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Dissertations / Theses on the topic 'Drug named entity recognition'

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Published: 5 June 2025

Last updated: 1 August 2025

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1

Benajiba, Yassine. "Arabic named entity recognition." Doctoral thesis, Universitat Politècnica de València, 2010. http://hdl.handle.net/10251/8318.

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En esta tesis doctoral se describen las investigaciones realizadas con el objetivo de determinar las mejores tecnicas para construir un Reconocedor de Entidades Nombradas en Arabe. Tal sistema tendria la habilidad de identificar y clasificar las entidades nombradas que se encuentran en un texto arabe de dominio abierto. La tarea de Reconocimiento de Entidades Nombradas (REN) ayuda a otras tareas de Procesamiento del Lenguaje Natural (por ejemplo, la Recuperacion de Informacion, la Busqueda de Respuestas, la Traduccion Automatica, etc.) a lograr mejores resultados gracias al enriquecimi

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MENEZES, DANIEL SPECHT SILVA. "NAMED ENTITY RECOGNITION FOR PORTUGUESE." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2018. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=35855@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>FUNDAÇÃO DE APOIO À PESQUISA DO ESTADO DO RIO DE JANEIRO<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>BOLSA NOTA 10<br>A produção e acesso a quantidades imensas dados é um elemento pervasivo da era da informação. O volume de informação disponível é sem precedentes na história da humanidade e está sobre constante processo de expansão. Uma oportunidade que emerge neste ambiente é o desenvolvimento de aplicações que sejam capazes de estruturar conhecimento contido nesses dados. Neste co

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Alotaibi, Fahd Saleh S. "Fine-grained Arabic named entity recognition." Thesis, University of Birmingham, 2015. http://etheses.bham.ac.uk//id/eprint/5970/.

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This thesis addresses the problem of fine-grained NER for Arabic, which poses unique linguistic challenges to NER; such as the absence of capitalisation and short vowels, the complex morphology, and the highly in infection process. Instead of classifying the detected NE phrases into small sets of classes, we target a broader range (i.e. 50 fine-grained classes 'hierarchal-based of two levels') to increase the depth of the semantic knowledge extracted. This has increased the number of classes, complicating the task, when compared with traditional (coarse-grained) NER, because of the increase in

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Sun, Bowen. "Named entity recognition : Evaluation of Existing Systems." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-11223.

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Nowadays, one subfield of information extraction, Named Entity Recognition, becomes more and more important. It helps machine to recognize proper nouns (entities) in text and associating them with the appropriate types. Common types in NER systems are location, person name, date, address, etc. There are several NER systems in the world. Whats the main core technology of these systems? Which kind of system is better? How to improve this technology in the future? This master thesis will show the basic and detail knowledge about NER.Three existing NER systems will be choose to evaluate in this p

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MICKELIN, JOEL. "Named Entity Recognition with Support Vector Machines." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-138012.

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This report describes a degree project in Computer Science, the aim of which was to construct a system for Named Entity Recognition in Swedish texts of names of people, locations and organizations, as well as expressions for time. This system was constructed from the part-of-speech tagger Granska and the Support Vector Machine system SVMlin. The completed system was trained to recognize Named Entities by analyzing patterns in training corpora consisting of lists of example words belonging to each category. The system was initially trained to recognize patterns based on individual characters in

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Aljic, Almir, and Theodor Kraft. "Contextualising government reports using Named Entity Recognition." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281835.

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The science of making a computer understand text and process it, natural language processing, is a topic of great interest among researchers. This study aims to further that research by comparing the BERT algorithm and classic logistic regression when identifying names of public organizations. The results show that BERT outperforms its competitor in the task from the data which consisted of public state inquiries and reports. Furthermore a literature study was conducted as a way of exploring how a system for NER can be implemented into the management of an organization. The study found that th

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Zhang, Yaxi. "Named Entity Recognition for Social Media Text." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-395978.

