Philosophiae Doctor - PhD / To ameliorate Hepatitis C Virus (HCV) therapeutic and diagnostic challenges requires robust intervention strategies, including approaches that leverage the plethora of rich data published in biomedical literature to gain greater understanding of HCV pathobiological mechanisms. The multitudes of metadata originating from HCV clinical trials as well as low and high-throughput experiments embedded in text corpora can be mined as data sources for the implementation of HCV-specific resources. HCV-customized resources may support the generation of worthy and testable hypothesis and reveal potential research clues to augment the pursuit of efficient diagnostic biomarkers and therapeutic targets. This research thesis report the development of two freely available HCV-specific web-based resources: (i) Dragon Exploratory System on Hepatitis C Virus (DESHCV) accessible via http://apps.sanbi.ac.za/DESHCV/ or http://cbrc.kaust.edu.sa/deshcv/ and(ii) Hepatitis C Virus Protein Interaction Database (HCVpro) accessible via http://apps.sanbi.ac.za/hcvpro/ or http://cbrc.kaust.edu.sa/hcvpro/.DESHCV is a text mining system implemented using named concept recognition and cooccurrence based approaches to computationally analyze about 32, 000 HCV related abstracts obtained from PubMed. As part of DESHCV development, the pre-constructed dictionaries of the Dragon Exploratory System (DES) were enriched with HCV biomedical concepts, including HCV proteins, name variants and symbols to enable HCV knowledge specific exploration. The DESHCV query inputs consist of user-defined keywords, phrases and concepts. DESHCV is therefore an information extraction tool that enables users to computationally generate association between concepts and support the prediction of potential hypothesis with diagnostic and therapeutic relevance.Additionally, users can retrieve a list of abstracts containing tagged concepts that can be used to overcome the herculean task of manual biocuration. DESHCV has been used to simulate previously reported thalidomide-chronic hepatitis C hypothesis and also to model a potentially novel thalidomide-amantadine hypothesis.HCVpro is a relational knowledgebase dedicated to housing experimentally detected HCV-HCV and HCV-human protein interaction information obtained from other databases and curated from biomedical journal articles. Additionally, the database contains consolidated biological information consisting of hepatocellular carcinoma(HCC) related genes, comprehensive reviews on HCV biology and drug development,functional genomics and molecular biology data, and cross-referenced links to canonical pathways and other essential biomedical databases. Users can retrieve enriched information including interaction metadata from HCVpro by using protein identifiers,gene chromosomal locations, experiment types used in detecting the interactions, PubMed IDs of journal articles reporting the interactions, annotated protein interaction IDs from external databases, and via “string searches”. The utility of HCVpro has been demonstrated by harnessing integrated data to suggest putative baseline clues that seem to support current diagnostic exploratory efforts directed towards vimentin. Furthermore,eight genes comprising of ACLY, AZGP1, DDX3X, FGG, H19, SIAH1, SERPING1 and THBS1 have been recommended for possible investigation to evaluate their diagnostic potential. The data archived in HCVpro can be utilized to support protein-protein interaction network-based candidate HCC gene prioritization for possible validation by experimental biologists.

With the significant advancementstaking place in the last three decades in the field ofInformation Technology (IT), we are witnesses of an era unprecedented to the standards that mankind was used to, for centuries. Having access to a huge amount of dataalmost instantly,entails certainadvantages. One of which is the ability to observe in which segments of their expertise do scientists focus their research. That kind of knowledge, if properly appraised could hold the key to explaining what the new directions of the applied sciences will be and thus could help to constructing a “map” of the future developments from the Research and Development labs of the industries worldwide.Though the above statement may be considered too “futuristic”, already there have been documented attempts in the literature that have been fruitful into using vast amount of scientific data in an attempt to outline future scientific trends and thus scientific discoveries.The purpose of this research is to try to use a pioneeringmethodof modeling text corpora that already hasbeen used previously to the task of mapping the history of scientific discovery, that of Latent Dirichlet Allocation (LDA)and try to evaluate itsusability into detecting emerging research trends by the mere use of only the “Abstracts” from a collectionof scientific articles.To do that an experimental set is being utilized and the process is repeated over three experimental runs.The results, although not the ones that would validate the hypothesis, are showing that with certain improvements in the processing the hypothesis could be confirmed.

