Persona:
Araujo Serna, M. Lourdes

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lurdes@lsi.uned.es
ORCID
0000-0002-7657-4794
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Araujo Serna
Nombre de pila
M. Lourdes
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2009 - 200922010 - 201982020 - 202514
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Mostrando 1 - 10 de 24
  • Miniatura
    Publicación
    Web spam detection : new classification features based on qualified link analysis and language models
    (Institute of Electrical and Electronics Engineers (IEEE), 2010-09-01) Araujo Serna, M. Lourdes; Martínez Romo, Juan
    Web spam is a serious problem for search engines because the quality of their results can be severely degraded by the presence of this kind of page. In this paper, we present an efficient spam detection system based on a classifier that combines new link-based features with language-model (LM)-based ones. These features are not only related to quantitative data extracted from the Web pages, but also to qualitative properties, mainly of the page links.We consider, for instance, the ability of a search engine to find, using information provided by the page for a given link, the page that the link actually points at. This can be regarded as indicative of the link reliability. We also check the coherence between a page and another one pointed at by any of its links. Two pages linked by a hyperlink should be semantically related, by at least a weak contextual relation. Thus, we apply an LM approach to different sources of information from aWeb page that belongs to the context of a link, in order to provide high-quality indicators of Web spam. We have specifically applied the Kullback–Leibler divergence on different combinations of these sources of information in order to characterize the relationship between two linked pages. The result is a system that significantly improves the detection of Web spam using fewer features, on two large and public datasets such as WEBSPAM-UK2006 and WEBSPAM-UK2007.
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    Detecting malicious tweets in trending topics using a statistical analysis of language
    (Elsevier, 2013-06-01) Martínez Romo, Juan; Araujo Serna, M. Lourdes
    Twitter spam detection is a recent area of research in which most previous works had focused on the identification of malicious user accounts and honeypot-based approaches. However, in this paper we present a methodology based on two new aspects: the detection of spam tweets in isolation and without previous information of the user; and the application of a statistical analysis of language to detect spam in trending topics. Trending topics capture the emerging Internet trends and topics of discussion that are in everybody’s lips. This growing microblogging phenomenon therefore allows spammers to disseminate malicious tweets quickly and massively. In this paper we present the first work that tries to detect spam tweets in real time using language as the primary tool. We first collected and labeled a large dataset with 34 K trending topics and 20 million tweets. Then, we have proposed a reduced set of features hardly manipulated by spammers. In addition, we have developed a machine learning system with some orthogonal features that can be combined with other sets of features with the aim of analyzing emergent characteristics of spam in social networks. We have also conducted an extensive evaluation process that has allowed us to show how our system is able to obtain an F-measure at the same level as the best state-ofthe- art systems based on the detection of spam accounts. Thus, our system can be applied to Twitter spam detection in trending topics in real time due mainly to the analysis of tweets instead of user accounts.
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    Discovering HIV related information by means of association rules and machine learning
    (Nature Research, 2022-10-22) Araujo Serna, M. Lourdes; Martínez Romo, Juan; Bisbal, Otilia; Sanchez de Madariaga, Ricardo; The Cohort of the National AIDS Network (CoRIS); https://orcid.org/0000-0003-3746-3378
    Acquired immunodeficiency syndrome (AIDS) is still one of the main health problems worldwide. It is therefore essential to keep making progress in improving the prognosis and quality of life of affected patients. One way to advance along this pathway is to uncover connections between other disorders associated with HIV/AIDS-so that they can be anticipated and possibly mitigated. We propose to achieve this by using Association Rules (ARs). They allow us to represent the dependencies between a number of diseases and other specific diseases. However, classical techniques systematically generate every AR meeting some minimal conditions on data frequency, hence generating a vast amount of uninteresting ARs, which need to be filtered out. The lack of manually annotated ARs has favored unsupervised filtering, even though they produce limited results. In this paper, we propose a semi-supervised system, able to identify relevant ARs among HIV-related diseases with a minimal amount of annotated training data. Our system has been able to extract a good number of relationships between HIV-related diseases that have been previously detected in the literature but are scattered and are often little known. Furthermore, a number of plausible new relationships have shown up which deserve further investigation by qualified medical experts.