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This thesis aims to perform named entity recognition for English social media texts. Named Entity Recognition (NER) is applied in many NLP tasks as an important preprocessing procedure. Social media texts contain lots of real-time data and therefore serve as a valuable source for information extraction. Nevertheless, NER for social media texts is a rather challenging task due to the noisy context. Traditional approaches to deal with this task use hand-crafted features but prove to be both time-consuming and very task-specific. As a result, they fail to deliver satisfactory performance. The goa

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Traboulsi, Hayssam N. "Named entity recognition : a local grammar-based approach." Thesis, University of Surrey, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.431104.

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Alasiry, Areej Mohammed. "Named entity recognition and classification in search queries." Thesis, Birkbeck (University of London), 2015. http://bbktheses.da.ulcc.ac.uk/154/.

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Named Entity Recognition and Classification is the task of extracting from text, instances of different entity classes such as person, location, or company. This task has recently been applied to web search queries in order to better understand their semantics, where a search query consists of linguistic units that users submit to a search engine to convey their search need. Discovering and analysing the linguistic units comprising a search query enables search engines to reveal and meet users' search intents. As a result, recent research has concentrated on analysing the constituent units com

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Algahtani, Shabib Mallouh. "Arabic named entity recognition : a corpus-based study." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/arabic-named-entity-recognition-a-corpusbased-study(6d7bbbd0-c2eb-4e6a-8ba5-b370f5c8d0e5).html.

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The task of finding and classifying proper nouns in natural language text is the core of most Named Entity Recognition (NER) systems. The NER problem has received much attention, as NER forms the basic building block of any Information Extraction system. Although finding and classifying proper nouns in text is a very challenging task in English, the task benefits a great deal from the distinguishing orthographic feature of capitalization. When this feature is missing, as in uppercase text, or is present at the start of a sentence, ambiguity increases, and requires more knowledge sources to res

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Althobaiti, Maha. "Minimally-supervised methods for Arabic Named Entity Recognition." Thesis, University of Essex, 2016. http://repository.essex.ac.uk/16144/.

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Named Entity Recognition (NER) has attracted much attention over the past twenty years, as a main task of Information Extraction. The current dominant techniques for addressing NER are supervised methods that can achieve high performance, but require new manually annotated data for every new domain and/or genre change. Our work focuses on approaches that make it possible to tackle new domains with minimal human intervention to identify Named Entities (NEs) in Arabic text. Specifically, we investigate two minimally-supervised methods: semi-supervised learning and distant learning. Our semi-supe

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MANCHANDA, PIKAKSHI. "Towards Adaptation of Named Entity Recognition and Linking Frameworks." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2017. http://hdl.handle.net/10281/151639.

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L'estrazione di informazioni strutturate a partire dal “web non strutturato”, ha suscitato un notevole interesse da parte delle comunità scientifiche che si occupano di elaborazione del linguaggio naturale e di sistemi basati sulla conoscenza per sviluppare a pieno la visione del “web semantico”. Nell'era moderna, l'uso pervasivo e diffuso delle reti sociali ha portato alla produzione di un flusso continuo di informazioni su piattaforme quali Twitter o Facebook, definite anche piattaforme di microblogging. Tali sorgenti informative, accessibili in tempo reale, producono informazioni caratteriz

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Liljeqvist, Sandra. "Named Entity Recognition for Search Queries in the Music Domain." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-193332.

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This thesis addresses the problem of named entity recognition (NER) in music-related search queries. NER is the task of identifying keywords in text and classifying them into predefined categories. Previous work in the field has mainly focused on longer documents of editorial texts. However, in recent years, the application of NER for queries has attracted increased attention. This task is, however, acknowledged to be challenging due to queries being short, ungrammatical and containing minimal linguistic context. The usage of NER for queries is especially useful for the implementation of natur

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Bridal, Olle. "Named-entity recognition with BERT for anonymization of medical records." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176547.