Extracting information from a stack of data is a tedious task and the scenario is no different in proteomics. Volumes of research papers are published about study of various proteins in several species, their interactions with other proteins and identification of protein(s) as possible biomarker in causing diseases. It is a challenging task for biologists to keep track of these developments manually by reading through the literatures. Several tools have been developed by computer linguists to assist identification, extraction and hypotheses generation of proteins and protein-protein interactions from biomedical publications and protein databases. However, they are confronted with the challenges of term variation, term ambiguity, access only to abstracts and inconsistencies in time-consuming manual curation of protein and protein-protein interaction repositories. This work attempts to attenuate the challenges by extracting protein-protein interactions in humans and elicit possible interactions using associative rule mining on full text, abstracts and captions from figures available from publicly available biomedical literature databases. Two such databases are used in our study: Directory of Open Access Journals (DOAJ) and PubMed Central (PMC). A corpus is built using articles based on search terms. A dataset of more than 38,000 protein-protein interactions from the Human Protein Reference Database (HPRD) is cross-referenced to validate discovered interactive pairs. A set of an optimal size of possible binary protein-protein interactions is generated to be made available for clinician or biological validation. A significant change in the number of new associations was found by altering the thresholds for support and confidence metrics. This study narrows down the limitations for biologists in keeping pace with discovery of protein-protein interactions via manually reading the literature and their needs to validate each and every possible interaction.

Selecionar atributos é, por vezes, uma atividade necessária para o correto desenvolvimento de tarefas de aprendizado de máquina. Em Mineração de Textos, reduzir o número de atributos em uma base de textos é essencial para a eficácia do processo e a compreensibilidade do conhecimento extraído, uma vez que se lida com espaços de alta dimensionalidade e esparsos. Quando se lida com contextos nos quais a coleção de textos é não-rotulada, métodos não-supervisionados de redução de atributos são utilizados. No entanto, não existe forma geral predefinida para a obtenção de medidas de utilidade de atributos em métodos não-supervisionados, demandando um esforço maior em sua realização. Assim, este trabalho aborda a seleção não-supervisionada de atributos por meio de um estudo exploratório de métodos dessa natureza, comparando a eficácia de cada um deles na redução do número de atributos em aplicações de Mineração de Textos. Dez métodos são comparados - Ranking porTerm Frequency, Ranking por Document Frequency, Term Frequency-Inverse Document Frequency, Term Contribution, Term Variance, Term Variance Quality, Método de Luhn, Método LuhnDF, Método de Salton e Zone-Scored Term Frequency - sendo dois deles aqui propostos - Método LuhnDF e Zone-Scored Term Frequency. A avaliação se dá em dois focos, supervisionado, pelo medida de acurácia de quatro classificadores (C4.5, SVM, KNN e Naïve Bayes), e não-supervisionado, por meio da medida estatística de Expected Mutual Information Measure. Aos resultados de avaliação, aplica-se o teste estatístico de Kruskal-Wallis para determinação de significância estatística na diferença de desempenho dos diferentes métodos de seleção de atributos comparados. Seis bases de textos são utilizadas nas avaliações experimentais, cada uma relativa a um grande domínio e contendo subdomínios, os quais correspondiam às classes usadas para avaliação supervisionada. Com esse estudo, este trabalho visa contribuir com uma aplicação de Mineração de Textos que visa extrair taxonomias de tópicos a partir de bases textuais não-rotuladas, selecionando os atributos mais representativos em uma coleção de textos. Os resultados das avaliações mostram que não há diferença estatística significativa entre os métodos não-supervisionados de seleção de atributos comparados. Além disso, comparações desses métodos não-supervisionados com outros supervisionados (Razão de Ganho e Ganho de Informação) apontam que é possível utilizar os métodos não-supervisionados em atividades supervisionadas de Mineração de Textos, obtendo eficiência compatível com os métodos supervisionados, dado que não detectou-se diferença estatística nessas comparações, e com um custo computacional menor / Feature selection is an activity sometimes necessary to obtain good results in machine learning tasks. In Text Mining, reducing the number of features in a text base is essential for the effectiveness of the process and the comprehensibility of the extracted knowledge, since it deals with high dimensionalities and sparse contexts. When dealing with contexts in which the text collection is not labeled, unsupervised methods for feature reduction have to be used. However, there aren\'t any general predefined feature quality measures for unsupervised methods, therefore demanding a higher effort for its execution. So, this work broaches the unsupervised feature selection through an exploratory study of methods of this kind, comparing their efficacies in the reduction of the number of features in the Text Mining process. Ten methods are compared - Ranking by Term Frequency, Ranking by Document Frequency, Term Frequency-Inverse Document Frequency, Term Contribution, Term Variance, Term Variance Quality, Luhn\'s Method, LuhnDF Method, Salton\'s Method and Zone-Scored Term Frequency - and two of them are proposed in