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Dissertations / Theses on the topic 'Hierarchical Multi-label Text Classification'

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Published: 25 June 2021

Last updated: 16 February 2022

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1

Dendamrongvit, Sareewan. "Induction in Hierarchical Multi-label Domains with Focus on Text Categorization." Scholarly Repository, 2011. http://scholarlyrepository.miami.edu/oa_dissertations/542.

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Induction of classifiers from sets of preclassified training examples is one of the most popular machine learning tasks. This dissertation focuses on the techniques needed in the field of automated text categorization. Here, each document can be labeled with more than one class, sometimes with many classes. Moreover, the classes are hierarchically organized, the mutual relations being typically expressed in terms of a generalization tree. Both aspects (multi-label classification and hierarchically organized classes) have so far received inadequate attention. Existing literature work largely as

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2

Borggren, Lukas. "Automatic Categorization of News Articles With Contextualized Language Models." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177004.

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This thesis investigates how pre-trained contextualized language models can be adapted for multi-label text classification of Swedish news articles. Various classifiers are built on pre-trained BERT and ELECTRA models, exploring global and local classifier approaches. Furthermore, the effects of domain specialization, using additional metadata features and model compression are investigated. Several hundred thousand news articles are gathered to create unlabeled and labeled datasets for pre-training and fine-tuning, respectively. The findings show that a local classifier approach is superior t

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3

Razavi, Amir Hossein. "Automatic Text Ontological Representation and Classification via Fundamental to Specific Conceptual Elements (TOR-FUSE)." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/23061.

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In this dissertation, we introduce a novel text representation method mainly used for text classification purpose. The presented representation method is initially based on a variety of closeness relationships between pairs of words in text passages within the entire corpus. This representation is then used as the basis for our multi-level lightweight ontological representation method (TOR-FUSE), in which documents are represented based on their contexts and the goal of the learning task. The method is unlike the traditional representation methods, in which all the documents are represented so

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4

Wei, Zhihua. "The research on chinese text multi-label classification." Thesis, Lyon 2, 2010. http://www.theses.fr/2010LYO20025/document.

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Text Classification (TC) which is an important field in information technology has many valuable applications. When facing the sea of information resources, the objects of TC are more complicated and diversity. The researches in pursuit of effective and practical TC technology are fairly challenging. More and more researchers regard that multi-label TC is more suited for many applications. This thesis analyses the difficulties and problems in multi-label TC and Chinese text representation based on a mass of algorithms for single-label TC and multi-label TC. Aiming at high dimensionality in fea

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5

Burkhardt, Sophie [Verfasser]. "Online Multi-label Text Classification using Topic Models / Sophie Burkhardt." Mainz : Universitätsbibliothek Mainz, 2018. http://d-nb.info/1173911235/34.

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6

Sendur, Zeynel. "Text Document Categorization by Machine Learning." Scholarly Repository, 2008. http://scholarlyrepository.miami.edu/oa_theses/209.

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Because of the explosion of digital and online text information, automatic organization of documents has become a very important research area. There are mainly two machine learning approaches to enhance the organization task of the digital documents. One of them is the supervised approach, where pre-defined category labels are assigned to documents based on the likelihood suggested by a training set of labeled documents; and the other one is the unsupervised approach, where there is no need for human intervention or labeled documents at any point in the whole process. In this thesis, we conce

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7

Artmann, Daniel. "Applying machine learning algorithms to multi-label text classification on GitHub issues." Thesis, Högskolan i Halmstad, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-43097.

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This report compares five machine learning algorithms in their ability to categorize code repositories. The focus of expanding software projects tend to shift from developing new software to the maintenance of the projects. Maintainers can label code repositories to organize the project, but this requires manual labor and time. This report will evaluate how machine learning algorithms perform in automatically classifying code repositories. Automatic classification can aid the management process by reducing both manual labor and human errors. GitHub provides online hosting for both private and

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8

Li, Xin. "Multi-label Learning under Different Labeling Scenarios." Diss., Temple University Libraries, 2015. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/350482.

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Computer and Information Science<br>Ph.D.<br>Traditional multi-class classification problems assume that each instance is associated with a single label from category set Y where |Y| > 2. Multi-label classification generalizes multi-class classification by allowing each instance to be associated with multiple labels from Y. In many real world data analysis problems, data objects can be assigned into multiple categories and hence produce multi-label classification problems. For example, an image for object categorization can be labeled as 'desk' and 'chair' simultaneously if it contains both ob

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9

Průša, Petr. "Multi-label klasifikace textových dokumentů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-412872.

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The master's thesis deals with automatic classifi cation of text document. It explains basic terms and problems of text mining. The thesis explains term clustering and shows some basic clustering algoritms. The thesis also shows some methods of classi fication and deals with matrix regression closely. Application using matrix regression for classifi cation was designed and developed. Experiments were focused on normalization and thresholding.

