Submitted by William Justo Figueiro (williamjf) on 2015-07-18T12:21:33Z No. of bitstreams: 1 42c.pdf: 1016491 bytes, checksum: 407619e0114b592531ee5a68ca0fd0f9 (MD5) / Made available in DSpace on 2015-07-18T12:21:33Z (GMT). No. of bitstreams: 1 42c.pdf: 1016491 bytes, checksum: 407619e0114b592531ee5a68ca0fd0f9 (MD5) Previous issue date: 2012 / UNISINOS - Universidade do Vale do Rio dos Sinos / Este trabalho propõe um processo de investigação do conteúdo de uma base de dados, composta por dados descritivos e pré-estruturados do domínio da saúde, mais especificamente da área da Reumatologia. Para a investigação da base de dados, foram compostos 3 conjuntos de interesse. O primeiro composto por uma classe com conteúdo descritivo relativo somente a área da Reumatologia em geral, e outra cujo seu conteúdo pertence a outras áreas da medicina. O segundo e o terceiro conjunto, foram constituídos após análises estatísticas na base de dados. Um formado pelo conteúdo descritivo associado as 5 maiores frequências de códigos CID, e outro formado por conteúdo descritivo associado as 3 maiores frequências de códigos CID relacionados exclusivamente à área da Reumatologia. Estes conjuntos foram pré-processados com técnicas clássicas de Pré-processamento tais como remoção de Stopwords e Stemmer. Com o objetivo de extrair padrões que através de sua interpretação resultem na produção de conhecimento, foram aplicados aos conjuntos de interesse técnicas de classificação e associação, visando à relação entre o conteúdo textual que descreve sintomas de doenças com o conteúdo pré-estruturado, que define o diagnóstico destas doenças. A execução destas técnicas foi realizada através da aplicação do algoritmo de classificação Support Vector Machines e do algoritmo para extração de Regras de Associação Apriori. Para o desenvolvimento deste processo foi pesquisado referencial teórico relativo à mineração de dados, bem como levantamento e estudo de trabalhos científicos produzidos no domínio da mineração textual e relacionados a Prontuário Médico Eletrônico, focando o conteúdo das bases de dados utilizadas, técnicas de pré-processamento e mineração empregados na literatura, bem como os resultados relatados. A técnica de classificação empregada neste trabalho obteve resultados acima de 80% de Acurácia, demonstrando capacidade do algoritmo de rotular dados da saúde relacionados ao domínio de interesse corretamente. Também foram descobertas associações entre conteúdo textual e conteúdo pré-estruturado, que segundo a análise de especialistas, podem conduzir a questionamentos quanto à utilização de determinados CIDs no local de origem dos dados. / This study suggests a process of investigation of the content of a database, comprising descriptive and pre-structured data related to the health domain, more particularly in the area of Rheumatology. For the investigation of the database, three sets of interest were composed. The first one formed by a class of descriptive content related only to the area of Rheumatology in general, and another whose content belongs to other areas of medicine. The second and third sets were constituted after statistical analysis in the database. One of them formed by the descriptive content associated to the five highest frequencies of ICD codes, and another formed by descriptive content associated with the three highest frequencies of ICD codes related exclusively to the area of Rheumatology. These sets were pre-processed with classic Pre-processing techniques such as Stopword Removal and Stemming. In order to extract patterns that, through their interpretation, result in knowledge production, association and classification techniques were applied to the sets of interest, aiming at to relate the textual content that describes symptoms of diseases with pre-structured content, which defines the diagnosis of these diseases. The implementation of these techniques was carried out by applying the classification algorithm Support Vector Machines and the Association Rules Apriori Algorithm. For the development of this process, theoretical references concerning data mining were researched, including selection and review of scientific publications produced on text mining and related to Electronic Medical Record, focusing on the content of the databases used, techniques for pre-processing and mining used in the literature, as well as the reported results. The classification technique used in this study reached over 80% accurate results, demonstrating the capacity the algorithm has to correctly label health data related to the field of interest. Associations between text content and pre-structured content were also found, which, according to expert analysis, may be questioned as for the use of certain ICDs in the place of origin of the data.