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Sharing data is an important part of the progress of science in many fields. In the largely deep learning dominated field of natural language processing, textual resources are in high demand. In certain domains, such as that of medical records, the sharing of data is limited by ethical and legal restrictions and therefore requires anonymization. The process of manual anonymization is tedious and expensive, thus automated anonymization is of great value. Since medical records consist of unstructured text, pieces of sensitive information have to be identified in order to be masked for anonymizat

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Borovikova, Mariya. "Domain Adaptation of Named Entity Recognition for Plant Health Monitoring." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG105.

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La complexité croissante des écosystèmes agricoles et le La complexité croissante des écosystèmes agricoles et le besoin urgent de surveillance efficace de la santé des plantes rendent nécessaires des solutions technologiques avancées pour traiter les données textuelles. Située dans le cadre du projet BEYOND, cette thèse répond à ces besoins en améliorant les systèmes de reconnaissance d'entités nommées (REN) adaptés au domaine de la santé des plantes. Reconnaissant les limites des approches traditionnelles, cette recherche intègre des stratégies d'adaptation au domaine.La principale contribut

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Andersson-Säll, Tim. "Transforming Legal Entity Recognition." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447240.

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Transformer-based architectures have in recent years advanced state-of-the-art performance in Natural Language Processing. Researchers have successfully adapted such models to downstream tasks within NLP in a domain-specific setting. This thesis examines the application of these models to the legal domain by doing Named Entity Recognition (NER) in a setting of scarce training data. Three different pre-trained BERT models are fine-tuned on a set of 101 court case documents, whereof one model is pre-trained on legal corpora and the other two on general corpora. Experiments are run to evaluate th

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Yavuz, Sermet Reha. "Named Entity Recognition In Turkish With Bayesian Learning And Hybrid Approaches." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613964/index.pdf.

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Information Extraction (IE) is the process of extracting structured and important pieces of information from a set of unstructured text documents in natural language. The final goal of structured information extraction is to populate a database and reach data effectively. Our study focuses on named entity recognition (NER) which is an important subtask of IE. NER is the task that deals with extraction of named entities like person, location, organization names, temporal expressions (date and time) and numerical expressions (money and percent). NER research on Turkish is known to be rare. There

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Rolnic, Sergiu Gabriel. "Anonimizzazione di documenti mediante Named Entity Recognition e Neural Language Model." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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I transformers hanno rivoluzionato il mondo dell'interpretazione linguistica da parte delle macchine. La possibilità di addestrare un neural language model su vocabolari ed enciclopedie intere, per poi utilizzare le conoscenze acquisite e trasmetterle a task specifici, ha permesso di raggiungere lo stato dell'arte in quasi tutti i domini applicativi del Natural Language Processing. In questo contesto è stato sviluppato un applicativo per l'anonimizzazione di file, in grado di identificare entità specifiche rappresentative di dati personali.

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Zhang, Ziqi. "Named entity recognition : challenges in document annotation, gazetteer construction and disambiguation." Thesis, University of Sheffield, 2013. http://etheses.whiterose.ac.uk/19276/.

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The 'information explosion' has generated unprecedented amount of published information that is still growing at an astonishing rate. As the amount of information grows, the problem of managing the information becomes challenging. A key to this challenge rests on the technology of Information Extraction, which automatically transforms un-structured textual data into structured representation that can be interpreted and manipulated by machines. It is recognised that a fundamental task in Information Extraction is Named Entity Recognition, the goals of which are identifying references of named e

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Remstam, Sophie. "A Novel Low Annotation-Cost Interactive Framework for Named Entity Recognition." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-287459.

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Named entity recognition (NER) is the process to sequence label an unstructured data to solve high ambiguity. It targets to identify all the named entities using predefined categories. The datasets used in domain-specific NER tasks require manual annotation. Unfortunately, the annotators are usually domain experts which can be extremely expensive. Recent studies have shown that using active learning combined with a machine learning algorithm can reduce the annotation effort. However, active learning queries experts for labels dozens of times during the training. The waiting time between the it

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Rosvall, Erik. "Comparison of sequence classification techniques with BERT for named entity recognition." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261419.

