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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
51

Reconhecimento de entidades nomeadas na ?rea da geologia : bacias sedimentares brasileiras

Amaral, Daniela Oliveira Ferreira do 14 September 2017 (has links)
Submitted by PPG Ci?ncia da Computa??o (ppgcc@pucrs.br) on 2018-05-03T18:01:24Z No. of bitstreams: 1 DANIELA_OLIVEIRA_FERREIRA_DO_AMARAL_TES.pdf: 6343384 bytes, checksum: a1d91fe5b12fa5cfdedb20ec1baf5042 (MD5) / Approved for entry into archive by Sheila Dias (sheila.dias@pucrs.br) on 2018-05-14T19:20:24Z (GMT) No. of bitstreams: 1 DANIELA_OLIVEIRA_FERREIRA_DO_AMARAL_TES.pdf: 6343384 bytes, checksum: a1d91fe5b12fa5cfdedb20ec1baf5042 (MD5) / Made available in DSpace on 2018-05-14T19:35:09Z (GMT). No. of bitstreams: 1 DANIELA_OLIVEIRA_FERREIRA_DO_AMARAL_TES.pdf: 6343384 bytes, checksum: a1d91fe5b12fa5cfdedb20ec1baf5042 (MD5) Previous issue date: 2017-09-14 / The treatment of textual information has been increasingly relevant in many do- mains. One of the first tasks for extracting information from texts is the Named Entities Recognition (NER), which consists of identifying references to certain entities and finding out their classification. There are many NER domains, among them the most usual are medicine and biology. One of the challenging domains in the recognition of Named Entities (NE) is the Geology domain, which is an area lacking computational linguistic resources. This thesis proposes a method for the recognition of relevant NE in the field of Geology, specifically to the subarea of Brazilian Sedimentary Basin, in Portuguese texts. Generic and geological features were defined for the generation of a machine learning model. Among the automatic approaches to NE classification, the most prominent is the Conditional Ran- dom Fields (CRF) probabilistic model. CRF has been effectively used for word processing in natural language. To generate our model, we created GeoCorpus, a reference corpus for Geological NER, annotated by specialists. Experimental evaluations were performed to compare the proposed method with other classifiers. The best results were achieved by CRF, which shows 76,78% of Precision and 54,33% of F-Measure. / O tratamento da informa??o textual torna-se cada vez mais relevante para muitos dom?nios. Nesse sentido, uma das primeira tarefas para Extra??o de Informa??es a partir de textos ? o Reconhecimento de Entidades Nomeadas (REN), que consiste na identifica??o de refer?ncias feitas a determinadas entidades e sua classifica??o. REN compreende muitos dom?nios, entre eles os mais usuais s?o medicina e biologia. Um dos dom?nios desafiadores no reconhecimento de EN ? o de Geologia, sendo essa uma ?rea carente de recursos lingu?sticos computacionais. A presente tese prop?e um m?todo para o reconhecimento de EN relevantes no dom?nio da Geologia, sub?rea Bacia Sedimentar Brasileira, em textos da l?ngua portuguesa. Definiram-se features gen?ricas e geol?gicas para a gera??o do modelo de aprendizado. Entre as abordagens autom?ticas para classifica??o de EN, a mais proeminente ? o modelo probabil?stico Conditional Random Fields (CRF). O CRF tem sido utilizado eficazmente no processamento de textos em linguagem natural. A fim de gerar um modelo de aprendizado foi criado o GeoCorpus, um corpus de refer?ncia para REN Geol?gicas, anotado por especialistas. Avalia??es experimentais foram realizadas com o objetivo de comparar o m?todo proposto com outros classificadores. Destacam-se os melhores resultados para o CRF, o qual alcan?ou 76,78% e 54,33% em Precis?o e Medida-F.
52

Estudo comparativo de diferentes classificadores baseados em aprendizagem de m?quina para o processo de Reconhecimento de Entidades Nomeadas

