Dissertations / Theses on the topic 'Information Extraction'
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Labský, Martin. "Information Extraction from Websites using Extraction Ontologies." Doctoral thesis, Vysoká škola ekonomická v Praze, 2002. http://www.nusl.cz/ntk/nusl-77102.
Full textArpteg, Anders. "Intelligent semi-structured information extraction : a user-driven approach to information extraction /." Linköping : Dept. of Computer and Information Science, Univ, 2005. http://www.bibl.liu.se/liupubl/disp/disp2005/tek946s.pdf.
Full textSwampillai, Kumutha. "Information extraction across sentences." Thesis, University of Sheffield, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.575468.
Full textTablan, Mihai Valentin. "Toward portable information extraction." Thesis, University of Sheffield, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.522379.
Full textLeen, Gayle. "Context assisted information extraction." Thesis, University of the West of Scotland, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.446043.
Full textSottovia, Paolo. "Information Extraction from data." Doctoral thesis, Università degli studi di Trento, 2019. http://hdl.handle.net/11572/242992.
Full textSottovia, Paolo. "Information Extraction from data." Doctoral thesis, Università degli studi di Trento, 2019. http://hdl.handle.net/11572/242992.
Full textArpteg, Anders. "Adaptive Semi-structured Information Extraction." Licentiate thesis, Linköping University, Linköping University, KPLAB - Knowledge Processing Lab, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5688.
Full textThe number of domains and tasks where information extraction tools can be used needs to be increased. One way to reach this goal is to construct user-driven information extraction systems where novice users are able to adapt them to new domains and tasks. To accomplish this goal, the systems need to become more intelligent and able to learn to extract information without need of expert skills or time-consuming work from the user.
The type of information extraction system that is in focus for this thesis is semistructural information extraction. The term semi-structural refers to documents that not only contain natural language text but also additional structural information. The typical application is information extraction from World Wide Web hypertext documents. By making effective use of not only the link structure but also the structural information within each such document, user-driven extraction systems with high performance can be built.
The extraction process contains several steps where different types of techniques are used. Examples of such types of techniques are those that take advantage of structural, pure syntactic, linguistic, and semantic information. The first step that is in focus for this thesis is the navigation step that takes advantage of the structural information. It is only one part of a complete extraction system, but it is an important part. The use of reinforcement learning algorithms for the navigation step can make the adaptation of the system to new tasks and domains more user-driven. The advantage of using reinforcement learning techniques is that the extraction agent can efficiently learn from its own experience without need for intensive user interactions.
An agent-oriented system was designed to evaluate the approach suggested in this thesis. Initial experiments showed that the training of the navigation step and the approach of the system was promising. However, additional components need to be included in the system before it becomes a fully-fledged user-driven system.
Report code: LiU-Tek-Lic-2002:73.
Schierle, Martin. "Language Engineering for Information Extraction." Doctoral thesis, Universitätsbibliothek Leipzig, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-81757.
Full textLam, Man I. "Business information extraction from web." Thesis, University of Macau, 2008. http://umaclib3.umac.mo/record=b1937939.
Full textJessop, David M. "Information extraction from chemical patents." Thesis, University of Cambridge, 2011. https://www.repository.cam.ac.uk/handle/1810/238302.
Full textNguyen, Thien Huu. "Deep Learning for Information Extraction." Thesis, New York University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10260911.
Full textThe explosion of data has made it crucial to analyze the data and distill important information effectively and efficiently. A significant part of such data is presented in unstructured and free-text documents. This has prompted the development of the techniques for information extraction that allow computers to automatically extract structured information from the natural free-text data. Information extraction is a branch of natural language processing in artificial intelligence that has a wide range of applications, including question answering, knowledge base population, information retrieval etc. The traditional approach for information extraction has mainly involved hand-designing large feature sets (feature engineering) for different information extraction problems, i.e, entity mention detection, relation extraction, coreference resolution, event extraction, and entity linking. This approach is limited by the laborious and expensive effort required for feature engineering for different domains, and suffers from the unseen word/feature problem of natural languages.