this work - LuhnDF Method and Zone-Scored Term Frequency. The evaluation process is done in two ways, supervised, through the accuracy measure of four classifiers (C4.5, SVM, KNN and Naïve Bayes), and unsupervised, using the Expected Mutual Information Measure. The evaluation results are submitted to the statistical test of Kruskal-Wallis in order to determine the statistical significance of the performance difference of the different feature selection methods. Six text bases are used in the experimental evaluation, each one related to one domain and containing sub domains, which correspond to the classes used for supervised evaluation. Through this study, this work aims to contribute with a Text Mining application that extracts topic taxonomies from unlabeled text collections, through the selection of the most representative features in a text collection. The evaluation results show that there is no statistical difference between the unsupervised feature selection methods compared. Moreover, comparisons of these unsupervised methods with other supervised ones (Gain Ratio and Information Gain) show that it is possible to use unsupervised methods in supervised Text Mining activities, obtaining an efficiency compatible with supervised methods, since there isn\'t any statistical difference the statistical test detected in these comparisons, and with a lower computational effort

Com a grande variedade de produtos e serviços disponíveis na Web, os usuários possuem, em geral, muita liberdade de escolha, o que poderia ser considerado uma vantagem se não fosse pela dificuldade encontrada em escolher o produto ou serviço que mais atenda a suas necessidades dentro do vasto conjunto de opções disponíveis. Sistemas de recomendação são sistemas que têm como objetivo auxiliar esses usuários a identificarem itens de interesse em um conjunto de opções. A maioria das abordagens de sistemas de recomendação foca em recomendar itens mais relevantes para usuários individuais, não levando em consideração o contexto dos usuários. Porém, em muitas aplicações é importante também considerar informações contextuais para fazer as recomendações. Por exemplo, um usuário pode desejar assistir um filme com a sua namorada no sábado à noite ou com os seus amigos durante um dia de semana, e uma locadora de filmes na Web pode recomendar diferentes tipos de filmes para este usuário dependendo do contexto no qual este se encontra. Um grande desafio para o uso de sistemas de recomendação sensíveis ao contexto é a falta de métodos para aquisição automática de informação contextual para estes sistemas. Diante desse cenário, neste trabalho é proposto um método para extrair informações contextuais do conteúdo de páginas Web que consiste em construir hierarquias de tópicos do conteúdo textual das páginas considerando, além da bag-of-words tradicional (informação técnica), também informações mais valiosas dos textos como entidades nomeadas e termos do domínio (informação privilegiada). Os tópicos extraídos das hierarquias das páginas Web são utilizados como informações de contexto em sistemas de recomendação sensíveis ao contexto. Neste trabalho foram realizados experimentos para avaliação do contexto extraído pelo método proposto em que foram considerados dois baselines: um sistema de recomendação que não considera informação de contexto e um método da literatura de extração de contexto implementado e adaptado para este mestrado. Além disso, foram utilizadas duas bases de dados. Os resultados obtidos foram, de forma geral, muito bons apresentando ganhos significativos sobre o baseline sem contexto. Com relação ao baseline que extrai informação contextual, o método proposto se mostrou equivalente ou melhor que o mesmo. / With the wide variety of products and services available on the web, it is difficult for users to choose the option that most meets their needs. In order to reduce or even eliminate this difficulty, recommender systems have emerged. A recommender system is used in various fields to recommend items of interest to users. Most recommender approaches focus only on users and items to make the recommendations. However, in many applications it is also important to incorporate contextual information into the recommendation process. For example, a user may want to watch a movie with his girlfriend on Saturday night or with his friends during a weekday, and a video store on the Web can recommend different types of movies for this user depending on his context. Although the use of contextual information by recommendation systems has received great focus in recent years, there is a lack of automatic methods to obtain such information for context-aware recommender systems. For this reason, the acquisition of contextual information is a research area that needs to be better explored. In this scenario, this work proposes a method to extract contextual information of Web page content. This method builds topic hierarchies of the pages textual content considering, besides the traditional bag-of-words, valuable information of texts as named entities and domain terms (privileged information). The topics extracted from the hierarchies are used as contextual information in context-aware recommender systems. By using two databases, experiments were conducted to evaluate the contextual information extracted by the proposed method. Two baselines were considered: a recommendation system that does not use contextual information (IBCF) and a method proposed in literature to extract contextual information (\\methodological\" baseline), adapted for this research. The results are, in general, very good and show significant gains over the baseline without context. Regarding the \"methodological\" baseline, the proposed method is equivalent to or better than this baseline.