Santos, Jadson da Silva 09 September 2016 (has links)
Submitted by Jadson Francisco de Jesus SILVA (jadson@uefs.br) on 2018-01-24T22:42:26Z No. of bitstreams: 1 JadsonDisst.pdf: 3499973 bytes, checksum: 5deaf9020f758e9c07f86e9e62890129 (MD5) / Made available in DSpace on 2018-01-24T22:42:26Z (GMT). No. of bitstreams: 1 JadsonDisst.pdf: 3499973 bytes, checksum: 5deaf9020f758e9c07f86e9e62890129 (MD5) Previous issue date: 2016-09-09 / The Named Entity Recognition (NER) process is the task of identifying relevant termsintextsandassigningthemalabel.Suchwordscanreferencenamesofpeople, organizations, and places. The variety of techniques that can be used in the named entityrecognitionprocessislarge.Thetechniquescanbeclassifiedintothreedistinct approaches: rule-based, machine learning and hybrid. Concerning to the machine learningapproaches,severalfactorsmayinfluenceitsaccuracy,includingtheselected classifier, the set of features extracted from the terms, the characteristics of the textual bases, and the number of entity labels. In this work, we compared classifiers that use machine learning applied to the NER task. The comparative study includes classifiers based on CRF (Conditional Random Fields), MEMM (MaximumEntropy Markov Model) and HMM (Hidden Markov Model), which are compared in two corpora in Portuguese derived from WikiNer, and HAREM, and two corporas in English derived from CoNLL-03 and WikiNer. The comparison of the classifiers shows that the CRF is superior to the other classifiers, both with Portuguese and English texts. This study also includes the comparison of the individual and joint contribution of features, including contextual features, besides the comparison ofthe NER per named entity labels, between classifiers andcorpora. / O processo de Reconhecimento de Entidades Nomeadas (REN) ? a tarefa de iden- tificar termos relevantes em textos e atribu?-los um r?tulo. Tais palavras podem referenciar nomes de pessoas, organiza??es e locais. A variedade de t?cnicas que podem ser usadas no processo de reconhecimento de entidades nomeadas ? grande. As t?cnicas podem ser classificadas em tr?s abordagens distintas: baseadas em regras, baseadas em aprendizagem de m?quina e h?bridas. No que diz respeito as abordagens de aprendizagem de m?quina, diversos fatores podem influenciar sua exatida?, incluindo o classificador selecionado, o conjunto de features extra?das dos termos, as caracter?sticas das bases textuais e o n?mero de r?tulos de entidades. Neste trabalho, comparamos classificadores que utilizam aprendizagem de m?quina aplicadas a tarefa do REN. O estudo comparativo inclui classificadores baseados no CRF (Condicional Random Fields), MEMM (Maximum Entropy Markov Model) e HMM (Hidden Markov Model), os quais s?o comparados em dois corporas em portugu?s derivados do WikiNer, e HAREM, e dois corporas em ingl?s derivados doCoNLL-03 e WikiNer. A compara??o dos classificadores demonstra que o CRF ? superior aos demais classificadores, tanto com textos em portugu?s, quanto ingl?s. Este estudo tamb?m inclui a compara??o da contribui??o, individual e em conjunto de features, incluindo features de contexto, al?m da compara??o do REN por r?otulos de entidades nomeadas, entre os classificadores e os corpora.
53

Extraction en langue chinoise d'actions spatiotemporalisées réalisées par des personnes ou des organismes / Extraction of spatiotemporally located actions performed by individuals or organizations from Chinese texts