A mineração de textos vem de encontro à realidade atual de se compreender e utilizar grandes massas de dados textuais. Uma forma de auxiliar a compreensão dessas coleções de textos é construir taxonomias de tópicos a partir delas. As taxonomias de tópicos devem organizar esses documentos, preferencialmente em hierarquias, identificando os grupos obtidos por meio de descritores. Construir manual, automática ou semi-automaticamente taxonomias de tópicos de qualidade é uma tarefa nada trivial. Assim, o objetivo deste trabalho é construir taxonomias de tópicos em domínios de conhecimento restrito, por meio de mineração de textos, a fim de auxiliar o especialista no domínio a compreender e organizar os textos. O domínio de conhecimento é restrito para que se possa trabalhar apenas com métodos de aprendizado estatístico não supervisionado sobre representações bag of words dos textos. Essas representações independem do contexto das palavras nos textos e, conseqüentemente, nos domínios. Assim, ao se restringir o domínio espera-se diminuir erros de interpretação dos resultados. A metodologia proposta para a construção de taxonomias de tópicos é uma instanciação do processo de mineração de textos. A cada etapa do processo propôem-se soluções adaptadas às necessidades específicas de construçao de taxonomias de tópicos, dentre as quais algumas contribuições inovadoras ao estado da arte. Particularmente, este trabalho contribui em três frentes no estado da arte: seleção de atributos n-gramas em tarefas de mineração de textos, dois modelos para rotulação de agrupamento hierárquico de documentos e modelo de validação do processo de rotulação de agrupamento hierárquico de documentos. Além dessas contribuições, ocorrem outras em adaptações e metodologias de escolha de processos de seleção de atributos, forma de geração de atributos, visualização das taxonomias e redução das taxonomias obtidas. Finalmente, a metodologia desenvolvida foi aplicada a problemas reais, tendo obtido bons resultados. / Text mining provides powerful techniques to help on the current needs of understanding and organizing huge amounts of textual documents. One way to do this is to build topic taxonomies from these documents. Topic taxonomies can be used to organize the documents, preferably in hierarchies, and to identify groups of related documents and their descriptors. Constructing high quality topic taxonomies, either manually, automatically or semi-automatically, is not a trivial task. This work aims to use text mining techniques to build topic taxonomies for well defined knowledge domains, helping the domain expert to understand and organize document collections. By using well defined knowledge domains, only unsupervised statistical methods are used, with a bag of word representation for textual documents. These representations are independent of the context of the words in the documents as well as in the domain. Thus, if the domain is well defined, a decrease of mistakes of the result interpretation is expected. The proposed methodology for topic taxonomy construction is an instantiation of the text mining process. At each step of the process, some solutions are proposed and adapted to the specific needs of topic taxonomy construction. Among these solutions there are some innovative contributions to the state of the art. Particularly, this work contributes to the state of the art in three different ways: the selection of n-grams attributes in text mining tasks, two models for hierarchical document cluster labeling and a validation model of the hierarchical document cluster labeling. Additional contributions include adaptations and methodologies of attribute selection process choices, attribute representation, taxonomy visualization and obtained taxonomy reduction. Finally, the proposed methodology was also validated by successfully applying it to real problems

Data Quality (DQ) has been a long-standing concern for a number of stakeholders in a variety of domains. It has become a critically important factor for the effectiveness of organisations and individuals. Previous work on DQ methodologies have mainly focused on either the analysis of structured data or the business-process level rather than analysing the data itself. Question and Answer Reports (QAR) are gaining momentum as a way to collect responses that can be used by data analysts, for instance, in business, education or healthcare. Various stakeholders benefit from QAR such as data brokers and data providers, and in order to effectively analyse and identify the common DQ problems in these reports, the various stakeholders' perspectives should be taken into account which adds another complexity for the analysis. This thesis investigates DQ in QAR through an in-depth DQ analysis and provide solutions that can highlight potential sources and causes of problems that result in "low-quality" collected data. The thesis proposes a DQ methodology that is appropriate for the context of QAR. The methodology consists of three modules: question analysis, medium analysis and answer analysis. In addition, a Question Design Support (QuDeS) framework is introduced to operationalise the proposed methodology through the automatic identification of DQ problems. The framework includes three components: question domain-independent profiling, question domain-dependent profiling and answers profiling. The proposed framework has been instantiated to address one example of DQ issues, namely Multi-Focal Question (MFQ). We introduce MFQ as a question with multiple requirements; it asks for multiple answers. QuDeS-MFQ (the implemented instance of QuDeS framework) has implemented two components of QuDeS for MFQ identification, these are question domain-independent profiling and question domain-dependent profiling. The proposed methodology and the framework are designed, implemented and evaluated in the context of the Carbon Disclosure Project (CDP) case study. The experiments show that we can identify MFQs with 90% accuracy. This thesis also demonstrates the challenges including the lack of domain resources for domain knowledge representation, such as domain ontology, the complexity and variability of the structure of QAR, as well as the variability and ambiguity of terminology and language expressions and understanding stakeholders or users need.