In this dissertation, we deal with the cross-view tasks related to information retrieval using embedding methods. We study existing methodologies and propose new methods to overcome their limitations. We formally introduce the concept of mixed-script IR, which deals with the challenges faced by an IR system when a language is written in different scripts because of various technological and sociological factors. Mixed-script terms are represented by a small and finite feature space comprised of character n-grams. We propose the cross-view autoencoder (CAE) to model such terms in an abstract space and CAE provides the state-of-the-art performance. We study a wide variety of models for cross-language information retrieval (CLIR) and propose a model based on compositional neural networks (XCNN) which overcomes the limitations of the existing methods and achieves the best results for many CLIR tasks such as ad-hoc retrieval, parallel sentence retrieval and cross-language plagiarism detection. We empirically test the proposed models for these tasks on publicly available datasets and present the results with analyses. In this dissertation, we also explore an effective method to incorporate contextual similarity for lexical selection in machine translation. Concretely, we investigate a feature based on context available in source sentence calculated using deep autoencoders. The proposed feature exhibits statistically significant improvements over the strong baselines for English-to-Spanish and English-to-Hindi translation tasks. Finally, we explore the the methods to evaluate the quality of autoencoder generated representations of text data and analyse its architectural properties. For this, we propose two metrics based on reconstruction capabilities of the autoencoders: structure preservation index (SPI) and similarity accumulation index (SAI). We also introduce a concept of critical bottleneck dimensionality (CBD) below which the structural information is lost and present analyses linking CBD and language perplexity. / En esta disertación estudiamos problemas de vistas-múltiples relacionados con la recuperación de información utilizando técnicas de representación en espacios de baja dimensionalidad. Estudiamos las técnicas existentes y proponemos nuevas técnicas para solventar algunas de las limitaciones existentes. Presentamos formalmente el concepto de recuperación de información con escritura mixta, el cual trata las dificultades de los sistemas de recuperación de información cuando los textos contienen escrituras en distintos alfabetos debido a razones tecnológicas y socioculturales. Las palabras en escritura mixta son representadas en un espacio de características finito y reducido, compuesto por n-gramas de caracteres. Proponemos los auto-codificadores de vistas-múltiples (CAE, por sus siglas en inglés) para modelar dichas palabras en un espacio abstracto, y esta técnica produce resultados de vanguardia. En este sentido, estudiamos varios modelos para la recuperación de información entre lenguas diferentes (CLIR, por sus siglas en inglés) y proponemos un modelo basado en redes neuronales composicionales (XCNN, por sus siglas en inglés), el cual supera las limitaciones de los métodos existentes. El método de XCNN propuesto produce mejores resultados en diferentes tareas de CLIR tales como la recuperación de información ad-hoc, la identificación de oraciones equivalentes en lenguas distintas y la detección de plagio entre lenguas diferentes. Para tal efecto, realizamos pruebas experimentales para dichas tareas sobre conjuntos de datos disponibles públicamente, presentando los resultados y análisis correspondientes. En esta disertación, también exploramos un método eficiente para utilizar similitud semántica de contextos en el proceso de selección léxica en traducción automática. Específicamente, proponemos características extraídas de los contextos disponibles en las oraciones fuentes mediante el uso de auto-codificadores. El uso de las características propuestas demuestra mejoras estadísticamente significativas sobre sistemas de traducción robustos para las tareas de traducción entre inglés y español, e inglés e hindú. Finalmente, exploramos métodos para evaluar la calidad de las representaciones de datos de texto generadas por los auto-codificadores, a la vez que analizamos las propiedades de sus arquitecturas. Como resultado, proponemos dos nuevas métricas para cuantificar la calidad de las reconstrucciones generadas por los auto-codificadores: el índice de preservación de estructura (SPI, por sus siglas en inglés) y el índice de acumulación de