Wang, Zhen 09 June 2016 (has links)
La thèse a deux objectifs : le premier est de développer un analyseur qui permet d'analyser automatiquement des sources textuelles en chinois simplifié afin de segmenter les textes en mots et de les étiqueter par catégories grammaticales, ainsi que de construire les relations syntaxiques entre les mots. Le deuxième est d'extraire des informations autour des entités et des actions qui nous intéressent à partir des textes analysés. Afin d'atteindre ces deux objectifs, nous avons traité principalement les problématiques suivantes : les ambiguïtés de segmentation, la catégorisation ; le traitement des mots inconnus dans les textes chinois ; l'ambiguïté de l'analyse syntaxique ; la reconnaissance et le typage des entités nommées. Le texte d'entrée est traité phrase par phrase. L'analyseur commence par un traitement typographique au sein des phrases afin d'identifier les écritures latines et les chiffres. Ensuite, nous segmentons la phrase en mots à l'aide de dictionnaires. Grâce aux règles linguistiques, nous créons des hypothèses de noms propres, changeons les poids des catégories ou des mots selon leur contextes gauches ou/et droits. Un modèle de langue n-gramme élaboré à partir d'un corpus d'apprentissage permet de sélectionner le meilleur résultat de segmentation et de catégorisation. Une analyse en dépendance est utilisée pour marquer les relations entre les mots. Nous effectuons une première identification d'entités nommées à la fin de l'analyse syntaxique. Ceci permet d'identifier les entités nommées en unité ou en groupe nominal et également de leur attribuer un type. Ces entités nommées sont ensuite utilisées dans l'extraction. Les règles d'extraction permettent de valider ou de changer les types des entités nommées. L'extraction des connaissances est composée des deux étapes : extraire et annoter automatiquement des contenus à partir des textes analysés ; vérifier les contenus extraits et résoudre la cohérence à travers une ontologie. / We have developed an automatic analyser and an extraction module for Chinese langage processing. The analyser performs automatic Chinese word segmentation based on linguistic rules and dictionaries, part-of-speech tagging based on n-gram statistics and dependency grammar parsing. The module allows to extract information around named entities and activities. In order to achieve these goals, we have tackled the following main issues: segmentation and part-of-speech ambiguity; unknown word identification in Chinese text; attachment ambiguity in parsing. Chinese texts are analysed sentence by sentence. Given a sentence, the analyzer begins with typographic processing to identify sequences of Latin characters and numbers. Then, dictionaries are used for preliminary segmentation into words. Linguistic-based rules are used to create proper noun hypotheses and change the weight of some word categories. These rules take into account word context. An n-gram language model is created from a training corpus and selects the best word segmentation and parts-of-speech. Dependency grammar parsing is used to annotate relations between words. A first step of named entity recognition is performed after parsing. Its goal is to identify single-word named entities and noun-phrase-based named entities and to determine their semantic type. These named entities are then used in knowledge extraction. Knowledge extraction rules are used to validate named entities or to change their types. Knowledge extraction consists of two steps: automatic content extraction and tagging from analysed text; extracted contents control and ontology-based co-reference resolution.
54

Reconhecimento de entidades mencionadas em português utilizando aprendizado de máquina / Portuguese named entity recognition using machine learning

Carvalho, Wesley Seidel 24 February 2012 (has links)
O Reconhecimento de Entidades Mencionadas (REM) é uma subtarefa da extração de informações e tem como objetivo localizar e classificar elementos do texto em categorias pré-definidas tais como nome de pessoas, organizações, lugares, datas e outras classes de interesse. Esse conhecimento obtido possibilita a execução de outras tarefas mais avançadas. O REM pode ser considerado um dos primeiros passos para a análise semântica de textos, além de ser uma subtarefa crucial para sistemas de gerenciamento de documentos, mineração de textos, extração da informação, entre outros. Neste trabalho, estudamos alguns métodos de Aprendizado de Máquina aplicados na tarefa de REM que estão relacionados ao atual estado da arte, dentre eles, dois métodos aplicados na tarefa de REM para a língua portuguesa. Apresentamos três diferentes formas de avaliação destes tipos de sistemas presentes na literatura da área. Além disso, desenvolvemos um sistema de REM para língua portuguesa utilizando Aprendizado de Máquina, mais especificamente, o arcabouço de máxima entropia. Os resultados obtidos com o nosso sistema alcançaram resultados equiparáveis aos melhores sistemas de REM para a língua portuguesa desenvolvidos utilizando outras abordagens de aprendizado de máquina. / Named Entity Recognition (NER), a task related to information extraction, aims to classify textual elements according to predefined categories such as names, places, dates etc. This enables the execution of more advanced tasks. NER is a first step towards semantic textual analysis and is also a crucial task for systems of information extraction and other types of systems. In this thesis, I analyze some Machine Learning methods applied to NER tasks, including two methods applied to Portuguese language. I present three ways of evaluating these types of systems found in the literature. I also develop an NER system for the Portuguese language utilizing Machine Learning that entails working with a maximum entropy framework. The results are comparable to the best NER systems for the Portuguese language developed with other Machine Learning alternatives.
55