En biomedicine, le domaine du « Big Data » (l'infobésité) pose le problème de l'analyse de gros volumes de données hétérogènes (i.e. vidéo, audio, texte, image). Les ontologies biomédicales, modèle conceptuel de la réalité, peuvent jouer un rôle important afin d'automatiser le traitement des données, les requêtes et la mise en correspondance des données hétérogènes. Il existe plusieurs ressources en anglais mais elles sont moins riches pour le français. Le manque d'outils et de services connexes pour les exploiter accentue ces lacunes. Dans un premier temps, les ontologies ont été construites manuellement. Au cours de ces dernières années, quelques méthodes semi-automatiques ont été proposées. Ces techniques semi-automatiques de construction/enrichissement d'ontologies sont principalement induites à partir de textes en utilisant des techniques du traitement du langage naturel (TALN). Les méthodes de TALN permettent de prendre en compte la complexité lexicale et sémantique des données biomédicales : (1) lexicale pour faire référence aux syntagmes biomédicaux complexes à considérer et (2) sémantique pour traiter l'induction du concept et du contexte de la terminologie. Dans cette thèse, afin de relever les défis mentionnés précédemment, nous proposons des méthodologies pour l'enrichissement/la construction d'ontologies biomédicales fondées sur deux principales contributions.La première contribution est liée à l'extraction automatique de termes biomédicaux spécialisés (complexité lexicale) à partir de corpus. De nouvelles mesures d'extraction et de classement de termes composés d'un ou plusieurs mots ont été proposées et évaluées. L'application BioTex implémente les mesures définies.La seconde contribution concerne l'extraction de concepts et le lien sémantique de la terminologie extraite (complexité sémantique). Ce travail vise à induire des concepts pour les nouveaux termes candidats et de déterminer leurs liens sémantiques, c'est-à-dire les positions les plus pertinentes au sein d'une ontologie biomédicale existante. Nous avons ainsi proposé une approche d'extraction de concepts qui intègre de nouveaux termes dans l'ontologie MeSH. Les évaluations, quantitatives et qualitatives, menées par des experts et non experts, sur des données réelles soulignent l'intérêt de ces contributions. / Big Data for biomedicine domain deals with a major issue, the analyze of large volume of heterogeneous data (e.g. video, audio, text, image). Ontology, conceptual models of the reality, can play a crucial role in biomedical to automate data processing, querying, and matching heterogeneous data. Various English resources exist but there are considerably less available in French and there is a strong lack of related tools and services to exploit them. Initially, ontologies were built manually. In recent years, few semi-automatic methodologies have been proposed. The semi-automatic construction/enrichment of ontologies are mostly induced from texts by using natural language processing (NLP) techniques. NLP methods have to take into account lexical and semantic complexity of biomedical data : (1) lexical refers to complex phrases to take into account, (2) semantic refers to sense and context induction of the terminology.In this thesis, we propose methodologies for enrichment/construction of biomedical ontologies based on two main contributions, in order to tackle the previously mentioned challenges. The first contribution is about the automatic extraction of specialized biomedical terms (lexical complexity) from corpora. New ranking measures for single- and multi-word term extraction methods have been proposed and evaluated. In addition, we present BioTex software that implements the proposed measures. The second contribution concerns the concept extraction and semantic linkage of the extracted terminology (semantic complexity). This work seeks to induce semantic concepts of new candidate terms, and to find the semantic links, i.e. relevant location of new candidate terms, in an existing biomedical ontology. We proposed a methodology that extracts new terms in MeSH ontology. The experiments conducted on real data highlight the relevance of the contributions.