similitud (SAI, por sus siglas en inglés). También presentamos el concepto de dimensión crítica de cuello de botella (CBD, por sus siglas en inglés), por debajo de la cual la información estructural se deteriora. Mostramos que, interesantemente, la CBD está relacionada con la perplejidad de la lengua. / En aquesta dissertació estudiem els problemes de vistes-múltiples relacionats amb la recuperació d'informació utilitzant tècniques de representació en espais de baixa dimensionalitat. Estudiem les tècniques existents i en proposem unes de noves per solucionar algunes de les limitacions existents. Presentem formalment el concepte de recuperació d'informació amb escriptura mixta, el qual tracta les dificultats dels sistemes de recuperació d'informació quan els textos contenen escriptures en diferents alfabets per motius tecnològics i socioculturals. Les paraules en escriptura mixta són representades en un espai de característiques finit i reduït, composat per n-grames de caràcters. Proposem els auto-codificadors de vistes-múltiples (CAE, per les seves sigles en anglès) per modelar aquestes paraules en un espai abstracte, i aquesta tècnica produeix resultats d'avantguarda. En aquest sentit, estudiem diversos models per a la recuperació d'informació entre llengües diferents (CLIR , per les sevas sigles en anglès) i proposem un model basat en xarxes neuronals composicionals (XCNN, per les sevas sigles en anglès), el qual supera les limitacions dels mètodes existents. El mètode de XCNN proposat produeix millors resultats en diferents tasques de CLIR com ara la recuperació d'informació ad-hoc, la identificació d'oracions equivalents en llengües diferents, i la detecció de plagi entre llengües diferents. Per a tal efecte, realitzem proves experimentals per aquestes tasques sobre conjunts de dades disponibles públicament, presentant els resultats i anàlisis corresponents. En aquesta dissertació, també explorem un mètode eficient per utilitzar similitud semàntica de contextos en el procés de selecció lèxica en traducció automàtica. Específicament, proposem característiques extretes dels contextos disponibles a les oracions fonts mitjançant l'ús d'auto-codificadors. L'ús de les característiques proposades demostra millores estadísticament significatives sobre sistemes de traducció robustos per a les tasques de traducció entre anglès i espanyol, i anglès i hindú. Finalment, explorem mètodes per avaluar la qualitat de les representacions de dades de text generades pels auto-codificadors, alhora que analitzem les propietats de les seves arquitectures. Com a resultat, proposem dues noves mètriques per quantificar la qualitat de les reconstruccions generades pels auto-codificadors: l'índex de preservació d'estructura (SCI, per les seves sigles en anglès) i l'índex d'acumulació de similitud (SAI, per les seves sigles en anglès). També presentem el concepte de dimensió crítica de coll d'ampolla (CBD, per les seves sigles en anglès), per sota de la qual la informació estructural es deteriora. Mostrem que, de manera interessant, la CBD està relacionada amb la perplexitat de la llengua. / Gupta, PA. (2017). Cross-view Embeddings for Information Retrieval [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/78457 / TESIS

La veille en santé animale, notamment la détection précoce de l'émergence d'agents pathogènes exotiques et émergents à l'échelle mondiale, est l'un des moyens de lutte contre l'introduction de ces agents pathogènes en France.Récemment, il y a eu une réelle prise de conscience par les autorités sanitaires de l'utilité de l'information non-structurée concernant les maladies infectieuses publiée sur le Web.C'est dans ce contexte que nous proposons un outil de veille basé sur une méthode de fouille de textes pour la détection, collecte, catégorisation et extraction de l'information sanitaire à partir des donnés textuelles non structurées (articles médias) publiées sur le Web.Notre méthode est générique. Toutefois, pour l'élaborer, nous l'appliquons à cinq maladies animales infectieuses exotiques : la peste porcine africaine, la fièvre aphteuse, la fièvre catarrhale ovine, la maladie du virus Schmallenberg et l'influenza aviaire.Nous démontrons que des techniques de fouille de textes, complétées par les connaissances d'experts du domaine, sont la fondation d'une veille sanitaire du Web à la fois efficace et réactive pour détecter des émergences de maladies exotiques au niveau international.Notre outil sera utilisé par le dispositif de veille sanitaire internationale en France, et facilitera la détection précoce de signaux de dangers sanitaires émergents dans