This dissertation explores a different approach for information extraction that uses deep learning to automate the representation learning process and generate more effective features. Deep learning is a subfield of machine learning that uses multiple layers of connections to reveal the underlying representations of data. I develop the fundamental deep learning models for information extraction problems and demonstrate their benefits through systematic experiments.
First, I examine word embeddings, a general word representation that is produced by training a deep learning model on a large unlabelled dataset. I introduce methods to use word embeddings to obtain new features that generalize well across domains for relation extraction. This is done for both the feature-based method and the kernel-based method of relation extraction.
Second, I investigate deep learning models for different problems, including entity mention detection, relation extraction and event detection. I develop new mechanisms and network architectures that allow deep learning to model the structures of information extraction problems more effectively. Some extensive experiments are conducted on the domain adaptation and transfer learning settings to highlight the generalization advantage of the deep learning models for information extraction.
Finally, I investigate the joint frameworks to simultaneously solve several information extraction problems and benefit from the inter-dependencies among these problems. I design a novel memory augmented network for deep learning to properly exploit such inter-dependencies. I demonstrate the effectiveness of this network on two important problems of information extraction, i.e, event extraction and entity linking.
Lee, Ji Young Ph D. Massachusetts Institute of Technology. "Information extraction with neural networks." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111905.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (pages 85-97).
Electronic health records (EHRs) have been widely adopted, and are a gold mine for clinical research. However, EHRs, especially their text components, remain largely unexplored due to the fact that they must be de-identified prior to any medical investigation. Existing systems for de-identification rely on manual rules or features, which are time-consuming to develop and fine-tune for new datasets. In this thesis, we propose the first de-identification system based on artificial neural networks (ANNs), which achieves state-of-the-art results without any human-engineered features. The ANN architecture is extended to incorporate features, further improving the de-identification performance. Under practical considerations, we explore transfer learning to take advantage of large annotated dataset to improve the performance on datasets with limited number of annotations. The ANN-based system is publicly released as an easy-to-use software package for general purpose named-entity recognition as well as de-identification. Finally, we present an ANN architecture for relation extraction, which ranked first in the SemEval-2017 task 10 (ScienceIE) for relation extraction in scientific articles (subtask C).
by Ji Young Lee.
Ph. D.
Harik, Ralph 1979. "Structural and semantic information extraction." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/87407.
Full textValenzuela, Escárcega Marco Antonio. "Interpretable Models for Information Extraction." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/613348.
Full textPerera, Pathirage Dinindu Sujan Udayanga. "Knowledge-driven Implicit Information Extraction." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1472474558.
Full textBatista-Navarro, Riza Theresa Bautista. "Information extraction from pharmaceutical literature." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/information-extraction-from-pharmaceutical-literature(3f8322b6-8b8d-44eb-a8cd-899026b267b9).html.
Full textKushmerick, Nicholas. "Wrapper induction for information extraction /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/6867.
Full textWang, Yefeng. "Information extraction from clinical notes." Thesis, The University of Sydney, 2010. https://hdl.handle.net/2123/28844.
Full textSpanò, Alvise <1977>. "Information extraction by type analysis." Doctoral thesis, Università Ca' Foscari Venezia, 2013. http://hdl.handle.net/10579/3047.
Full textLa tesi propone un utilizzo alternativo delle tecniche di type reconstruction come strumento per l'estrazione della conoscenza da programmi scritti in linguaggi debolmente tipati. L'approfondimento si dirama in due fronti distinti ma correlati. Nella prima parte si presenta un sistema che sfrutta una tecnica di typing per estrarre informazioni da programmi sorgente COBOL: ricostruire tipi informativi è un buon modo per generare automaticamente della documentazione preliminare sul software legacy ed è anche un buon punto di partenza su cui applicare ulteriori approcci di Program Understanding. Nella seconda parte si applicano principi simili ad un contesto apparentemente distante: verificare la comunicazione tra componenti di applicazioni Android tramite la ricostruzione dei tipi dei dati contenuti negli Intent - i mattoni sui quali si basa lo scambio di messaggi in Android. Infine, sia per COBOL che per Android presentiamo una implementazione distinta del sistema di analisi statica proposto.
