Relevant bibliographies by topics / Textmining

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Textmining'

Author: Grafiati
Published: 4 June 2021
Last updated: 21 February 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Textmining.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Textmining"

1

Han, Haneul, Yongjin Kim, and Nala Shin. "Textmining Analysis on Block Chain and Logistics Industry." Journal of Humanities and Social sciences 21 12, no. 5 (October 31, 2021): 2567–78. http://dx.doi.org/10.22143/hss21.12.5.181.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Schönbach, Christian, Takeshi Nagashima, and Akihiko Konagaya. "Textmining in support of knowledge discovery for vaccine development." Methods 34, no. 4 (December 2004): 488–95. http://dx.doi.org/10.1016/j.ymeth.2004.06.009.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Kim, J. D., T. Ohta, Y. Tateisi, and J. Tsujii. "GENIA corpus--a semantically annotated corpus for bio-textmining." Bioinformatics 19, Suppl 1 (July 3, 2003): i180—i182. http://dx.doi.org/10.1093/bioinformatics/btg1023.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Go, Gwang-Su, Won-Kyo Jung, Young-Geun Shin, Sang-Sung Park, and Dong-Sik Jang. "A Study on Development of Patent Information Retrieval Using Textmining." Journal of the Korea Academia-Industrial cooperation Society 12, no. 8 (August 31, 2011): 3677–88. http://dx.doi.org/10.5762/kais.2011.12.8.3677.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

KODAIRA, Tomoe, and Takehiko ITO. "A textmining study of titles of autobibliography of people with schizophrenia." Proceedings of the Annual Convention of the Japanese Psychological Association 77 (September 19, 2013): 1PM—108–1PM—108. http://dx.doi.org/10.4992/pacjpa.77.0_1pm-108.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Yang, Ji yoon and Joo Yun Kim. "A study on public perception of wales Millenium Centre architecture using textmining." Journal of Korea Intitute of Spatial Design 12, no. 5 (October 2017): 193–201. http://dx.doi.org/10.35216/kisd.2017.12.5.193.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Yang, Ji-Yun. "A Study on Public Perception on Department Store Experience Trend through Textmining." Journal of the Korean Institute of Interior Design 31, no. 4 (August 31, 2022): 41–49. http://dx.doi.org/10.14774/jkiid.2022.31.4.041.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Park, Jinkyeun, Taekyoun Kim, and Min Song. "Entitymetrics Analysis of the Research Works of Dong-ju Yun using Textmining." Journal of the Korean BIBLIA Society for library and Information Science 28, no. 1 (March 30, 2017): 191–207. http://dx.doi.org/10.14699/kbiblia.2017.28.1.191.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Yang, Ji Yun. "A Study on Iconic Architecture Strategy through SNS Textmining - focused on British cases -." Journal of Basic Design & Art 23, no. 3 (June 30, 2022): 153–64. http://dx.doi.org/10.47294/ksbda.23.3.12.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Oh, Chang-Seok, Yong-taeck Lee, and Minsu Ko. "Establishment of ITS Policy Issues Investigation Method in the Road Section applied Textmining." Journal of The Korea Institute of Intelligent Transport Systems 15, no. 6 (December 31, 2016): 10–23. http://dx.doi.org/10.12815/kits.2016.15.6.010.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Textmining"

1

Ly, Antoine. "Algorithmes de machine learning en assurance : solvabilité, textmining, anonymisation et transparence." Thesis, Paris Est, 2019. http://www.theses.fr/2019PESC2030/document.