les articles médias du Web. / Monitoring animal health worldwide, especially the early detection of outbreaks of emerging and exotic pathogens, is one of the means of preventing the introduction of infectious diseases in France.Recently, there is an increasing awareness among health authorities for the use of unstructured information published on the Web for epidemic intelligence purposes.In this manuscript we present a semi-automatic text mining approach, which detects, collects, classifies and extracts information from non-structured textual data available in the media reports on the Web. Our approach is generic; however, it was elaborated using five exotic animal infectious diseases: african swine fever, foot-and-mouth disease, bluetongue, Schmallenberg, and avian influenza.We show that the text mining techniques, supplemented by the knowledge of domain experts, are the foundation of an efficient and reactive system for monitoring animal health emergence on the Web.Our tool will be used by the French epidemic intelligence team for international monitoring of animal health, and will facilitate the early detection of events related to emerging health hazards identified from media reports on the Web.

So far, studies seeking to recognise moral foundations in texts have been relatively successful (Araque et al., 2019; Lin et al., 2018; Mooijman et al., 2017; Rezapouret al., 2019). There are, however, two issues with these studies: Firstly, it is an extensive process to gather and annotate sufficient material for training. Secondly, models are only trained and tested within the same domain. It is yet unexplored how these models for moral foundation prediction perform when tested in other domains, but from their experience with annotation, Hoover et al. (2017) describe how moral sentiments on one topic (e.g. black lives matter) might be completely different from moral sentiments on another (e.g. presidential elections). This study attempts to explore to what extent models generalise to other domains. More specifically, we focus on training on Twitter data from non-extremist sources, and testing on data from an extremist (white nationalist) forum. We conducted two experiments. In our first experiment we test whether it is possible to do cross domain classification of moral foundations. Additionally, we compare the performance of a model using the Word2Vec embeddings used in previous studies to a model using the newer BERT embeddings. We find that although the performance drops significantly on the extremist out-domain test sets, out-domain classification is not impossible. Furthermore, we find that the BERT model generalises marginally better to the out-domain test set, than the Word2Vec model. In our second experiment we attempt to improve the generalisation to extremist test data by providing contextual knowledge. Although this does not improve the model, it does show the model’s robustness against noise. Finally we suggest an alternative approach for accounting for contextual knowledge.

This work deals with the problem of text mining which is becoming more popular due to exponential growth of the data in electronic form. The work explores contemporary methods and their improvement using optimization methods, as well as the problem of text data understanding in general. The work addresses the problem in three ways: using traditional methods and their optimizations, using Big Data in train phase and abstraction through the minimization of language-dependent parts, and introduction of the new method based on the deep learning which is closer to how human reads and understands text data. The main aim of the dissertation was to propose a method for machine understanding of unstructured text data. The method was experimentally verified by classification of text data on 5 different languages – Czech, English, German, Spanish and Chinese. This demonstrates possible application to different languages families. Validation on the Yelp evaluation database achieve accuracy higher by 0.5% than current methods.

Lemmatization is an important preprocessing step for many applications of text mining. Lemmatization process is similar to the stemming process, with the difference that determines not only the word stem, but it´s trying to determines the basic form of the word using the methods Brute Force and Suffix Stripping. The main aim of this paper is to present methods for algorithmic improvements Czech lemmatization. The created training set of data are content of this paper and can be freely used for student and academic works dealing with similar problematics.