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This thesis takes its starting point from the recent advances in Natural Language Processing being developed upon the Transformer model. One of the significant developments recently was the release of a deep bidirectional encoder called BERT that broke several state of the art results at its release. BERT utilises Transfer Learning to improve modelling language dependencies in texts. BERT is used for several different Natural Language Processing tasks, this thesis looks at Named Entity Recognition, sometimes referred to as sequence classification. This thesis compares the model architecture as

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MEHMOOD, TAHIR. "Knowledge Transfer Techniques in Deep Learning for Biomedical Named Entity Recognition." Doctoral thesis, Università degli studi di Brescia, 2021. http://hdl.handle.net/11379/546098.

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Elsebai, A. "A rules based system for named entity recognition in modern standard Arabic." Thesis, University of Salford, 2009. http://usir.salford.ac.uk/14925/.

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The amount of textual information available electronically has made it difficult for many users to find and access the right information within acceptable time. Research communities in the natural language processing (NLP) field are developing tools and techniques to alleviate these problems and help users in exploiting these vast resources. These techniques include Information Retrieval (IR) and Information Extraction (IE). The work described in this thesis concerns IE and more specifically, named entity extraction in Arabic. The Arabic language is of significant interest to the NLP community

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Kim, Ji-Hwan. "Named entity recognition from speech and its use in the generation of enhanced speech recognition output." Thesis, University of Cambridge, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.621352.

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Alanazi, Saad. "A Named Entity Recognition system applied to Arabic text in the medical domain." Thesis, Staffordshire University, 2017. http://eprints.staffs.ac.uk/3129/.

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Currently, 30-35% of the global population uses the Internet. Furthermore, there is a rapidly increasing number of non-English language internet users, accompanied by an also increasing amount of unstructured text online. One area replete with underexploited online text is the Arabic medical domain, and one method that can be used to extract valuable data from Arabic medical texts is Named Entity Recognition (NER). NER is the process by which a system can automatically detect and categorise Named Entities (NE). NER has numerous applications in many domains, and medical texts are no exception.

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Pafilis, Evangelos. "Web-based named entity recognition and data integration to accelerate molecular biology research." [S.l. : s.n.], 2008. http://nbn-resolving.de/urn:nbn:de:bsz:16-opus-89706.

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Hubková, Helena. "Named-entity recognition in Czech historical texts : Using a CNN-BiLSTM neural network model." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385682.

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The thesis presents named-entity recognition in Czech historical newspapers from Modern Access to Historical Sources Project. Our goal was to create a specific corpus and annotation manual for the project and evaluate neural networks methods for named-entity recognition within the task. We created the corpus using scanned Czech historical newspapers. The scanned pages were converted to digitize text by optical character recognition (OCR) method. The data were preprocessed by deleting some OCR errors. We also defined specific named entities types for our task and created an annotation manual wi

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Bayraktar, Ozkan. "Person Name Recognition In Turkish Financial Texts By Using Local Grammar Approach." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608862/index.pdf.

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Named entity recognition (NER) is the task of identifying the named entities (NEs) in the texts and classifying them into semantic categories such as person, organization, and place names and time, date, monetary, and percent expressions. NER has two principal aims: identification of NEs and classification of them into semantic categories. The local grammar (LG) approach has recently been shown to be superior to other NER techniques such as the probabilistic approach, the symbolic approach, and the hybrid approach in terms of being able to work with untagged corpora. The LG approach does not r

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Nikolic, Vladan. "Creating a Graph Database from a Set of Documents." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-176042.

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In the context of search, it may be advantageous in some use-cases to have documents saved in a graph database rather than a document-orientated database. Graph databases are able to model relationships between objects, in this case documents, in ways which allow for efficient retrieval, as well as search queries that are slightly more specific or complex. This report will attempt to explore the possibilities of storing an existing set of documents into a graph database. A Named Entity Recognizer was used on a set of news articles in order to extract entities from each news article’s body of t

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Volkova, Svitlana. "Entity extraction, animal disease-related event recognition and classification from web." Thesis, Kansas State University, 2010. http://hdl.handle.net/2097/4593.