Malgré l’importance d'une nomenclature internationale, le domaine de la chimie souffre encore de quelques problèmes linguistiques, liés notamment à ses unités terminologiques simples et complexes, pouvant gêner la communication scientifique. L’arabe ne fait pas exception, d’autant plus que sa graphie agglutinante et, en général, non-voyellée, pose d’énormesproblèmes d’ambiguïté. A cela s’ajoute l’emploi récurrent d’emprunts. La question est de savoir comment représenter les unités terminologiques simples et complexes de cette langue spécialisée. En d’autres termes, formaliser les caractéristiques terminologiques en étudiant les mécanismes de la construction morphosyntaxique des termes de la chimie en arabe. Cette étude devrait aboutir à la mise en place d’un outil de désambigüisation sémantique qui vise à constituer un outil d’extraction des termes de la chimie en arabe et de leurs relations. Une recherche pertinente en arabe passant obligatoirement par un système automatisé du traitement de la langue ; le traitement automatiquement des corpus écrits en arabe ne pouvant se faire sansanalyse linguistique ; cette analyse linguistique, plus précisément, cette étude terminologique, est la base pour la construction des règles d’une grammaire d’identification afin de déterminer les termes de la chimie en arabe. La construction de cette grammaire d’identification nécessite la modélisation des patrons morphosyntaxiques à partir de leur observation en corpus etdébouche sur la définition de règles de grammaire et de contraintes. / Despite the importance of an international nomenclature, the field of chemistry still suffers from some linguistic problems, linked in particular to its simple and complex terminological units, which can hinder scientific communication. Arabic is no exception, especially since its agglutinating spelling and, in general, not vowelized, may lead to enormous ambiguity's problems. This is in addition to the recurring use of borrowings. The problematic is how to represent the simple and complex terminological units of this specialized language. In other words, formalize the terminological characteristics by studying the mechanisms of themorphosyntactic construction of the chemistry' terms in Arabic. This study should lead to the establishment of a semantic-disambiguation tool that aims to create a tool for extracting the terms of Arabic chemistry and their relationships. A relevant search in Arabic cannot be done without an automated system of language processing; this automatic processing of corpuswritten in Arabic cannot be done without a language analysis; this linguistic analysis, more exactly, this terminology study, is the basis to build the rules of an identification grammar in order to identify the chemistry's terms in Arabic. The construction of this identification grammar requires modelling of morphosyntactic patterns from their observation in corpus and leads to the definition of rules of grammar and constraints.

Biagrupamento e coagrupamento são tarefas de mineração de dados que permitem a extração de informação relevante sobre dados e têm sido aplicadas com sucesso em uma ampla variedade de domínios, incluindo aqueles que envolvem dados textuais -- foco de interesse desta pesquisa. Nas tarefas de biagrupamento e coagrupamento, os critérios de similaridade são aplicados simultaneamente às linhas e às colunas das matrizes de dados, agrupando simultaneamente os objetos e os atributos e possibilitando a criação de bigrupos/cogrupos. Contudo suas definições variam segundo suas naturezas e objetivos, sendo que a tarefa de coagrupamento pode ser vista como uma generalização da tarefa de biagrupamento. Estas tarefas, quando aplicadas nos dados textuais, demandam uma representação em um modelo de espaço vetorial que, comumente, leva à geração de espaços caracterizados pela alta dimensionalidade e esparsidade, afetando o desempenho de muitos dos algoritmos. Este trabalho apresenta uma análise do comportamento do algoritmo para biagrupamento Cheng e Church e do algoritmo para coagrupamento de decomposição de valores em blocos não negativos (\\textit{Non-Negative Block Value Decomposition} - NBVD), aplicado ao contexto de dados textuais. Resultados experimentais quantitativos e qualitativos são apresentados a partir das experimentações destes algoritmos em conjuntos de dados sintéticos criados com diferentes níveis de esparsidade e em um conjunto de dados real. Os resultados são avaliados em termos de medidas próprias de biagrupamento, medidas internas de agrupamento a partir das projeções nas linhas dos bigrupos/cogrupos e em termos de geração de informação. As análises dos resultados esclarecem questões referentes às dificuldades encontradas por estes algoritmos nos ambiente de experimentação, assim como se são capazes de fornecer informações diferenciadas e úteis na área de mineração de texto. De forma geral, as análises realizadas mostraram que o algoritmo NBVD é mais adequado para trabalhar com conjuntos de dados em altas dimensões e com alta esparsidade. O algoritmo de Cheng e Church, embora tenha obtidos resultados bons de acordo com os objetivos