The EXIST (sEXism Identification in Social neTworks) competition, initiated in 2021, aimed to advance research in automatically identifying these forms of online sex...

The paper describes the EXIST 2024 lab on Sexism identification in social networks, that is expected to take place at the CLEF 2024 conference and represents the fourth edition of the EXIST challenge. The lab comprises five tasks in two languages, English and Spanish, with the initial three tasks building upon those from EXIST 2023 (sexism identifi...

The increasing interest from research agencies, governments, and universities in understanding research funding and prioritising research efforts has highlighted the need for reliable and efficient methods for exploring research portfolios. In biomedical research, this involves exploring research across what is normally considered fundamental and a...

The detection and analysis of media bias and propaganda have become essential in the current information age. This paper presents our participation in the DIPROMATS task, which focusses on identifying and characterising propaganda techniques in text. We propose a hierarchical model that leverages both the provided DIPROMATS dataset and the SemEval'...

In recent years, the rapid increase in the dissemination of offensive and discriminatory material aimed at women through social media platforms has emerged as a significant concern. This trend has had adverse effects on women’s well-being and their ability to freely express themselves. The EXIST campaign has been promoting research in online sexism...

Detecting media bias is a challenging task due to the complexity and ambiguity of language. Current approaches are limited in their ability to generalise across regions and styles of journalism. This paper proposes a new approach that focusses on detecting rhetorical linguistic techniques rather than just analysing words or contextual representatio...

How similar is the detection of media bias to the detection of persuasive techniques? We have explored how transferring knowledge from one task to the other may help to improve the performance. This paper presents the systems developed for participating in the SemEval-2023 Task 3: Detecting the Genre, the Framing, and the Persuasion Techniques in O...

The paper describes the lab on Sexism identification in social networks (EXIST 2023) that will be hosted as a lab at the CLEF 2023 conference. The lab consists of three tasks, two of which are continuation of EXIST 2022 (sexism detection and sexism categorization) and a third and novel one on source intention identification. For this edition new te...

The paper describes the organization, goals, and results of the sEXism Identification in Social neTworks (EXIST)2022 challenge, a shared task proposed for the second year at IberLEF. EXIST 2022 consists of two challenges: sexism identification and sexism categorization of tweets and gabs, both in Spanish and English. We have received a total of 45...

Detecting and tackling sexist messages in social media is important for encouraging better behaviours in our society as well as to contribute to effective equality between men and women. In this paper we present our participation in the sEXism Identification in Social neTworks (EXIST) task at IberLEF'2021 [1]. Our approach to solve the task is base...

Online reputation management (ORM) comprises the collection of techniques that help monitoring and improving the public image of an entity (companies, products, institutions) on the Internet. The ORM experts try to minimize the negative impact of the information about an entity while maximizing the positive material for being more trustworthy to th...

Producing online reputation summaries for an entity (company, brand, etc.) is a focused summarization task with a distinctive feature: issues that may affect the reputation of the entity take priority in the summary. In this paper we (i) present a new test collection of manually created (abstractive and extractive) reputation reports which summariz...

Given the task of finding influencers of a given domain (i.e. banking) in a social network, in this paper we investigate (i) the importance of characterizing followers for the automatic detection of influencers; (ii) the most effective way to combine signals obtained from followers and from the main profiles for the automatic detection of influence...

During the last decade, hateful and sexist content towards women is being increasingly spread on social networks. The exposure to sexist speech has serious consequences to women’s life and limits their freedom of speech. Previous studies have focused on identifying hatred or violence towards women. However, sexism is expressed in very different for...

Given the task of finding influencers (opinion makers) for a given domain in a social network, we investigate (a) what is the relative importance of domain and authority signals, (b) what is the most effective way of combining signals (voting, classification, learning to rank, etc.) and how best to model the vocabulary signal, and (c) how large is...

Introduction Surveys indicate that patients, particularly those suffering from chronic conditions, strongly benefit from the information found in social networks and online forums. One challenge in accessing online health information is to differentiate between factual and more subjective information. In this work, we evaluate the feasibility of ex...

Introduction Exploiting information in health-related social media services is of great interest for patients, researchers and medical companies. The challenge is, however, to provide easy, quick and relevant access to the vast amount of information that is available. One step towards facilitating information access to online health data is opinion...

Evaluation is crucial in the research and development of automatic summarization applications, in order to determine the appropriateness of a summary based on different criteria, such as the content it contains, and the way it is presented. To perform an adequate evaluation is of great relevance to ensure that automatic summaries can be useful for...