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Master of Science<br>Department of Computing and Information Sciences<br>William H. Hsu<br>Global epidemic surveillance is an essential task for national biosecurity management and bioterrorism prevention. The main goal is to protect the public from major health threads. To perform this task effectively one requires reliable, timely and accurate medical information from a wide range of sources. Towards this goal, we present a framework for epidemiological analytics that can be used to extract and visualize infectious disease outbreaks from the variety of unstructured web sources automatic

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Puttkammer, Martin Johannes. "Outomatiese Afrikaanse tekseenheididentifisering / deur Martin J. Puttkammer." Thesis, North-West University, 2006. http://hdl.handle.net/10394/872.

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An important core technology in the development of human language technology applications is an automatic morphological analyser. Such a morphological analyser consists of various modules, one of which is a tokeniser. At present no tokeniser exists for Afrikaans and it has therefore been impossible to develop a morphological analyser for Afrikaans. Thus, in this research project such a tokeniser is being developed, and the project therefore has two objectives: i)to postulate a tag set for integrated tokenisation, and ii) to develop an algorithm for integrated tokenisation. In order to achieve

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Lenas, Erik. "Prerequisites for Extracting Entity Relations from Swedish Texts." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281275.

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Natural language processing (NLP) is a vibrant area of research with many practical applications today like sentiment analyses, text labeling, questioning an- swering, machine translation and automatic text summarizing. At the moment, research is mainly focused on the English language, although many other lan- guages are trying to catch up. This work focuses on an area within NLP called information extraction, and more specifically on relation extraction, that is, to ex- tract relations between entities in a text. What this work aims at is to use machine learning techniques to build a Swedish

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Torstensson, Erik, and Fredrik Carls. "Undersökande studie inom Information Extraction : Konsten att Klassicera." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189327.

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Denna uppsats är en undersökande studie inom Information Extraction. Huvudsyftet är att skapa och utvärdera metoder inom Information Extraction och undersöka hur de kan hjälpa till att förbättra det vetenskapliga resultatet av klassificering av textelement. En deluppgift är att utvärdera den befintliga marknaden för Information Extraction i Sverige. För att göra detta har vi skapat ett program bestående av två delar. Den första delen utgörs av ett basfall som är en enkel metod och den andra är mer avancerad och använder sig av olika tekniker inom området Information Extra

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Matthew, Gordon Derrac. "Benoemde-entiteitherkenning vir Afrikaans / G.D. Matthew." Thesis, North-West University, 2013. http://hdl.handle.net/10394/10170.

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According to the Constitution of South Africa, the government is required to make all the infor-mation in the ten indigenous languages of South Africa (excluding English), available to the public. For this reason, the government made the information, that already existed for these ten languages, available to the public and an effort is also been made to increase the amount of resources available in these languages (Groenewald & Du Plooy, 2010). This release of infor-mation further helps to implement Krauwer‟s (2003) idea that there is an inventory for the mini-mal number of language-related re

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Soriano-Morales, Edmundo-Pavel. "Hypergraphs and information fusion for term representation enrichment : applications to named entity recognition and word sense disambiguation." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSE2009/document.

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Donner du sens aux données textuelles est une besoin essentielle pour faire les ordinateurs comprendre notre langage. Pour extraire des informations exploitables du texte, nous devons les représenter avec des descripteurs avant d’utiliser des techniques d’apprentissage. Dans ce sens, le but de cette thèse est de faire la lumière sur les représentations hétérogènes des mots et sur la façon de les exploiter tout en abordant leur nature implicitement éparse.Dans un premier temps, nous proposons un modèle de réseau basé sur des hypergraphes qui contient des données linguistiques hétérogènes dans u

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Yosef, Mohamed Amir [Verfasser], and Gerhard [Akademischer Betreuer] Weikum. "U-AIDA : a customizable system for named entity recognition, classification, and disambiguation / Mohamed Amir Yosef. Betreuer: Gerhard Weikum." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2016. http://d-nb.info/1083894722/34.