do algoritmo, no contexto de dados textuais, propiciou resultados com baixa relevância / Biclustering e coclustering are data mining tasks that allow the extraction of relevant information about data and have been applied successfully in a wide variety of domains, including those involving textual data - the focus of interest of this research. In biclustering and coclustering tasks, similarity criteria are applied simultaneously to the rows and columns of the data matrices, simultaneously grouping the objects and attributes and enabling the discovery of biclusters/coclusters. However their definitions vary according to their natures and objectives, being that the task of coclustering can be seen as a generalization of the task of biclustering. These tasks applied in the textual data demand a representation in a model of vector space, which commonly leads to the generation of spaces characterized by high dimensionality and sparsity and influences the performance of many algorithms. This work provides an analysis of the behavior of the algorithm for biclustering Cheng and Church and the algorithm for coclustering non-negative block decomposition (NBVD) applied to the context of textual data. Quantitative and qualitative experimental results are shown, from experiments on synthetic datasets created with different sparsity levels and on a real data set. The results are evaluated in terms of their biclustering oriented measures, internal clustering measures applied to the projections in the lines of the biclusters/coclusters and in terms of generation of information. The analysis of the results clarifies questions related to the difficulties faced by these algorithms in the experimental environment, as well as if they are able to provide differentiated information useful to the field of text mining. In general, the analyses carried out showed that the NBVD algorithm is better suited to work with datasets in high dimensions and with high sparsity. The algorithm of Cheng and Church, although it obtained good results according to its own objectives, provided results with low relevance in the context of textual data

Ce travail a pour objet l’étude de deux méthodes de fouille automatique de textes, l’extraction d’informations et la textométrie, toutes deux mises au service de la veille stratégique des événements économiques. Pour l’extraction d’informations, il s’agit d’identifier et d’étiqueter des unités de connaissances, entités nommées — sociétés, lieux, personnes, qui servent de points d’entrée pour les analyses d’activités ou d’événements économiques — fusions, faillites, partenariats, impliquant ces différents acteurs. La méthode textométrique, en revanche, met en œuvre un ensemble de modèles statistiques permettant l’analyse des distributions de mots dans de vastes corpus, afin faire émerger les caractéristiques significatives des données textuelles. Dans cette recherche, la textométrie, traditionnellement considérée comme étant incompatible avec la fouille par l’extraction, est substituée à cette dernière pour obtenir des informations sur des événements économiques dans le discours. Plusieurs analyses textométriques (spécificités et cooccurrences) sont donc menées sur un corpus de flux de presse numérisé. On étudie ensuite les résultats obtenus grâce à la textométrie en vue de les comparer aux connaissances mises en évidence au moyen d’une procédure d’extraction d’informations. On constate que chacune des approches contribuent différemment au traitement des données textuelles, produisant toutes deux des analyses complémentaires. À l’issue de la comparaison est exposé l’apport des deux méthodes de fouille pour la veille d’événements. / This research demonstrates two methods of text mining for strategic monitoring purposes: information extraction and Textometry. In strategic monitoring, text mining is used to automatically obtain information on the activities of corporations. For this objective, information extraction identifies and labels units of information, named entities (companies, places, people), which then constitute entry points for the analysis of economic activities or events. These include mergers, bankruptcies, partnerships, etc., involving corresponding corporations. A Textometric method, however, uses several statistical models to study the distribution of words in large corpora, with the goal of shedding light on significant characteristics of the textual data. In this research, Textometry, an approach traditionally considered incompatible with information extraction methods, is applied to the same corpus as an information extraction procedure in order to obtain information on economic events. Several textometric analyses (characteristic elements, co-occurrences) are examined on a corpus of online news feeds. The results are then compared to those produced by the information extraction procedure. Both approaches contribute differently to processing textual data, producing complementary analyses of the corpus. Following the comparison, this research presents the advantages for these two text mining methods in strategic monitoring of current events.