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    Anonimización de Informes Médicos
    (Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial, 2021-09-15) Gaitán Rivas, José Antonio; Araujo Serna, M. Lourdes; Martínez, Raquel
    Con el objetivo de mejorar la salud y seguridad de los pacientes cada vez existe un mayor interés en gestionar eficientemente el contenido de los historiales clínicos electrónicos. Dichos informes médicos están escritos principalmente en lenguaje natural, por lo que contienen información no estructurada generalizadamente, haciéndose imprescindibles tecnologías de Minería de Textos y de PLN (Procesamiento de Lenguaje Natural) para su explotación. Con técnicas apropiadas de dichas tecnologías se ayuda en la toma de decisiones clínicas o se facilita la reutilización de medicamentos, entre muchas otras ventajas. Sin embargo, los registros clínicos con información de salud protegida (PHI o Protected Health Information) no pueden ser compartidos directamente debido a restricciones relacionadas con la protección de datos sobre dicha información privada de los pacientes. Es necesaria pues, una anonimización o disociación de dichos registros antes de poder ser usados externamente, debiéndose eliminar total o parcialmente toda información que permita identificar al paciente. La base del presente trabajo ha sido la tarea de evaluación MEDDOCAN (Medical Document Anonymization), a la que puede accederse en https://temu.bsc.es/meddocan , que forma parte de la iniciativa IberLEF 2019, y con la que se organizó un desafío para la comunidad hispano-hablante, con el objetivo de diseñar sistemas eficientes de anonimización de documentos médicos escritos en español. La tarea de MEDDOCAN se estructura en dos subtareas:  Identificación y clasificación de entidades (nombres de paciente, teléfonos, etc.)  Detección de texto sensible La evaluación oficial de la tarea, por tanto, engloba los resultados de ambas subtareas. El corpus está formado por 1.000 estudios de casos clínicos, y cada uno de ellos cuenta, de forma anexa, con expresiones PHI realizadas por profesionales. 4 Del total de 1.000 casos, se reservó el 50% (500 casos) para entrenamiento de la tarea, un 25% (250 casos) para labores de desarrollo, y el otro 25% (250 casos) para pruebas. En el desafío participaron 18 equipos, de un total de 8 nacionalidades distintas, y el mejor resultado, basado en la métrica F-score, fue de 0.9360 para la subtarea 1 (“Identificación y clasificación de entidades”) y de 0.9611 para la subtarea 2 (“Detección de texto sensible”). A lo largo del presente trabajo estudiaremos y compararemos los datos proporcionados por los organizadores de la tarea, y propondremos un sistema que implementa una solución simple mediante técnicas de Aprendizaje Automático y Minería de Textos. Finalmente analizaremos los resultados obtenidos con dicho sistema y serán comparados con los de los participantes en la tarea, exponiendo las ventajas e inconvenientes para la arquitectura escogida, respecto a las presentadas. En dichas conclusiones incorporaremos un listado de posibles mejoras o implementaciones futuras recomendadas para mejorar el rendimiento.
  • Miniatura
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    Semi‑supervised incremental learning with few examples for discovering medical association rules
    (BioMed Central, 2022) Sánchez‑de‑Madariaga, Ricardo; Cantero Escribano, José Miguel; Martínez Romo, Juan; Araujo Serna, M. Lourdes
    Background: Association Rules are one of the main ways to represent structural patterns underlying raw data. They represent dependencies between sets of observations contained in the data. The associations established by these rules are very useful in the medical domain, for example in the predictive health field. Classic algorithms for association rule mining give rise to huge amounts of possible rules that should be filtered in order to select those most likely to be true. Most of the proposed techniques for these tasks are unsupervised. However, the accuracy provided by unsupervised systems is limited. Conversely, resorting to annotated data for training supervised systems is expensive and time‑consuming. The purpose of this research is to design a new semi‑supervised algorithm that performs like supervised algorithms but uses an affordable amount of training data. Methods: In this work we propose a new semi‑supervised data mining model that combines unsupervised techniques (Fisher’s exact test) with limited supervision. Starting with a small seed of annotated data, the model improves results (F‑measure) obtained, using a fully supervised system (standard supervised ML algorithms). The idea is based on utilising the agreement between the predictions of the supervised system and those of the unsupervised techniques in a series of iterative steps. Results: The new semi‑supervised ML algorithm improves the results of supervised algorithms computed using the F‑measure in the task of mining medical association rules, but training with an affordable amount of manually annotated data. Conclusions: Using a small amount of annotated data (which is easily achievable) leads to results similar to those of a supervised system. The proposal may be an important step for the practical development of techniques for mining association rules and generating new valuable scientific medical knowledge.
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    Discovering related scientific literature beyond semantic similarity: a new co-citation approach
    (Springer, 2019-05-17) Rodríguez Prieto, Oscar; Araujo Serna, M. Lourdes; Martínez Romo, Juan
    We propose a new approach to recommend scientific literature, a domain in which the efficient organization and search of information is crucial. The proposed system relies on the hypothesis that two scientific articles are semantically related if they are co-cited more frequently than they would be by pure chance. This relationship can be quantified by the probability of co-citation, obtained from a null model that statistically defines what we consider pure chance. Looking for article pairs that minimize this probability, the system is able to recommend a ranking of articles in response to a given article. This system is included in the co-occurrence paradigm of the field. More specifically, it is based on co-cites so it can produce recommendations more focused on relatedness than on similarity. Evaluation has been performed on the ACL Anthology collection and on the DBLP dataset, and a new corpus has been compiled to evaluate the capacity of the proposal to find relationships beyond similarity. Results show that the system is able to provide, not only articles similar to the submitted one, but also articles presenting other kind of relations, thus providing diversity, i.e. connections to new topics.