Full text
Abstract:
En été 2013, le terme de "Big Data" fait son apparition et suscite un fort intérêt auprès des entreprises. Cette thèse étudie ainsi l'apport de ces méthodes aux sciences actuarielles. Elle aborde aussi bien les enjeux théoriques que pratiques sur des thématiques à fort potentiel comme l'textit{Optical Character Recognition} (OCR), l'analyse de texte, l'anonymisation des données ou encore l'interprétabilité des modèles. Commençant par l'application des méthodes du machine learning dans le calcul du capital économique, nous tentons ensuite de mieux illustrer la frontrière qui peut exister entre l'apprentissage automatique et la statistique. Mettant ainsi en avant certains avantages et différentes techniques, nous étudions alors l'application des réseaux de neurones profonds dans l'analyse optique de documents et de texte, une fois extrait. L'utilisation de méthodes complexes et la mise en application du Réglement Général sur la Protection des Données (RGPD) en 2018 nous a amené à étudier les potentiels impacts sur les modèles tarifaires. En appliquant ainsi des méthodes d'anonymisation sur des modèles de calcul de prime pure en assurance non-vie, nous avons exploré différentes approches de généralisation basées sur l'apprentissage non-supervisé. Enfin, la réglementation imposant également des critères en terme d'explication des modèles, nous concluons par une étude générale des méthodes qui permettent aujourd'hui de mieux comprendre les méthodes complexes telles que les réseaux de neurones
In summer 2013, the term "Big Data" appeared and attracted a lot of interest from companies. This thesis examines the contribution of these methods to actuarial science. It addresses both theoretical and practical issues on high-potential themes such as textit{Optical Character Recognition} (OCR), text analysis, data anonymization and model interpretability. Starting with the application of machine learning methods in the calculation of economic capital, we then try to better illustrate the boundary that may exist between automatic learning and statistics. Highlighting certain advantages and different techniques, we then study the application of deep neural networks in the optical analysis of documents and text, once extracted. The use of complex methods and the implementation of the General Data Protection Regulation (GDPR) in 2018 led us to study its potential impacts on pricing models. By applying anonymization methods to pure premium calculation models in non-life insurance, we explored different generalization approaches based on unsupervised learning. Finally, as regulations also impose criteria in terms of model explanation, we conclude with a general study of methods that now allow a better understanding of complex methods such as neural networks
APA, Harvard, Vancouver, ISO, and other styles
2

Muthiah, Sathappan. "Forecasting Protests by Detecting Future Time Mentions in News and Social Media." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/49535.

Full text
Abstract:
Civil unrest (protests, strikes, and ``occupy'' events) is a common occurrence in both democracies and authoritarian regimes. The study of civil unrest is a key topic for political scientists as it helps capture an important mechanism by which citizenry express themselves. In countries where civil unrest is lawful, qualitative analysis has revealed that more than 75% of the protests are planned, organized, and/or announced in advance; therefore detecting future time mentions in relevant news and social media is a simple way to develop a protest forecasting system. We develop such a system in this thesis, using a combination of key phrase learning to identify what to look for, probabilistic soft logic to reason about location occurrences in extracted results, and time normalization to resolve future tense mentions. We illustrate the application of our system to 10 countries in Latin America, viz. Argentina, Brazil, Chile, Colombia, Ecuador, El Salvador, Mexico, Paraguay, Uruguay, and Venezuela. Results demonstrate our successes in capturing significant societal unrest in these countries with an average lead time of 4.08 days. We also study the selective superiorities of news media versus social media (Twitter, Facebook) to identify relevant tradeoffs.
Master of Science
APA, Harvard, Vancouver, ISO, and other styles
3

TOMA, Anca Mirela. "L'ascesa del FinTech: un' analisi statistica delle opportunità e dei rischi di un nuovo modello di business." Doctoral thesis, Università degli studi di Bergamo, 2021. http://hdl.handle.net/10446/185922.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

López, Aravena Camilo Alberto. "Diseño y construcción de una plataforma de clasificación de texto basada en textmining aplicada sobre una red de blogs para Betazeta Networks S.A." Tesis, Universidad de Chile, 2012. http://www.repositorio.uchile.cl/handle/2250/110971.