Producing online reputation reports for an entity (company, brand, etc.) is a focused summarization task with a distinctive feature: issues that may affect the reputation of the entity take priority in the summary. In this paper we (i) propose a novel methodology to evaluate summaries in the context of online reputation which profits from an analog...

Background: Research in biomedical text categorization has mostly used the bag-of-words representation. Other more sophisticated representations of text based on syntactic, semantic and argumentative properties have been less studied. In this paper, we evaluate the impact of different text representations of biomedical texts as features for reprod...

Objective: Automatic summarization of biomedical literature usually relies on domain knowledge from external sources to build rich semantic representations of the documents to be summarized. In this paper, we investigate the impact of the knowledge source used on the quality of the summaries that are generated. Materials and methods: We present...

Automatic summarization is emerging as a feasible instrument to help biomedical researchers to access online literature and face information overload. The Natural Language Processing community is actively working toward the development of effective summarization applications; however, automatic summaries are sometimes less informative than the user...

We present an emotion computational model based on social tags. The model is built upon an automatically generated lexicon that describes emotions by means of synonym and antonym terms, and that is linked to multiple domain-specific emotion folksonomies extracted from entertainment social tagging systems. Using these cross-domain folksonomies, we d...

In this paper we present an emotion computational model based on social tags. The model is built upon an automatically generated lexicon that describes emotions by means of synonym and antonym terms, and that is linked to multiple domain-specific emotion folksonomies extracted from entertainment social tagging systems. Using these cross-domain folk...

Negation, intensifiers, and modality are common linguistic constructions that may modify the emotional meaning of the text and therefore need to be taken into consideration in sentiment analysis. Negation is usually considered as a polarity shifter, whereas intensifiers are regarded as amplifiers or diminishers of the strength of such polarity. Mod...

MEDLINE citations are manually indexed at the U.S. National Library of Medicine (NLM) using as reference the Medical Subject Headings (MeSH) controlled vocabulary. For this task, the human indexers read the full text of the article. Due to the growth of MEDLINE, the NLM Indexing Initiative explores indexing methodologies that can support the task o...

Summarization techniques have become increasingly important over the last few years, specially in biomedical research, where information overload is major problem. Researchers of this area need a shorter version of the texts which contains all the important information while discarding irrelevant one. There are several applications which deal with...

We present an emotion model based on social tags, which is built upon an automatically generated lexicon that describes emotions by means of synonym and antonym terms. Using this model we develop a number of methods that transform social tag-based item profiles into emotion-oriented item profiles. We show that the model’s representation of a number...

This paper presents a detailed analysis of the use of crowdsourcing services for the Text Summarization task in the context of the tourist domain. In particular, our aim is to retrieve relevant information about a place or an object pictured in an image in order to provide a short summary which will be of great help for a tourist. For tackling this...

In this article, we investigate what sorts of information humans request about geographical objects of the same type. For example, Edinburgh Castle and Bodiam Castle are two objects of the same type: “castle.” The question is whether specific information is requested for the object type “castle” and how this information differs for objects of other...

Background The position of a sentence in a document has been traditionally considered an indicator of the relevance of the sentence, and therefore it is frequently used by automatic summarization systems as an attribute for sentence selection. Sentences close to the beginning of the document are supposed to deal with the main topic and thus are sel...

Sentiment Analysis is a novel and broad area of Natural Language Processing (NLP) aiming to understand people's sentiments and opinions about a given topic. In particular, this chapter focuses on the application of Sentiment Analysis to automatically evaluate online products and services reviews. Undoubtedly, the information in customer reviews is...

This paper reports an initial study that aims to assess the viability of multi-document summarization techniques for automatic captioning of geo-referenced images. The automatic captioning procedure requires summarizing multiple Web documents that contain information related to images’ location. We use different state-of-the art summarization syste...

Access to the vast body of research literature that is now available on biomedicine and related fields can be improved with automatic summarization. This paper describes a summarization system for the biomedical domain that represents documents as graphs formed from concepts and relations in the UMLS Metathesaurus. This system has to deal with the...

This paper presents one of the two contributions from the Universidad Complutense de Madrid to the *SEM Shared Task 2012 on Resolving the Scope and Focus of Negation. We describe a rule-based system for detecting the presence of negations and delimitating their scope. It was initially intended for processing negation in opinionated texts, and has b...

UCM-2 infers the words that are affected by negations by browsing dependency syntactic structures. It first makes use of an algorithm that detects negation cues, like no, not or nothing, and the words affected by them by traversing Minipar dependency structures. Second, the scope of these negation cues is computed by using a post-processing rule-ba...