<p>Danas, servisi e-Uprave u različitim oblastima koriste question answer sisteme koncepta u poku&scaron;aju da se razume tekst i da pomognu građanima u dobijanju odgovora na svoje upite u bilo koje vreme i veoma brzo. Automatsko mapiranje relevantnih dokumenata se ističe kao važna aplikacija za automatsku strategiju klasifikacije: upit-dokumenta. Ova doktorska disertacija ima za cilj doprinos u identifikaciji nestruktuiranih dokumenata i predstavlja važan korak ka razja&scaron;njavanju uloge eksplicitnih koncepata u pronalaženju podataka uop&scaron;te ajče&scaron; a reprezenta vna &scaron;ema u tekstualnoj kategorizaciji je BoW pristup, kada je u pozadini veliki skup znanja. Ova disertacija uvodi novi pristup ka stvaranju koncepta zasnovanog na tekstualnoj prezantaciji i primeni kategorizacije teksta, kako bi se stvorile definisane klase u slučaju sažetih tekstualnih dokumenata Takođe, ovde je prikazan algoritam zasnovan na klasifikaciji, modelovan za upite koji odgovaraju temi. Otežavaju a okolnost u slučaju ovog koncepta, koji prezentuje termine sa visokom frekvencijom pojavljivanja u upitma, zasniva se na sličnostima u prethodno definisanim klasama dokumenata Rezultati eksperimenta iz oblasti Krivičnog zakonika Republike Srbije, u ovom slučaju i studija, pokazuju da prezentacija teksta zasnovana na konceptu ima zadovoljavaju e rezultate i u slučaju kada ne postoji rečnik za datu oblast.</p> / <p>Nowadays, the concept of Question Answering Systems (QAS) has been used by e-government services in various fields as an attempt to understand the text and help citizens in getting answers to their questions promptly and at any time. Automatic mapping of relevant documents stands out as an important application for automatic classification strategy: query-document. This doctoral thesis aims to contribute to identification of unstructured documents and represents an important step towards clarifying the role of explicit concepts within Information Retrieval in general. The most common scheme in text categorization is BoW approach, especially when, as a basis, we have a large set of knowledge. This thesis introduces a new approach to the creation of text presentation based concept and applying text categorization, with the aim to create a defined class in case of compressed text documents.Also, this paper discusses the classification based algorithm modeled for queries that suit the theme. What makes the situation more complicated is the fact that this concept is based on the similarities in previously defined classes of documents and terms with a high frequency of appearance presented in queries. The results of the experiment in the field of the Criminal Code, and this paper as well, show that the text presentation based concept has satisfactory results even in case where there is no vocabulary for certain field.</p>

Dans une perspective comparative entre la France et les États-Unis, ce travail analyse le processus de publicisation des liens entre l’antibiorésistance et l’alimentation, ainsi que ses implications en termes de contribution au développement de la production appelée sans antibiotique et de l’approche One Health. En partant de la prise de conscience des conséquences de l’usage des antibiotiques dans l’élevage, la recherche s’inscrit dans une réflexion pragmatiste de constitution des problèmes publics et s’appuie sur un corpus hybride composé de documents publiés entre 1980 et 2016 (presse écrite, littérature institutionnelle et entretiens semi-directifs). La méthode développée s’enrichit des outils de textométrie issus de l’analyse de discours et s’intéresse à l’émergence des dénominations et des formules qui nomment le problème, ses causes et ses solutions. La comparaison montre que le processus de publicisation de liens entre l’antibiorésistance et l’alimentation dévoile une trajectoire opposée dans les deux pays. Dans le cas français, ce processus s’inscrit dans un schéma top-down et se caractérise par une publicisation tardive faisant suite aux démarches des instances sanitaires européennes et internationales. L’appropriation du problème par des associations de consommateurs, ainsi que l’investissement des acteurs agroalimentaires dans le développement de la production sans antibiotique, n’émergent que récemment. En revanche, aux États-Unis, ce processus s’inscrit dans un modèle bottom-up suite à la constitution d’un public d’organisations non gouvernementales autour du problème. Leur mobilisation a contribué significativement au développement de programmes d’élevage sans antibiotique ainsi qu’à la mise à l’agenda gouvernemental du problème et le lancement d’un plan national dans une approche One Health. / In a cross-country perspective between France and the United States, this research analyses the process of publicizing the links between antibiotic resistance and food, as well as its contribution to the development of the antibiotic free production and the implementation of the One Health approach. Starting with the awareness of the antibiotic use in livestock consequences, the study relies on the pragmatist approach of the constitution of the public problems. It is based on wide corpora composed by documents published between 1980 and 2016 (written press, institutional literature and semi-directive interviews). The analysis method uses textometric tools derived from discourse analysis and focuses on the emergence of formulas that name the problem, its causes and its solutions. The comparison uncovers an opposite process between the two countries. In France, this process is part of a top-down approach and is characterized by a late publicization following the European and international health authorities’ initiatives. The consumer associations taking over the problem, as well as the agri-food actors’ commitment to the antibiotic free production, is very recent. In the United States, this process reveals a bottom-up model following a non-governmental organizations public constitution taking over the problem. Their mobilization has contributed to the development of the antibiotic free breeding programs, as well as to place the problem on the government agenda that launched a national plan in a One Health approach.