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10

Rios, Anthony. "Deep Neural Networks for Multi-Label Text Classification: Application to Coding Electronic Medical Records." UKnowledge, 2018. https://uknowledge.uky.edu/cs_etds/71.

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Coding Electronic Medical Records (EMRs) with diagnosis and procedure codes is an essential task for billing, secondary data analyses, and monitoring health trends. Both speed and accuracy of coding are critical. While coding errors could lead to more patient-side financial burden and misinterpretation of a patient’s well-being, timely coding is also needed to avoid backlogs and additional costs for the healthcare facility. Therefore, it is necessary to develop automated diagnosis and procedure code recommendation methods that can be used by professional medical coders. The main difficulty wit

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11

Tsatsaronis, George. "An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition." BioMed Central, 2015. https://tud.qucosa.de/id/qucosa%3A29496.

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This article provides an overview of the first BioASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. BioASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies.

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12

Tsatsaronis, George. "An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-202687.

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This article provides an overview of the first BioASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. BioASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies.

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13

Rodríguez, Medina Samuel. "Multi-Label Text Classification with Transfer Learning for Policy Documents : The Case of the Sustainable Development Goals." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-395186.

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We created and analyzed a text classification dataset from freely-available web documents from the United Nation's Sustainable Development Goals. We then used it to train and compare different multi-label text classifiers with the aim of exploring the alternatives for methods that facilitate the search of information of this type of documents. We explored the effectiveness of deep learning and transfer learning in text classification by fine-tuning different pre-trained language representations — Word2Vec, GloVe, ELMo, ULMFiT and BERT. We also compared these approaches against a baseline of mo

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14

Metz, Jean. "Abordagens para aprendizado semissupervisionado multirrótulo e hierárquico." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-13012012-144607/.

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A tarefa de classificação em Aprendizado de Máquina consiste da criação de modelos computacionais capazes de identificar automaticamente a classe de objetos pertencentes a um domínio pré-definido a partir de um conjunto de exemplos cuja classe é conhecida. Existem alguns cenários de classificação nos quais cada objeto pode estar associado não somente a uma classe, mas a várias classes ao mesmo tempo. Adicionalmente, nesses cenários denominados multirrótulo, as classes podem ser organizadas em uma taxonomia que representa as relações de generalização e especialização entre as diferentes classes

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15

Santos, Araken de Medeiros. "Investigando a combina??o de t?cnicas de aprendizado semissupervisionado e classifica??o hier?rquica multirr?tulo." Universidade Federal do Rio Grande do Norte, 2012. http://repositorio.ufrn.br:8080/jspui/handle/123456789/18690.

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Made available in DSpace on 2015-03-03T15:48:39Z (GMT). No. of bitstreams: 1 ArakenMS_TESE.pdf: 4060697 bytes, checksum: 5efe25ac134a602cc32c96b66e749ea0 (MD5) Previous issue date: 2012-05-25<br>Data classification is a task with high applicability in a lot of areas. Most methods for treating classification problems found in the literature dealing with single-label or traditional problems. In recent years has been identified a series of classification tasks in which the samples can be labeled at more than one class simultaneously (multi-label classification). Additionally, these classes can

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16

Cerri, Ricardo. "Redes neurais e algoritmos genéticos para problemas de classificação hierárquica multirrótulo." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-24032014-163900/.

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Em problemas convencionais de classificação, cada exemplo de um conjunto de dados é associado a apenas uma dentre duas ou mais classes. No entanto, existem problemas de classificação mais complexos, nos quais as classes envolvidas no problema são estruturadas hierarquicamente, possuindo subclasses e superclasses. Nesses problemas, exemplos podem ser atribuídos simultaneamente a classes pertencentes a dois ou mais caminhos de uma hierarquia, ou seja, exemplos podem ser classificados em várias classes localizadas em um mesmo nível hierárquico. Tal hierarquia pode ser estruturada como uma árvore

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17

Araújo, Hiury Nogueira de. "Utilizando aprendizado emissupervisionado multidescrição em problemas de classificação hierárquica multirrótulo." Universidade Federal Rural do Semi-Árido, 2017. http://bdtd.ufersa.edu.br:80/tede/handle/tede/839.