Full text
Abstract:
Betazeta Networks S.A. es una empresa dedicada a la publicación de información mediante una red de blogs de diversas temáticas. A corto plazo, la empresa necesita visualizar cómo se distribuye el contenido actual para tomar decisiones estratégicas respecto al mercado que enmarca los contenidos que publican. En el mediano plazo, la empresa emitirá contenido generado por los usuarios, el cual debe ser revisado para mantener la calidad de cada Blog. Para esto se requiere contar con métodos automáticos de clasificación para dichos mensajes, los cuales serán revisados por periodistas expertos en diferentes áreas. El trabajo realizado en esta memoria constituye un prototipo que apunta a resolver la problemática de la empresa. Para ello se construye una plataforma de procesamiento de texto, denominada Tanalyzer, que permite manejar grandes volúmenes de información, visualizar, clasificar y hacer predicciones sobre las temáticas de nuevos documentos utilizando text-mining, sub área de la minería de datos especializada en texto, implementando el modelo de tópicos generativo Latent Dirichlet Allocation. Las pruebas realizadas al software son satisfactorias. Sobre un modelo que maneja 8 temáticas, cada una asociada a uno de los 8 blogs de la empresa que se encuentran bajo estudio, es posible predecir documentos con un 80% de precision y 64% de recall, lo que demuestra la viabilidad de la aplicación. Actualmente, la solución permite escalar tanto en velocidad como en costos. Con un tiempo de ejecución de 2.5 horas para 300.000 documentos, permite entrenar en ese tiempo un mes de publicaciones a una tasa de 1250 artículos enviados diariamente repartidos en 8 blogs, frente a la tasa actual de publicación de 12.5 artículos diarios por blog. Entrenar 10 veces un modelo de esta magnitud representa para la empresa un costo de $USD 17 utilizando los servicios de Amazon Cloud Computing. Si bien los resultados obtenidos son positivos y la memoria cumple sus objetivos a cabailidad, existen múltiples mejoras realizables a la plataforma que constituyen el trabajo futuro de esta investigación y que deben ser consideradas por la empresa para llevar a cabo una implementación en producción. Por un lado es posible mejorar aún más los tiempos de ejecución y por otra parte se debe solucionar la disminución de recall cuando la cantidad de temáticas y la especificidad de éstas aumenta.
APA, Harvard, Vancouver, ISO, and other styles
5

Doms, Andreas. "GoPubMed: Ontology-based literature search for the life sciences." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2009. http://nbn-resolving.de/urn:nbn:de:bsz:14-ds-1232454035091-47450.

Full text
Abstract:
Background: Most of our biomedical knowledge is only accessible through texts. The biomedical literature grows exponentially and PubMed comprises over 18.000.000 literature abstracts. Recently much effort has been put into the creation of biomedical ontologies which capture biomedical facts. The exploitation of ontologies to explore the scientific literature is a new area of research. Motivation: When people search, they have questions in mind. Answering questions in a domain requires the knowledge of the terminology of that domain. Classical search engines do not provide background knowledge for the presentation of search results. Ontology annotated structured databases allow for data-mining. The hypothesis is that ontology annotated literature databases allow for text-mining. The central problem is to associate scientific publications with ontological concepts. This is a prerequisite for ontology-based literature search. The question then is how to answer biomedical questions using ontologies and a literature corpus. Finally the task is to automate bibliometric analyses on an corpus of scientific publications. Approach: Recent joint efforts on automatically extracting information from free text showed that the applied methods are complementary. The idea is to employ the rich terminological and relational information stored in biomedical ontologies to markup biomedical text documents. Based on established semantic links between documents and ontology concepts the goal is to answer biomedical question on a corpus of documents. The entirely annotated literature corpus allows for the first time to automatically generate bibliometric analyses for ontological concepts, authors and institutions. Results: This work includes a novel annotation framework for free texts with ontological concepts. The framework allows to generate recognition patterns rules from the terminological and relational information in an ontology. Maximum entropy models can be trained to distinguish the meaning of ambiguous concept labels. The framework was used to develop a annotation pipeline for PubMed abstracts with 27,863 Gene Ontology concepts. The evaluation of the recognition performance yielded a precision of 79.9% and a recall of 72.7% improving the previously used algorithm by 25,7% f-measure. The evaluation was done on a manually created (by the original authors) curation corpus of 689 PubMed abstracts with 18,356 curations of concepts. Methods to reason over large amounts of documents with ontologies were developed. The ability to answer questions with the online system was shown on a set of biomedical question of the TREC Genomics Track 2006 benchmark. This work includes the first ontology-based, large scale, online available, up-to-date bibliometric analysis for topics in molecular biology represented by GO concepts. The automatic bibliometric analysis is in line with existing, but often out-dated, manual analyses. Outlook: A number of promising continuations starting from this work have been spun off. A freely available online search engine has a growing user community. A spin-off company was funded by the High-Tech Gründerfonds which commercializes the new ontology-based search paradigm. Several off-springs of GoPubMed including GoWeb (general web search), Go3R (search in replacement, reduction, refinement methods for animal experiments), GoGene (search in gene/protein databases) are developed.
APA, Harvard, Vancouver, ISO, and other styles
6