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    A Novel Methodology for Enhancing Cross-language and Domain Adaptability in Temporal Expression Normalization
    (Massachusetts Institute of Technology Press, 2025-04-19) Sánchez de Castro Fernández, Alejandro; Araujo Serna, M. Lourdes; Martínez Romo, Juan; Agencia Estatal de Investigación (España); Universidad Nacional de Educación a Distancia (UNED)
    Accurate temporal expression normalization, the process of assigning a numerical value to a temporal expression, is essential for tasks such as timeline creation and temporal reasoning. While rule-based normalization systems are limited in adaptability across different domains and languages, deep-learning solutions in this area have not been extensively explored. An additional challenge is the scarcity of manually annotated corpora with temporal annotations. To address the adaptability limitations of current systems, we propose a highly adaptable methodology that can be applied to multiple domains and languages. This can be achieved by leveraging a multilingual Pre-trained Language Model (PTLM) with a fill-mask architecture, using a Value Intermediate Representation (VIR) where the temporal expression value format is adjusted to the fill-mask representation. Our approach involves a two-phase training process. Initially, the model is trained with a novel masking policy on a large English biomedical corpus that is automatically annotated with normalized temporal expressions, along with a complementary hand-crafted temporal expressions corpus. This addresses the lack of manually annotated data and helps to achieve sufficient capacity for adaptation to diverse domains or languages. In the second phase, we show how the model can be tailored to different domains and languages using various techniques, showcasing the versatility of the proposed methodology. This approach significantly outperforms existing systems.
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    Automatic Recommendation of Forum Threads and Reinforcement Activities in a Data Structure and Programming Course
    (MDPI, 2023-09-21) Plaza Morales, Laura; Araujo Serna, M. Lourdes; López Ostenero, Fernando; Martínez Romo, Juan
    Online learning is quickly becoming a popular choice instead of traditional education. One of its key advantages lies in the flexibility it offers, allowing individuals to tailor their learning experiences to their unique schedules and commitments. Moreover, online learning enhances accessibility to education, breaking down geographical and economical boundaries. In this study, we propose the use of advanced natural language processing techniques to design and implement a recommender that supports e-learning students by tailoring materials and reinforcement activities to students’ needs. When a student posts a query in the course forum, our recommender system provides links to other discussion threads where related questions have been raised and additional activities to reinforce the study of topics that have been challenging. We have developed a content-based recommender that utilizes an algorithm capable of extracting key phrases, terms, and embeddings that describe the concepts in the student query and those present in other conversations and reinforcement activities with high precision. The recommender considers the similarity of the concepts extracted from the query and those covered in the course discussion forum and the exercise database to recommend the most relevant content for the student. Our results indicate that we can recommend both posts and activities with high precision (above 80%) using key phrases to represent the textual content. The primary contributions of this research are three. Firstly, it centers on a remarkably specialized and novel domain; secondly, it introduces an effective recommendation approach exclusively guided by the student’s query. Thirdly, the recommendations not only provide answers to immediate questions, but also encourage further learning through the recommendation of supplementary activities.
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    Structure of morphologically expanded queries : a genetic algorithm approach
    (Elsevier, 2009-10-13) Zaragoza, Hugo; Pérez Agüera, José R.; Pérez Iglesias, Joaquín; Araujo Serna, M. Lourdes
    In this paper we deal with two issues. First, we discuss the negative effects of term correlation in query expansion algorithms, and we propose a novel and simple method (query clauses) to represent expanded queries which may alleviate some of these negative effects. Second, we discuss a method to optimize local query-expansion methods using genetic algorithms, and we apply this method to improve stemming. We evaluate this method with the novel query representation method and show very significant improvements for the problem of stemming optimization.
  • Miniatura
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    Analyzing information retrieval methods to recover broken web links
    (2011-06-19) Martínez Romo, Juan; Araujo Serna, M. Lourdes
    In this work we compare different techniques to automatically find candidate web pages to substitute broken links. We extract information from the anchor text, the content of the page containing the link, and the cache page in some digital library.The selected information is processed and submitted to a search engine. We have compared different information retrievalmethods for both, the selection of terms used to construct the queries submitted to the search engine, and the ranking of the candidate pages that it provides, in order to help the user to find the best replacement. In particular, we have used term frequencies, and a language model approach for the selection of terms; and cooccurrence measures and a language model approach for ranking the final results. To test the different methods, we have also defined a methodology which does not require the user judgments, what increases the objectivity of the results.