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Olsson, Fredrik. "Bootstrapping Named Entity Annotation by Means of Active Machine Learning: A Method for Creating Corpora." Doctoral thesis, SICS, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-22935.

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This thesis describes the development and in-depth empirical investigation of a method, called BootMark, for bootstrapping the marking up of named entities in textual documents. The reason for working with documents, as opposed to for instance sentences or phrases, is that the BootMark method is concerned with the creation of corpora. The claim made in the thesis is that BootMark requires a human annotator to manually annotate fewer documents in order to produce a named entity recognizer with a given performance, than would be needed if the documents forming the basis for the recognizer were r

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Kliegr, Tomáš. "Unsupervised Entity Classification with Wikipedia and WordNet." Doctoral thesis, Vysoká škola ekonomická v Praze, 2007. http://www.nusl.cz/ntk/nusl-126861.

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This dissertation addresses the problem of classification of entities in text represented by noun phrases. The goal of this thesis is to develop a method for automated classification of entities appearing in datasets consisting of short textual fragments. The emphasis is on unsupervised and semi-supervised methods that will allow for fine-grained character of the assigned classes and require no labeled instances for training. The set of target classes is either user-defined or determined automatically. Our initial attempt to address the entity classification problem is called Semantic Concept

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Osborne, John D., Matthew B. Neu, Maria I. Danila, Thamar Solorio, and Steven J. Bethard. "CUILESS2016: a clinical corpus applying compositional normalization of text mentions." BIOMED CENTRAL LTD, 2018. http://hdl.handle.net/10150/626563.

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Background: Traditionally text mention normalization corpora have normalized concepts to single ontology identifiers ("pre-coordinated concepts"). Less frequently, normalization corpora have used concepts with multiple identifiers ("post-coordinated concepts") but the additional identifiers have been restricted to a defined set of relationships to the core concept. This approach limits the ability of the normalization process to express semantic meaning. We generated a freely available corpus using post-coordinated concepts without a defined set of relationships that we term "compositional con

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Alruily, Meshrif. "Using text mining to identify crime patterns from Arabic crime news report corpus." Thesis, De Montfort University, 2012. http://hdl.handle.net/2086/7584.

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Most text mining techniques have been proposed only for English text, and even here, most research has been conducted on specific texts related to special contexts within the English language, such as politics, medicine and crime. In contrast, although Arabic is a widely spoken language, few mining tools have been developed to process Arabic text, and some Arabic domains have not been studied at all. In fact, Arabic is a language with a very complex morphology because it is highly inflectional, and therefore, dealing with texts written in Arabic is highly complicated. This research studies the

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Sil, Avirup. "Entity Information Extraction using Structured and Semi-structured resources." Diss., Temple University Libraries, 2014. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/272966.

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Computer and Information Science<br>Ph.D.<br>Among all the tasks that exist in Information Extraction, Entity Linking, also referred to as entity disambiguation or entity resolution, is a new and important problem which has recently caught the attention of a lot of researchers in the Natural Language Processing (NLP) community. The task involves linking/matching a textual mention of a named-entity (like a person or a movie-name) to an appropriate entry in a database (e.g. Wikipedia or IMDB). If the database does not contain the entity it should return NIL (out-of-database) value. Existing tech

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Al-Olimat, Hussein S. "Knowledge-Enabled Entity Extraction." Wright State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1578100367105233.

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Audenaert, Michael Neal. "Feature identification framework and applications (FIFA)." Texas A&M University, 2005. http://hdl.handle.net/1969.1/3317.