Doms, Andreas. "GoPubMed: Ontology-based literature search for the life sciences." Doctoral thesis, Technische Universität Dresden, 2008. https://tud.qucosa.de/id/qucosa%3A23835.

Full text
Abstract:
Background: Most of our biomedical knowledge is only accessible through texts. The biomedical literature grows exponentially and PubMed comprises over 18.000.000 literature abstracts. Recently much effort has been put into the creation of biomedical ontologies which capture biomedical facts. The exploitation of ontologies to explore the scientific literature is a new area of research. Motivation: When people search, they have questions in mind. Answering questions in a domain requires the knowledge of the terminology of that domain. Classical search engines do not provide background knowledge for the presentation of search results. Ontology annotated structured databases allow for data-mining. The hypothesis is that ontology annotated literature databases allow for text-mining. The central problem is to associate scientific publications with ontological concepts. This is a prerequisite for ontology-based literature search. The question then is how to answer biomedical questions using ontologies and a literature corpus. Finally the task is to automate bibliometric analyses on an corpus of scientific publications. Approach: Recent joint efforts on automatically extracting information from free text showed that the applied methods are complementary. The idea is to employ the rich terminological and relational information stored in biomedical ontologies to markup biomedical text documents. Based on established semantic links between documents and ontology concepts the goal is to answer biomedical question on a corpus of documents. The entirely annotated literature corpus allows for the first time to automatically generate bibliometric analyses for ontological concepts, authors and institutions. Results: This work includes a novel annotation framework for free texts with ontological concepts. The framework allows to generate recognition patterns rules from the terminological and relational information in an ontology. Maximum entropy models can be trained to distinguish the meaning of ambiguous concept labels. The framework was used to develop a annotation pipeline for PubMed abstracts with 27,863 Gene Ontology concepts. The evaluation of the recognition performance yielded a precision of 79.9% and a recall of 72.7% improving the previously used algorithm by 25,7% f-measure. The evaluation was done on a manually created (by the original authors) curation corpus of 689 PubMed abstracts with 18,356 curations of concepts. Methods to reason over large amounts of documents with ontologies were developed. The ability to answer questions with the online system was shown on a set of biomedical question of the TREC Genomics Track 2006 benchmark. This work includes the first ontology-based, large scale, online available, up-to-date bibliometric analysis for topics in molecular biology represented by GO concepts. The automatic bibliometric analysis is in line with existing, but often out-dated, manual analyses. Outlook: A number of promising continuations starting from this work have been spun off. A freely available online search engine has a growing user community. A spin-off company was funded by the High-Tech Gründerfonds which commercializes the new ontology-based search paradigm. Several off-springs of GoPubMed including GoWeb (general web search), Go3R (search in replacement, reduction, refinement methods for animal experiments), GoGene (search in gene/protein databases) are developed.
APA, Harvard, Vancouver, ISO, and other styles
7

Hakenberg, Jörg. "Mining relations from the biomedical literature." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2010. http://dx.doi.org/10.18452/16073.