Automatic Extraction and Assessment of Entities from the Web

Urbansky, David 23 October 2012 (has links) (PDF)
The search for information about entities, such as people or movies, plays an increasingly important role on the Web. This information is still scattered across many Web pages, making it more time consuming for a user to find all relevant information about an entity. This thesis describes techniques to extract entities and information about these entities from the Web, such as facts, opinions, questions and answers, interactive multimedia objects, and events. The findings of this thesis are that it is possible to create a large knowledge base automatically using a manually-crafted ontology. The precision of the extracted information was found to be between 75–90 % (facts and entities respectively) after using assessment algorithms. The algorithms from this thesis can be used to create such a knowledge base, which can be used in various research fields, such as question answering, named entity recognition, and information retrieval.
56

La structuration dans les entités nommées / Structuration in named entities

Dupont, Yoann 23 November 2017 (has links)
La reconnaissance des entités nommées et une discipline cruciale du domaine du TAL. Elle sert à l'extraction de relations entre entités nommées, ce qui permet la construction d'une base de connaissance (Surdeanu and Ji, 2014), le résumé automatique (Nobata et al., 2002), etc... Nous nous intéressons ici aux phénomènes de structurations qui les entourent.Nous distinguons ici deux types d'éléments structurels dans une entité nommée. Les premiers sont des sous-chaînes récurrentes, que nous appelerons les affixes caractéristiques d'une entité nommée. Le second type d'éléments est les tokens ayant un fort pouvoir discriminant, appelés des tokens déclencheurs. Nous détaillerons l'algorithme que nous avons mis en place pour extraire les affixes caractéristiques, que nous comparerons à Morfessor (Creutz and Lagus, 2005b). Nous appliquerons ensuite notre méthode pour extraire les tokens déclencheurs, utilisés pour l'extraction d'entités nommées du Français et d'adresses postales.Une autre forme de structuration pour les entités nommées est de nature syntaxique, qui suit généralement une structure d'imbrications ou arborée. Nous proposons un type de cascade d'étiqueteurs linéaires qui n'avait jusqu'à présent jamais été utilisé pour la reconnaissance d'entités nommées, généralisant les approches précédentes qui ne sont capables de reconnaître des entités de profondeur finie ou ne pouvant modéliser certaines particularités des entités nommées structurées.Tout au long de cette thèse, nous comparons deux méthodes par apprentissage automatique, à savoir les CRF et les réseaux de neurones, dont nous présenterons les avantages et inconvénients de chacune des méthodes. / Named entity recognition is a crucial discipline of NLP. It is used to extract relations between named entities, which allows the construction of knowledge bases (Surdeanu and Ji, 2014), automatic summary (Nobata et al., 2002) and so on. Our interest in this thesis revolves around structuration phenomena that surround them.We distinguish here two kinds of structural elements in named entities. The first one are recurrent substrings, that we will call the caracteristic affixes of a named entity. The second type of element is tokens with a good discriminative power, which we call trigger tokens of named entities. We will explain here the algorithm we provided to extract such affixes, which we will compare to Morfessor (Creutz and Lagus, 2005b). We will then apply the same algorithm to extract trigger tokens, which we will use for French named entity recognition and postal address extraction.Another form of structuration for named entities is of a syntactic nature. It follows an overlapping or tree structure. We propose a novel kind of linear tagger cascade which have not been used before for structured named entity recognition, generalising other previous methods that are only able to recognise named entities of a fixed depth or being unable to model certain characteristics of the structure. Ours, however, can do both.Throughout this thesis, we compare two machine learning methods, CRFs and neural networks, for which we will compare respective advantages and drawbacks.
57