Hoang, Thi Bich Ngoc. "Information diffusion, information and knowledge extraction from social networks." Thesis, Toulouse 2, 2018. http://www.theses.fr/2018TOU20078.
Full textThe popularity of online social networks has rapidly increased over the last decade. According to Statista, approximated 2 billion users used social networks in January 2018 and this number is still expected to grow in the next years. While serving its primary purpose of connecting people, social networks also play a major role in successfully connecting marketers with customers, famous people with their supporters, need-help people with willing-help people. The success of online social networks mainly relies on the information the messages carry as well as the spread speed in social networks. Our research aims at modeling the message diffusion, extracting and representing information and knowledge from messages on social networks. Our first contribution is a model to predict the diffusion of information on social networks. More precisely, we predict whether a tweet is going to be diffused or not and the level of the diffusion. Our model is based on three types of features: user-based, time-based and content-based features. Being evaluated on various collections corresponding to dozen millions of tweets, our model significantly improves the effectiveness (F-measure) compared to the state-of-the-art, both when predicting if a tweet is going to be retweeted or not, and when predicting the level of retweet. The second contribution of this thesis is to provide an approach to extract information from microblogs. While several pieces of important information are included in a message about an event such as location, time, related entities, we focus on location which is vital for several applications, especially geo-spatial applications and applications linked to events. We proposed different combinations of various existing methods to extract locations in tweets targeting either recall-oriented or precision-oriented applications. We also defined a model to predict whether a tweet contains a location or not. We showed that the precision of location extraction tools on the tweets we predict to contain a location is significantly improved as compared when extracted from all the tweets.Our last contribution presents a knowledge base that better represents information from a set of tweets on events. We combined a tweet collection with other Internet resources to build a domain ontology. The knowledge base aims at bringing users a complete picture of events referenced in the tweet collection (we considered the CLEF 2016 festival tweet collection)
Toledo, Testa Juan Ignacio. "Information extraction from heterogeneous handwritten documents." Doctoral thesis, Universitat Autònoma de Barcelona, 2019. http://hdl.handle.net/10803/667388.
Full textEl objetivo de esta tesis es la extracción de Información de documentos total o parcialmente manuscritos, con una cierta estructura. Básicamente trabajamos con dos escenarios de aplicación diferentes. El primer escenario son los documentos modernos altamente estructurados, como los formularios. En estos documentos, la información semántica está pre-definida en campos con una posición concreta en el documento i la extracción de información es equivalente a una transcripción. El segundo escenario son los documentos semi-estructurados totalmente manuscritos, donde, además de transcribir, es necesario asociar un valor semántico, de entre un conjunto conocido de valores posibles, a las palabras manuscritas. En ambos casos, la calidad de la transcripción tiene un gran peso en la precisión del sistema. Por ese motivo proponemos modelos basados en redes neuronales para transcribir el texto manuscrito. Para poder afrontar el reto de los documentos semi-estructurados, hemos generado un benchmark, compuesto de dataset, una serie de tareas y una métrica que fue presentado a la comunidad científica a modo de competición internacional. También proponemos diferentes modelos basados en Redes Neuronales Convolucionales y Recurrentes, capaces de transcribir y asignar diferentes etiquetas semánticas a cada palabra manuscrita, es decir, capaces de extraer información.
The goal of this thesis is information Extraction from totally or partially handwritten documents. Basically we are dealing with two different application scenarios. The first scenario are modern highly structured documents like forms. In this kind of documents, the semantic information is encoded in different fields with a pre-defined location in the document, therefore, information extraction becomes equivalent to transcription. The second application scenario are loosely structured totally handwritten documents, besides transcribing them, we need to assign a semantic label, from a set of known values to the handwritten words. In both scenarios, transcription is an important part of the information extraction. For that reason in this thesis we present two methods based on Neural Networks, to transcribe handwritten text.In order to tackle the challenge of loosely structured documents, we have produced a benchmark, consisting of a dataset, a defined set of tasks and a metric, that was presented to the community as an international competition. Also, we propose different models based on Convolutional and Recurrent neural networks that are able to transcribe and assign different semantic labels to each handwritten words, that is, able to perform Information Extraction.
Walessa, Marc. "Bayesian information extraction from SAR images." [S.l. : s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=964273659.