This paper presents SentiSense, a concept-based affective lexicon. It is intended to be used in sentiment analysis-related tasks, specially in polarity and intensity classification and emotion identification. SentiSense attaches emotional meanings to concepts from the WordNet lexical database, instead of terms, thus allowing to address the word amb...

Sentiment Analysis is a novel and broad area of Natural Language Processing (NLP) aiming to understand people’s sentiments and opinions about a given topic. In particular, this chapter focuses on the application of Sentiment Analysis to automatically evaluate online products and services reviews. Undoubtedly, the information in customer reviews is...

Access to the vast body of research literature that is available in biomedicine and related fields may be improved by automatic summarisation. This paper presents a method for summarising biomedical scientific literature that takes into consideration the characteristics of the domain and the type of documents. To address the problem of identifying...

Word sense disambiguation (WSD) attempts to solve lexical ambiguities by identifying the correct meaning of a word based on its context. WSD has been demonstrated to be an important step in knowledge-based approaches to automatic summarization. However, the correlation between the accuracy of the WSD methods and the summarization performance has ne...

The information in customer reviews is of great interest to both companies and consumers. This information is usually presented as non-structured free-text so that automatically extracting and rating user opinions about a product is a challenging task. Moreover, this opinion highly depends on the product features on which the user judgments and imp...

This paper presents a semantic graph-based method for extractive summarization. The summarizer uses WordNet concepts and relations to produce a semantic graph that represents the document, and a degree-based clustering algorithm is used to discover different themes or topics within the text. The selection of sentences for the summary is based on th...

The aim of the AutoIndexer project is the development of an infrastructure of applications for automatic indexing clinical documents using advanced language resources and technologies. © 2011 Sociedad Española Para el Procesamiento del Lenguaje Natural.

This paper presents two different approaches to automatic captioning of geo-tagged images by summarizing multiple web-documents that contain information related to an image’s location: a graph-based and a statistical-based approach. The graph-based method uses text cohesion techniques to identify information relevant to a location. The statistical-...

Physicians often use information from previous clinical cases in their decision-making process. However, the large amount of patient records available in hospitals makes an exhaustive search unfeasible. We propose a method for the retrieval of similar clinical cases, based on mapping the text onto UMLS concepts and representing the patient records...

Los autores declaran no tener ningún tipo de interés económico o comercial RESUMEn En un entorno como el de la medicina, caracterizado por la sobrecarga de trabajo y la escasez de tiempo, los sistemas in-teligentes de acceso a la información pueden y deben utilizarse para facilitar la labor de investigadores y profesionales. Sin embargo, sorprende...

This paper presents the process of development and the characteristics of an evaluation collection for a personalisation system for digital newspapers. This system selects, adapts and presents contents according to a user model that define information needs. The collection presented here contains data that are cross-related over four different axes...

We describe a concept-based summarization system for biomedical documents and show that its performance can be improved using Word Sense Disambiguation. The system represents the documents as graphs formed from concepts and relations from the UMLS. A degree-based clustering algorithm is applied to these graphs to discover different themes or topics...

In this paper, the authors present a new approach to sentence level sentiment analysis. The aim is to determine whether a sentence expresses a positive, negative or neutral sentiment, as well as its intensity. The method performs WSD over the words in the sentence in order to work with concepts rather than terms, and makes use of the knowledge in a...

One of the main handicaps in research on automatic summarization is the vague semantic comprehension of the source, which is reflected in the poor quality of the consequent summaries. Using further knowledge, as that provided by ontologies, to construct a complex semantic representation of the text, can considerably alleviate the problem. In this p...

One of the main problems in research on automatic summarization is the inaccurate semantic interpretation of the source. Using specific domain knowledge can considerably alleviate the problem. In this paper, we introduce an ontology-based extractive method for summarization. It is based on mapping the text to concepts and representing the document...

Uno de los principales problemas en la investigación sobre generación automática de resúmenes (GAR) es la falta de utilización de conocimiento de dominio, que se refleja en la incorrecta interpretación semántica del documento y la baja calidad de los resúmenes obtenidos. En este trabajo se propone un método de extracción de oraciones para la GAR de...

En esta memoria de tesis se propone una arquitectura para la generación de resúmenes informativos monodocumento en un dominio específico: la biomedicina. La utilidad de estos resúmenes es indudable, en un campo en el que los profesionales han de estar continuamente al corriente de los nuevos avances científicos, pero a la vez necesitan economizar e...