新聞事件可描述為「一個時間區間內、同一主題的相似新聞之集合」，而新聞大多僅是一完整事件的零碎片段，其內容也易受到媒體立場或撰寫角度不同有所差異；除此之外，龐大的新聞量亦使得想要瞭解事件全貌的困難度大增。因此，本研究將利用文字探勘技術群聚相關新聞為事件，以增進新聞所帶來的價值。 分類分群為文字探勘中很常見的步驟，亦是本研究將新聞群聚成事件所運用到的主要方法。最近鄰 (k-nearest neighbor, kNN)搜尋法可視為分類法中最常見的演算法之一，但由於kNN在分類上必須要每篇新聞兩兩比較並排序才得以選出最近鄰，這也產生了kNN在實作上的效能瓶頸。本研究提出了一個「建立距離參考基準點」的方法RTD-based kNN (Relative Text-Distance-based kNN)，透過在向量空間中建立一個基準點，讓所有文件利用與基準點的相對距離建立起遠近的關係，使得在選取前k個最近鄰之前，直接以相對關係篩選出較可能的候選文件，進而選出前k個最近鄰，透過相對距離的概念減少比較次數以改善效率。 本研究於Google News中抽取62個事件(共742篇新聞)，並依其分群結果作為測試與評估依據，以比較RTD-based kNN與kNN新聞事件分群時的績效。實驗結果呈現出RTD-based kNN的基準點以常用字字彙建立較佳，分群後的再合併則有助於改善結果，而在RTD-based kNN與kNN的F-measure並無顯著差距(α=0.05)的情況下，RTD-based kNN的運算時間低於kNN達28.13%。顯示RTD-based kNN能提供新聞事件分群時一個更好的方法。最後，本研究提供一些未來研究之方向。 / News Events can be described as "the aggregation of many similar news that describe the particular incident within a specific timeframe". Most of news article portraits only a part of a passage, and many of the content are bias because of different media standpoint or different viewpoint of reporters; in addition, the massive news source increases complexity of the incident. Therefore, this research paper employs Text Mining Technique to cluster similar news to a events that can value added a news contributed. Classification and Clustering technique is a frequently used in Text Mining, and K-nearest neighbor(kNN) is one of most common algorithms apply in classification. However, kNN requires massive comparison on each individual article, and it becomes the performance bottlenecks of kNN. This research proposed Relative Text-Distance-based kNN(RTD-based kNN), the core concept of this method is establish a Base, a distance reference point, through a Vector Space, all documents can create the distance relationship through the relative distance between itself and base. Through the concept of relative distance, it can decrease the number of comparison and improve the efficiency. This research chooses a sample of 62 events (with total of 742 news articles) from Google News for the test and evaluation. Under the condition of RTD-based kNN and kNN with a no significant difference in F-measure (α=0.05), RTD-based kNN out perform kNN in time decreased by 28.13%. This confirms RTD-based kNN is a better method in clustering news event. At last, this research provides some of the research aspect for the future.