Full textPopescu, Ana-Maria. "Information extraction from unstructured web text /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/6935.
Full textWilliams, Dean Ashley. "Combining data integration and information extraction." Thesis, Birkbeck (University of London), 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.499152.
Full textDuarte, Lucio Mauro. "Behaviour Model Extraction Using Context Information." Thesis, Imperial College London, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.498466.
Full textSukhahuta, Rattasit. "Information extraction system for Thai documents." Thesis, University of East Anglia, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368173.
Full textCiravegna, Fabio. "User-defined information extraction from texts." Thesis, University of East Anglia, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.273293.
Full textBabych, Bogdan. "Information extraction technology in machine translation." Thesis, University of Leeds, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.416402.
Full textCollier, Robin. "Automatic template creation for information extraction." Thesis, University of Sheffield, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286986.
Full textO'Malley, C. J. "Information extraction for enhanced bioprocess development." Thesis, University College London (University of London), 2008. http://discovery.ucl.ac.uk/14247/.
Full textForsling, Robin. "Decentralized Estimation Using Conservative Information Extraction." Licentiate thesis, Linköpings universitet, Reglerteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-171998.
Full textFerreira, Liliana da Silva. "Medical information extraction in European Portuguese." Doctoral thesis, Universidade de Aveiro, 2011. http://hdl.handle.net/10773/7678.
Full textThe electronic storage of medical patient data is becoming a daily experience in most of the practices and hospitals worldwide. However, much of the data available is in free-form text, a convenient way of expressing concepts and events, but especially challenging if one wants to perform automatic searches, summarization or statistical analysis. Information Extraction can relieve some of these problems by offering a semantically informed interpretation and abstraction of the texts. MedInX, the Medical Information eXtraction system presented in this document, is the first information extraction system developed to process textual clinical discharge records written in Portuguese. The main goal of the system is to improve access to the information locked up in unstructured text, and, consequently, the efficiency of the health care process, by allowing faster and reliable access to quality information on health, for both patient and health professionals. MedInX components are based on Natural Language Processing principles, and provide several mechanisms to read, process and utilize external resources, such as terminologies and ontologies, in the process of automatic mapping of free text reports onto a structured representation. However, the flexible and scalable architecture of the system, also allowed its application to the task of Named Entity Recognition on a shared evaluation contest focused on Portuguese general domain free-form texts. The evaluation of the system on a set of authentic hospital discharge letters indicates that the system performs with 95% F-measure, on the task of entity recognition, and 95% precision on the task of relation extraction. Example applications, demonstrating the use of MedInX capabilities in real applications in the hospital setting, are also presented in this document. These applications were designed to answer common clinical problems related with the automatic coding of diagnoses and other health-related conditions described in the documents, according to the international classification systems ICD-9-CM and ICF. The automatic review of the content and completeness of the documents is an example of another developed application, denominated MedInX Clinical Audit system.
O armazenamento electrónico dos dados médicos do paciente é uma prática cada vez mais comum nos hospitais e clínicas médicas de todo o mundo. No entanto, a maior parte destes dados são disponibilizados sob a forma de texto livre, uma forma conveniente de expressar conceitos e termos mas particularmente desafiante quando se pretende realizar procuras, sumarização ou análise estatística de uma forma automática. As tecnologias de extracção automática de informação podem ajudar a solucionar alguns destes problemas através da interpretação semântica e da abstracção do conteúdo dos textos. O sistema de Extracção de Informação Médica apresentado neste documento, o MedInX, é o primeiro sistema desenvolvido para o processamento de cartas de alta hospitalar escritas em Português. O principal objectivo do sistema é a melhoria do acesso à informação trancada nos textos e, consequentemente, a melhoria da eficiência dos cuidados de saúde, através do acesso rápido e confiável à informação, quer relativa ao doente, quer aos profissionais de saúde. O MedInX utiliza diversas componentes, baseadas em princípios de processamento de linguagem natural, para a análise dos textos clínicos, e contém vários mecanismos para ler, processar e utilizar recursos externos, como terminologias e ontologias. Este recursos são utilizados, em particular, no mapeamento automático do texto livre para uma representação estruturada. No entanto, a arquitectura flexível e escalável do sistema permitiu, também, a sua aplicação na tarefa de Reconhecimento de Entidades Nomeadas numa avaliação conjunta relativa ao processamento de textos de domínio geral, escritos em Português. A avaliação do sistema num conjunto de cartas de alta hospitalar reais, indica que o sistema realiza a tarefa de extracção de informação com uma medida F de 95% e a tarefa de extracção de relações com uma precisão de 95%. A utilidade do sistema em aplicações reais é demonstrada através do desenvolvimento de um conjunto de projectos exemplificativos, que pretendem responder a problemas concretos e comuns em ambiente hospitalar. Estes problemas estão relacionados com a codificação automática de diagnósticos e de outras condições relacionadas com o estado de saúde do doente, seguindo as classificações internacionais, ICD-9-CM e ICF. A revisão automática do conteúdo dos documentos é outro exemplo das possíveis aplicações práticas do sistema. Esta última aplicação é representada pelo o sistema de auditoria do MedInX.