Natural language processing in cross-media analysis

Woldemariam, Yonas Demeke January 2018 (has links)
A cross-media analysis framework is an integrated multi-modal platform where a media resource containing different types of data such as text, images, audio and video is analyzed with metadata extractors, working jointly to contextualize the media resource. It generally provides cross-media analysis and automatic annotation, metadata publication and storage, searches and recommendation services. For on-line content providers, such services allow them to semantically enhance a media resource with the extracted metadata representing the hidden meanings and make it more efficiently searchable. Within the architecture of such frameworks, Natural Language Processing (NLP) infrastructures cover a substantial part. The NLP infrastructures include text analysis components such as a parser, named entity extraction and linking, sentiment analysis and automatic speech recognition. Since NLP tools and techniques are originally designed to operate in isolation, integrating them in cross-media frameworks and analyzing textual data extracted from multimedia sources is very challenging. Especially, the text extracted from audio-visual content lack linguistic features that potentially provide important clues for text analysis components. Thus, there is a need to develop various techniques to meet the requirements and design principles of the frameworks. In our thesis, we explore developing various methods and models satisfying text and speech analysis requirements posed by cross-media analysis frameworks. The developed methods allow the frameworks to extract linguistic knowledge of various types and predict various information such as sentiment and competence. We also attempt to enhance the multilingualism of the frameworks by designing an analysis pipeline that includes speech recognition, transliteration and named entity recognition for Amharic, that also enables the accessibility of Amharic contents on the web more efficiently. The method can potentially be extended to support other under-resourced languages.
58

Automatically Detecting the Resonance of Terrorist Movement Frames on the Web

Etudo, Ugochukwu O 01 January 2017 (has links)
The ever-increasing use of the internet by terrorist groups as a platform for the dissemination of radical, violent ideologies is well documented. The internet has, in this way, become a breeding ground for potential lone-wolf terrorists; that is, individuals who commit acts of terror inspired by the ideological rhetoric emitted by terrorist organizations. These individuals are characterized by their lack of formal affiliation with terror organizations, making them difficult to intercept with traditional intelligence techniques. The radicalization of individuals on the internet poses a considerable threat to law enforcement and national security officials. This new medium of radicalization, however, also presents new opportunities for the interdiction of lone wolf terrorism. This dissertation is an account of the development and evaluation of an information technology (IT) framework for detecting potentially radicalized individuals on social media sites and Web fora. Unifying Collective Action Framing Theory (CAFT) and a radicalization model of lone wolf terrorism, this dissertation analyzes a corpus of propaganda documents produced by several, radically different, terror organizations. This analysis provides the building blocks to define a knowledge model of terrorist ideological framing that is implemented as a Semantic Web Ontology. Using several techniques for ontology guided information extraction, the resultant ontology can be accurately processed from textual data sources. This dissertation subsequently defines several techniques that leverage the populated ontological representation for automatically identifying individuals who are potentially radicalized to one or more terrorist ideologies based on their postings on social media and other Web fora. The dissertation also discusses how the ontology can be queried using intuitive structured query languages to infer triggering events in the news. The prototype system is evaluated in the context of classification and is shown to provide state of the art results. The main outputs of this research are (1) an ontological model of terrorist ideologies (2) an information extraction framework capable of identifying and extracting terrorist ideologies from text, (3) a classification methodology for classifying Web content as resonating the ideology of one or more terrorist groups and (4) a methodology for rapidly identifying news content of relevance to one or more terrorist groups.
59

Reconhecimento de entidades mencionadas em português utilizando aprendizado de máquina / Portuguese named entity recognition using machine learning