Full text
Abstract:
Textmining beschäftigt sich mit der automatisierten Annotierung von Texten und der Extraktion einzelner Informationen aus Texten, die dann für die Weiterverarbeitung zur Verfügung stehen. Texte können dabei kurze Zusammenfassungen oder komplette Artikel sein, zum Beispiel Webseiten und wissenschaftliche Artikel, umfassen aber auch textuelle Einträge in sonst strukturierten Datenbanken. Diese Dissertationsschrift bespricht zwei wesentliche Themen des biomedizinischen Textmining: die Extraktion von Zusammenhängen zwischen biologischen Entitäten ---das Hauptaugenmerk liegt dabei auf der Erkennung von Protein-Protein-Interaktionen---, und einen notwendigen Vorverarbeitungsschritt, die Erkennung von Proteinnamen. Diese Schrift beschreibt Ziele, Herausforderungen, sowie typische Herangehensweisen für alle wesentlichen Komponenten des biomedizinischen Textmining. Wir stellen eigene Methoden zur Erkennung von Proteinnamen sowie der Extraktion von Protein-Protein-Interaktionen vor. Zwei eigene Verfahren zur Erkennung von Proteinnamen werden besprochen, eines basierend auf einem Klassifikationsproblem, das andere basierend auf Suche in Wörterbüchern. Für die Extraktion von Interaktionen entwickeln wir eine Methode zur automatischen Annotierung großer Mengen von Text im Bezug auf Relationen; diese Annotationen werden dann zur Mustererkennung verwendet, um anschließend die gefundenen Muster auf neuen Text anwenden zu können. Um Muster zu erkennen, berechnen wir Ähnlichkeiten zwischen zuvor gefundenen Sätzen, die denselben Typ von Relation/Interaktion beschreiben. Diese Ähnlichkeiten speichern wir als sogenannte `consensus patterns''. Wir entwickeln eine Alignmentstrategie, die mehrschichtige Annotationen pro Position im Muster erlaubt. In Versuchen auf bekannten Benchmarks zeigen wir empirisch, dass unser vollautomatisches Verfahren Resultate erzielt, die vergleichbar sind mit existierenden Methoden, welche umfangreiche Eingriffe von Experten voraussetzen.
Text mining deals with the automated annotation of texts and the extraction of facts from textual data for subsequent analysis. Such texts range from short articles and abstracts to large documents, for instance web pages and scientific articles, but also include textual descriptions in otherwise structured databases. This thesis focuses on two key problems in biomedical text mining: relationship extraction from biomedical abstracts ---in particular, protein--protein interactions---, and a pre-requisite step, named entity recognition ---again focusing on proteins. This thesis presents goals, challenges, and typical approaches for each of the main building blocks in biomedical text mining. We present out own approaches for named entity recognition of proteins and relationship extraction of protein-protein interactions. For the first, we describe two methods, one set up as a classification task, the other based on dictionary-matching. For relationship extraction, we develop a methodology to automatically annotate large amounts of unlabeled data for relations, and make use of such annotations in a pattern matching strategy. This strategy first extracts similarities between sentences that describe relations, storing them as consensus patterns. We develop a sentence alignment approach that introduces multi-layer alignment, making use of multiple annotations per word. For the task of extracting protein-protein interactions, empirical results show that our methodology performs comparable to existing approaches that require a large amount of human intervention, either for annotation of data or creation of models.
APA, Harvard, Vancouver, ISO, and other styles
8

Pfeifer, Katja. "Serviceorientiertes Text Mining am Beispiel von Entitätsextrahierenden Diensten." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-150646.