Roberts, Angus. "Clinical information extraction : lowering the barrier." Thesis, University of Sheffield, 2012. http://etheses.whiterose.ac.uk/3254/.
Full textWimalasuriya, Daya Chinthana. "Use of ontologies in information extraction." Thesis, University of Oregon, 2011. http://hdl.handle.net/1794/11216.
Full textInformation extraction (IE) aims to recognize and retrieve certain types of information from natural language text. For instance, an information extraction system may extract key geopolitical indicators about countries from a set of web pages while ignoring other types of information. IE has existed as a research field for a few decades, and ontology-based information extraction (OBIE) has recently emerged as one of its subfields. Here, the general idea is to use ontologies--which provide formal and explicit specifications of shared conceptualizations--to guide the information extraction process. This dissertation presents two novel directions for ontology-based information extraction in which ontologies are used to improve the information extraction process. First, I describe how a component-based approach for information extraction can be designed through the use of ontologies in information extraction. A key idea in this approach is identifying components of information extraction systems which make extractions with respect to specific ontological concepts. These components are termed "information extractors". The component-based approach explores how information extractors as well as other types of components can be used in developing information extraction systems. This approach has the potential to make a significant contribution towards the widespread usage and commercialization of information extraction. Second, I describe how an ontology-based information extraction system can make use of multiple ontologies. Almost all previous systems use a single ontology, although multiple ontologies are available for most domains. Using multiple ontologies in information extraction has the potential to extract more information from text and thus leads to an improvement in performance measures. The concept of information extractor, conceived in the component-based approach for information extraction, is used in designing the principles for accommodating multiple ontologies in an ontology-based information extraction system.
Committee in charge: Dr. Dejing Dou, Chair; Dr. Arthur Farley, Member; Dr. Michal Young, Member; Dr. Monte Westerfield, Outside Member
Wang, Wei. "Unsupervised Information Extraction From Text - Extraction and Clustering of Relations between Entities." Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00998390.
Full textCatalà, Roig Neus. "Acquiring information extraction patterns from unannotated corpora." Doctoral thesis, Universitat Politècnica de Catalunya, 2003. http://hdl.handle.net/10803/6671.
Full textThe main issue when building IE systems is how to obtain the knowledge needed to identify relevant information in a document. Today, IE systems are commonly based on extraction rules or IE patterns to represent the kind of information to be extracted. Most approaches to IE pattern acquisition require expert human intervention in many steps of the acquisition process. This dissertation presents a novel method for acquiring IE patterns, Essence, that significantly reduces the need for human intervention. The method is based on ELA, a specifically designed learning algorithm for acquiring IE patterns from unannotated corpora.
The distinctive features of Essence and ELA are that 1) they permit the automatic acquisition of IE patterns from unrestricted and untagged text representative of the domain, due to 2) their ability to identify regularities around semantically relevant concept-words for the IE task by 3) using non-domain-specific lexical knowledge tools such as WordNet and 4) restricting the human intervention to defining the task, and validating and typifying the set of IE patterns obtained.