Wesley Seidel Carvalho 24 February 2012 (has links)
O Reconhecimento de Entidades Mencionadas (REM) é uma subtarefa da extração de informações e tem como objetivo localizar e classificar elementos do texto em categorias pré-definidas tais como nome de pessoas, organizações, lugares, datas e outras classes de interesse. Esse conhecimento obtido possibilita a execução de outras tarefas mais avançadas. O REM pode ser considerado um dos primeiros passos para a análise semântica de textos, além de ser uma subtarefa crucial para sistemas de gerenciamento de documentos, mineração de textos, extração da informação, entre outros. Neste trabalho, estudamos alguns métodos de Aprendizado de Máquina aplicados na tarefa de REM que estão relacionados ao atual estado da arte, dentre eles, dois métodos aplicados na tarefa de REM para a língua portuguesa. Apresentamos três diferentes formas de avaliação destes tipos de sistemas presentes na literatura da área. Além disso, desenvolvemos um sistema de REM para língua portuguesa utilizando Aprendizado de Máquina, mais especificamente, o arcabouço de máxima entropia. Os resultados obtidos com o nosso sistema alcançaram resultados equiparáveis aos melhores sistemas de REM para a língua portuguesa desenvolvidos utilizando outras abordagens de aprendizado de máquina. / Named Entity Recognition (NER), a task related to information extraction, aims to classify textual elements according to predefined categories such as names, places, dates etc. This enables the execution of more advanced tasks. NER is a first step towards semantic textual analysis and is also a crucial task for systems of information extraction and other types of systems. In this thesis, I analyze some Machine Learning methods applied to NER tasks, including two methods applied to Portuguese language. I present three ways of evaluating these types of systems found in the literature. I also develop an NER system for the Portuguese language utilizing Machine Learning that entails working with a maximum entropy framework. The results are comparable to the best NER systems for the Portuguese language developed with other Machine Learning alternatives.
60

Named Entity Recognition för Klassificering av Rubriker i Fakturor / Classification of Invoice Headers using Named Entity Recognition

Karlsson, Ludvig, Gyllström, Benjamin January 2021 (has links)
Fakturor är en viktig källa av information för företag. Två exempel på viktiga fält i en faktura kan vara, hur mycket pengar som ska betalas och faktura id. På grund av olika format och innehåll i fakturor som skiljer sig åt är extraktionen av information från dessa fakturor ofta en manuell process som kräver mycket tid. För att kunna spara viktig information från semi-strukturerade dokument som fakturor så måste vissa företag lägga ner mycket manuellt arbete. Detta arbete inkluderar att behöva förstå fakturan och därefter veta vilket innehåll som är av intresse för företaget. Detta arbete kan ta mycket tid och därför hade en automatisering av denna process varit av stort intresse. I denna forskningen används named entity recognition för att lösa problemet. De frågor som forskningen besvarar är: Hur effektiv named entity recognition är för klassificering av rubriker i fakturor, samt hur mycket effektiviteten kan öka vid komplettering av ytterligare komponenter. Named entity recognition används för att kategorisera entiteter som i detta fallet är rubriker för fält i fakturor. Modellen som skapas ska avgöra om rubriker i fakturan kan kategoriseras under någon av kategorierna: Invoice number, invoice date, due date, customer number, total amount, vat code, vat amount eller currency. Forskningen försöker endast göra en proof of concept för att se om denna algoritm kan användas för att minska tiden av manuellt arbete. Produktionsmodellen som skapas evalueras med måttet f1-score. Den får med denna metod resultatet 79 av 100. Detta resultatet antyder på att named entity recognition kan användas i ett verkligt scenario för att identifiera rubriker av intresse i en faktura. Men för att få så bra resultat som möjligt så bör modellen kombineras med en lösning som identifierar fält med hjälp av dess data. / Invoices are an important source of information for businesses. Two examples of important fields in an invoice could be the amount of money to be paid and the invoice Id. Due to the different formats and content of invoices, the extraction of information from these is often a manual and time consuming process. In order to save important information from semi-structured documents such as invoices, some companies have to put in a lot of manual work. This work includes understanding the invoice and then knowing what content is of interest to the company. This work can take a lot of time and therefore an automation of this process would be of great interest. In this research named entity recognition is used to solve the mentioned problem. The topics for this research are: How effective named entity recognition is for classification of headers in invoices, as well as how much the efficiency can be improved by complementing with further components. Named entity recognition is used to categorize entities. In this case the entities are the headings of the invoice. The model that is created must determine whether headings in the invoice can be categorized under one of the following categories: Invoice number, invoice date, due date, customer number, total amount, vat code, vat amount or currency. This research tries to make a proof of concept to discover if this algorithm can be used to reduce the time spent on manual work. The production model that is created is evaluated with the f1-score measurement. With this method, it gets a result of 79 out of 100. This result indicates that named entity recognition can be used by companies in real-world scenarios to identify headings in invoices. But to get the best results possible, the model should also be combined with a solution that identifies fields using its corresponding data.

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