Full text
Abstract:
Der Großteil des geschäftsrelevanten Wissens liegt heute als unstrukturierte Information in Form von Textdaten auf Internetseiten, in Office-Dokumenten oder Foreneinträgen vor. Zur Extraktion und Verwertung dieser unstrukturierten Informationen wurde eine Vielzahl von Text-Mining-Lösungen entwickelt. Viele dieser Systeme wurden in der jüngeren Vergangenheit als Webdienste zugänglich gemacht, um die Verwertung und Integration zu vereinfachen. Die Kombination verschiedener solcher Text-Mining-Dienste zur Lösung konkreter Extraktionsaufgaben erscheint vielversprechend, da so bestehende Stärken ausgenutzt, Schwächen der Systeme minimiert werden können und die Nutzung von Text-Mining-Lösungen vereinfacht werden kann. Die vorliegende Arbeit adressiert die flexible Kombination von Text-Mining-Diensten in einem serviceorientierten System und erweitert den Stand der Technik um gezielte Methoden zur Auswahl der Text-Mining-Dienste, zur Aggregation der Ergebnisse und zur Abbildung der eingesetzten Klassifikationsschemata. Zunächst wird die derzeit existierende Dienstlandschaft analysiert und aufbauend darauf eine Ontologie zur funktionalen Beschreibung der Dienste bereitgestellt, so dass die funktionsgesteuerte Auswahl und Kombination der Text-Mining-Dienste ermöglicht wird. Des Weiteren werden am Beispiel entitätsextrahierender Dienste Algorithmen zur qualitätssteigernden Kombination von Extraktionsergebnissen erarbeitet und umfangreich evaluiert. Die Arbeit wird durch zusätzliche Abbildungs- und Integrationsprozesse ergänzt, die eine Anwendbarkeit auch in heterogenen Dienstlandschaften, bei denen unterschiedliche Klassifikationsschemata zum Einsatz kommen, gewährleisten. Zudem werden Möglichkeiten der Übertragbarkeit auf andere Text-Mining-Methoden erörtert.
APA, Harvard, Vancouver, ISO, and other styles
9

Dietze, Heiko. "GoWeb: Semantic Search and Browsing for the Life Sciences." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2010. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-63267.

Full text
Abstract:
Searching is a fundamental task to support research. Current search engines are keyword-based. Semantic technologies promise a next generation of semantic search engines, which will be able to answer questions. Current approaches either apply natural language processing to unstructured text or they assume the existence of structured statements over which they can reason. This work provides a system for combining the classical keyword-based search engines with semantic annotation. Conventional search results are annotated using a customized annotation algorithm, which takes the textual properties and requirements such as speed and scalability into account. The biomedical background knowledge consists of the GeneOntology and Medical Subject Headings and other related entities, e.g. proteins/gene names and person names. Together they provide the relevant semantic context for a search engine for the life sciences. We develop the system GoWeb for semantic web search and evaluate it using three benchmarks. It is shown that GoWeb is able to aid question answering with success rates up to 79%. Furthermore, the system also includes semantic hyperlinks that enable semantic browsing of the knowledge space. The semantic hyperlinks facilitate the use of the eScience infrastructure, even complex workflows of composed web services. To complement the web search of GoWeb, other data source and more specialized information needs are tested in different prototypes. This includes patents and intranet search. Semantic search is applicable for these usage scenarios, but the developed systems also show limits of the semantic approach. That is the size, applicability and completeness of the integrated ontologies, as well as technical issues of text-extraction and meta-data information gathering. Additionally, semantic indexing as an alternative approach to implement semantic search is implemented and evaluated with a question answering benchmark. A semantic index can help to answer questions and address some limitations of GoWeb. Still the maintenance and optimization of such an index is a challenge, whereas GoWeb provides a straightforward system.
APA, Harvard, Vancouver, ISO, and other styles
10

Gerner, Lars Martin Anders. "Integrating text-mining approaches to identify entities and extract events from the biomedical literature." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/integrating-textmining-approaches-to-identify-entities-and-extract-events-from-the-biomedical-literature(44f8e79a-3782-4687-85c7-eee1fda5cb76).html.