Since Essence does not require a corpus annotated with the type of information to be extracted and it does makes use of a general purpose ontology and widely applied syntactic tools, it reduces the expert effort required to build an IE system and therefore also reduces the effort of porting the method to any domain.
In order to Essence be validated we conducted a set of experiments to test the performance of the method. We used Essence to generate IE patterns for a MUC-like task. Nevertheless, the evaluation procedure for MUC competitions does not provide a sound evaluation of IE systems, especially of learning systems. For this reason, we conducted an exhaustive set of experiments to further test the abilities of Essence.
The results of these experiments indicate that the proposed method is able to learn effective IE patterns.
Carbonell, Nuñez Manuel. "Neural Information Extraction from Semi-structured Documents." Doctoral thesis, Universitat Autònoma de Barcelona, 2020. http://hdl.handle.net/10803/671583.
Full textSectores como la información y tecnología de seguros, finanzas y legal, procesan un continuo de facturas, justificantes, reclamaciones o similar diariamente. El éxito en la automatización de estas transacciones se basa en la habilidad de digitalizar correctamente el contenido textual asi como incorporar la comprensión semántica. Este proceso, conococido como Extracción de Información (EI) consiste en varios pasos que son, el reconocimiento del texto, la identificación de entidades nombradas y en ocasiones en reconocer relaciones entre estas entidades. En nuestro trabajo exploramos modelos neurales multi-tarea a nivel de imagen y de grafo para solucionar los pasos de este proceso de forma unificada. En el camino, estudiamos los beneficios e inconvenientes de estos enfoques en comparación con métodos que resuelven las tareas secuencialmente por separado.
Sectors as fintech, legaltech or insurance process an inflow of million of forms, invoices, id documents, claims or similar every day. The success in the automation of these transactions depends on the ability to correctly digitize the textual content as well as to incorporate semantic understanding. This procedure, known as information extraction (IE) comprises the steps of localizing and recognizing text, identifying named entities contained in it and optionally finding relationships among its elements. In this work we explore multi-task neural models at image and graph level to solve all steps in a unified way. While doing so we find benefits and limitations of these end-to-end approaches in comparison with sequential separate methods.
Marcińczuk, Michał. "Pattern Acquisition Methods for Information Extraction Systems." Thesis, Blekinge Tekniska Högskola, Avdelningen för programvarusystem, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4291.
Full text(+48)669808616
Bengtsson, Fredrik. "Algorithms for aggregate information extraction from sequences." Doctoral thesis, Luleå : Department of computer science and electrical engineering, Luleå university of technology, 2007. http://epubl.ltu.se/1402-1544/2007/25/.
Full textJanevski, Angel. "UniversityIE: Information Extraction From University Web Pages." UKnowledge, 2000. http://uknowledge.uky.edu/gradschool_theses/217.
Full textCrowe, J. D. M. "Constraint based event recognition for information extraction." Thesis, University of Edinburgh, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648968.
Full textSinha, Srija. "Extraction domains and information partition in Hindi." Thesis, University of York, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.274519.
Full textLoper, Edward (Edward Daniel) 1977. "Applying semantic relation extraction to information retrieval." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86521.
Full textVlachos, Andreas. "Semi-supervised learning for biomedical information extraction." Thesis, University of Cambridge, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.608805.
Full textFRAZER, SCOTT RAYMOND. "INFORMATION EXTRACTION IN CHROMATOGRAPHY USING CORRELATION TECHNIQUES." Diss., The University of Arizona, 1985. http://hdl.handle.net/10150/187978.
Full textTeufel, Simone. "Argumentative zoning : information extraction from scientific text." Thesis, University of Edinburgh, 1999. http://hdl.handle.net/1842/11456.
Full textTabassum, Binte Jafar Jeniya. "Information Extraction From User Generated Noisy Texts." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1606315356821532.
Full textAu, Kwok Chung. "Information extraction for on-line job advertisements." HKBU Institutional Repository, 2004. http://repository.hkbu.edu.hk/etd_ra/525.
Full textWang, Jiying. "Information extraction and integration for Web databases /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?COMP%202004%20WANGJ.
Full textIncludes bibliographical references (leaves 112-118). Also available in electronic version. Access restricted to campus users.