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Submitted by Lara Oliveira (lara@ufersa.edu.br) on 2018-03-14T20:25:58Z No. of bitstreams: 1 HiuryNA_DISSERT.pdf: 3188162 bytes, checksum: d40d42a78787557868ebc6d3cd5af945 (MD5)<br>Approved for entry into archive by Vanessa Christiane (referencia@ufersa.edu.br) on 2018-06-18T16:58:58Z (GMT) No. of bitstreams: 1 HiuryNA_DISSERT.pdf: 3188162 bytes, checksum: d40d42a78787557868ebc6d3cd5af945 (MD5)<br>Approved for entry into archive by Vanessa Christiane (referencia@ufersa.edu.br) on 2018-06-18T16:59:18Z (GMT) No. of bitstreams: 1 HiuryNA_DISSERT.pdf: 3188162 bytes, checksum: d40d42a78787557868ebc

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18

jia-liang, Chen, and 陳佳良. "Hierarchical Multi-class Text Classification Using Support Vector Machines." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/09344492149763276087.

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碩士<br>元智大學<br>資訊管理學系<br>96<br>This study presents a hierarchical multi-class text classification framework based on the characteristics of enterprise documents. The multi-class classifiers are based on Support Vector Machines using an one-against-one approach. The features used by each classifier are selected using DF (Document Frequency) and CC (Correlated Coefficient). We conducted experiments on two different datasets; one contains enterprise documents from IC a local equipment manufacture and the other contains mainland china news. The experimental results show that our proposed method per

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19

Tsai, Shang-Chi, and 蔡尚錡. "Leveraging Hierarchical Category Knowledge for Multi-Label Diagnostic Text Understanding." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/24rczv.

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碩士<br>國立臺灣大學<br>資料科學學位學程<br>107<br>Clinical notes are essential medical documents to record each patient''s symptoms. Each record is typically annotated with medical diagnostic codes, which means diagnosis and treatment. This paper focuses on predicting diagnostic codes given the descriptive present illness in electronic health records by leveraging domain knowledge. We investigate various losses in a convolutional model to utilize hierarchical category knowledge of diagnostic codes in order to allow the model to share semantics across different labels under the same category. The pr

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20

Rau, YI-EN, and 饒以恩. "An empirical study of multi-label text classification: word2vector vs traditional techniques." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/54syt9.

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碩士<br>國立中央大學<br>資訊管理學系在職專班<br>107<br>The development of the Internet has led to the rapid advancement of social media. Because the free speech and anonymity of social media characteristic, it causes abuse such as cyber harassment and Toxic Comments. Machine learning have changed many fields, for example computer vision, speech recognition and language processing. I will use the text classification of machine learning to effectively filter out Toxic Comments. The dataset is from the competition organized by Kaggle: Toxic Comment Classification Challenge, whose source is Wikipedia's comments. Th

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21

Ma, Long. "A Multi-label Text Classification Framework: Using Supervised and Unsupervised Feature Selection Strategy." 2017. http://scholarworks.gsu.edu/cs_diss/123.

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Text classification, the task of metadata to documents, requires significant time and effort when performed by humans. Moreover, with online-generated content explosively growing, it becomes a challenge for manually annotating with large scale and unstructured data. Currently, lots of state-or-art text mining methods have been applied to classification process, many of them based on the key word extraction. However, when using these key words as features in classification task, it is common that feature dimension is huge. In addition, how to select key words from tons of documents as features

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22

Chen, Sung-En, and 陳頌恩. "A hierarchical Multi-label Classification System of K-12 Cross-Knowledge Points Math Question." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/tqz79a.

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碩士<br>國立中央大學<br>資訊工程學系<br>107<br>With the development and progress of science and technology, the learning patterns also evolve. In Question-Driven learning, students clarify and validate what they learn by answering questions. Such a large number of questions needs good management. A well-performed management can avoid the situation that learning materials with the same knowledge set are deﬁned into different sections due to ambiguous expressions. In this work, a hierarchical classiﬁcation system that focuses on K-12 learning materials is proposed. We test several combination of document repr

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23

George, Susanna Serene. "Emergency Medical Service EMR-Driven Concept Extraction From Narrative Text." Thesis, 2021. http://dx.doi.org/10.7912/C2/68.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>Being in the midst of a pandemic with patients having minor symptoms that quickly become fatal to patients with situations like a stemi heart attack, a fatal accident injury, and so on, the importance of medical research to improve speed and efficiency in patient care, has increased. As researchers in the computer domain work hard to use automation in technology in assisting the first responders in the work they do, decreasing the cognitive load on the field crew, time taken for documentation of each patient case and improving

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24

(10947207), Susanna S. George. "EMERGENCY MEDICAL SERVICE EMR-DRIVEN CONCEPT EXTRACTION FROM NARRATIVE TEXT." Thesis, 2021.

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Being in the midst of a pandemic with patients having minor symptoms that quickly become fatal to patients with situations like a stemi heart attack, a fatal accident injury, and so on, the importance of medical research to improve speed and efficiency in patient care, has increased. As researchers in the computer domain work hard to use automation in technology in assisting the first responders in the work they do, decreasing the cognitive load on the field crew, time taken for documentation of each patient case and improving accuracy in details of a report has been a priority. <br>This paper

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