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Large digital libraries typically contain large collections of heterogeneous resources intended to be delivered to a variety of user communities. One key challenge for these libraries is providing tight integration between resources both within a single collection and across the several collections of the library with out requiring hand coding. One key tool in doing this is elucidating the internal structure of the digital resources and using that structure to form connections between the resources. The heterogeneous nature of the collections and the diversity of the needs in the user communit

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Duarte, Eduardo Santos. "Sentiment analysis on twitter for the portuguese language." Master's thesis, Faculdade de Ciências e Tecnologia, 2013. http://hdl.handle.net/10362/11338.

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Dissertação para obtenção do Grau de Mestre em Engenharia Informática<br>With the growth and popularity of the internet and more specifically of social networks, users can more easily share their thoughts, insights and experiences with others. Messages shared via social networks provide useful information for several applications, such as monitoring specific targets for sentiment or comparing the public sentiment on several targets, avoiding the traditional marketing research method with the use of surveys to explicitly get the public opinion. To extract information from the large amounts o

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Smith, Andrew. "Logarithmic opinion pools for conditional random fields." Thesis, University of Edinburgh, 2007. http://hdl.handle.net/1842/1730.

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Since their recent introduction, conditional random fields (CRFs) have been successfully applied to a multitude of structured labelling tasks in many different domains. Examples include natural language processing (NLP), bioinformatics and computer vision. Within NLP itself we have seen many different application areas, like named entity recognition, shallow parsing, information extraction from research papers and language modelling. Most of this work has demonstrated the need, directly or indirectly, to employ some form of regularisation when applying CRFs in order to overcome the tendency fo

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Michalko, Boris. "Extrakce informací z textu." Master's thesis, Vysoká škola ekonomická v Praze, 2008. http://www.nusl.cz/ntk/nusl-2840.

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Cieľom tejto práce je preskúmať dostupné systémy pre extrakciu informácií a možnosti ich použitia v projekte MedIEQ. Teoretickú časť obsahuje úvod do oblasti extrakcie informácií. Popisujem účel, potreby a použitie a vzťah k iným úlohám spracovania prirodzeného jazyka. Prechádzam históriou, nedávnym vývojom, meraním výkonnosti a jeho kritikou. Taktiež popisujem všeobecnú architektúru IE systému a základné úlohy, ktoré má riešiť, s dôrazom na extrakciu entít. V praktickej časti sa nacházda prehľad algoritmov používaných v systémoch pre extrakciu informácií. Opisujem oba typy algoritmov ? pravid

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Dai, Xiang. "Recognising Biomedical Names: Challenges and Solutions." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/25482.

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The growth rate in the amount of biomedical documents is staggering. Unlocking information trapped in these documents can enable researchers and practitioners to operate confidently in the information world. Biomedical Named Entity Recognition (NER), the task of recognising biomedical names, is usually employed as the first step of the NLP pipeline. Standard NER models, based on sequence tagging technique, are good at recognising short entity mentions in the generic domain. However, there are several open challenges of applying these models to recognise biomedical names: ● Biomedical names

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Winter, Luca. "Algoritmy pro rozpoznávání pojmenovaných entit." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2017. http://www.nusl.cz/ntk/nusl-320108.

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The aim of this work is to find out which algorithm is the best at recognizing named entities in e-mail messages. The theoretical part explains the existing tools in this field. The practical part describes the design of two tools specifically designed to create new models capable of recognizing named entities in e-mail messages. The first tool is based on a neural network and the second tool uses a CRF graph model. The existing and newly created tools and their ability to generalize are compared on a subset of e-mail messages provided by Kiwi.com.

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Hemati, Wahed [Verfasser], Alexander [Gutachter] Mehler, and Visvanathan [Gutachter] Ramesh. "TextImager-VSD : large scale verb sense disambiguation and named entity recognition in the context of TextImager / Wahed Hemati ; Gutachter: Alexander Mehler, Visvanathan Ramesh." Frankfurt am Main : Universitätsbibliothek Johann Christian Senckenberg, 2019. http://d-nb.info/1219963224/34.

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Mendes, Pablo N. "Adaptive Semantic Annotation of Entity and Concept Mentions in Text." Wright State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=wright1401665504.

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