Full text
Abstract:
The amount of biomedical literature available is increasing at an exponential rate and is becoming increasingly difficult to navigate. Text-mining methods can potentially mitigate this problem, through the systematic and large-scale extraction of structured information from inherently unstructured biomedical text. This thesis reports the development of four text-mining systems that, by building on each other, has enabled the extraction of information about a large number of published statements in the biomedical literature. The first system, LINNAEUS, enables highly accurate detection ('recognition') and identification ('normalization') of species names in biomedical articles. Building on LINNAEUS, we implemented a range of improvements in the GNAT system, enabling high-throughput gene/protein detection and identification. Using gene/protein identification from GNAT, we developed the Gene Expression Text Miner (GETM), which extracts information about gene expression statements. Finally, building on GETM as a pilot project, we constructed the BioContext integrated event extraction system, which was used to extract information about over 11 million distinct biomolecular processes in 10.9 million abstracts and 230,000 full-text articles. The ability to detect negated statements in the BioContext system enables the preliminary analysis of potential contradictions in the biomedical literature. All tools (LINNAEUS, GNAT, GETM, and BioContext) are available under open-source software licenses, and LINNAEUS and GNAT are available as online web-services. All extracted data (36 million BioContext statements, 720,000 GETM statements, 72,000 contradictions, 37 million mentions of species names, 80 million mentions of gene names, and 57 million mentions of anatomical location names) is available for bulk download. In addition, the data extracted by GETM and BioContext is also available to biologists through easy-to-use search interfaces.
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Textmining"

1

Sauer, Sebastian. "Textmining." In Moderne Datenanalyse mit R, 449–62. Wiesbaden: Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-21587-3_24.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Lee, Yu Lim, Minji Jung, In-Hyoung Park, Ahyoung Kim, and Jae-Eun Chung. "Examining Feedback of Apple Watch Users in Korea Using Textmining Analysis." In Advances in Intelligent Systems and Computing, 865–70. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39512-4_132.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Stachowiak, Maria, Artur Skoczylas, Paweł Stefaniak, and Paweł Śliwiński. "Multidimensional Failure Analysis Based on Data Fusion from Various Sources Using TextMining Techniques." In Advances in Intelligent Systems and Computing, 766–76. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68154-8_66.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Öztayşi, Başar, Ahmet Tezcan Tekin, Cansu Özdikicioğlu, and Kerim Caner Tümkaya. "Personalized Content Recommendation Engine for Web Publishing Services Using Textmining and Predictive Analytics." In Advances in Business Information Systems and Analytics, 113–24. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2148-8.ch007.

Full text
Abstract:
Recommendation systems have become very important especially for internet based business such as e-commerce and web publishing. While content based filtering and collaborative filtering are most commonly used groups in recommendation systems there are still researches for new approaches. In this study, a personalized recommendation system based on text mining and predictive analytics is proposed for a real world web publishing company. The approach given in this chapter first preprocesses existing web contents, integrate the structured data with history of a specific user and create an extended TDM for the user. Then this data is used for prediction of the users interest in new content. In order to reach that point, SVM, K-NN and Naïve Bayesian methods are used. Finally, the best performing method is used for determining the interest level of the user in a new content. Based on the forecasted interest levels the system recommends among the alternatives.
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Textmining"

1

You, Hanmin. "Customer Complaints Analysis Using Textmining Method." In WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2022. http://dx.doi.org/10.4271/2022-01-0131.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Inje, Bhushan, and Ujawla Patil. "Operational pattern detection in textmining using pattern taxonomy." In 2014 International Conference on Electronics and Communication Systems (ICECS). IEEE, 2014. http://dx.doi.org/10.1109/ecs.2014.6892780.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Haberle, Matthias, Martin Werner, and Xiao Xiang Zhu. "Building Type Classification from Social Media Texts via Geo-Spatial Textmining." In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019. http://dx.doi.org/10.1109/igarss.2019.8898836.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Lee, Sang Hee, Yong Won Cho, Eun Tack Im, and Gwang-Yong Gim. "A Study on Customer Satisfaction Analysis of Public Institutions using Social Textmining." In 2019 20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD). IEEE, 2019. http://dx.doi.org/10.1109/snpd.2019.8935791.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Meisheri, Hardik, Rupsa Saha, Priyanka Sinha, and Lipika Dey. "Textmining at EmoInt-2017: A Deep Learning Approach to Sentiment Intensity Scoring of English Tweets." In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Stroudsburg, PA, USA: Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/w17-5226.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Holzinger, Andreas, Klaus-Martin Simonic, and Pinar Yildirim. "Disease-Disease Relationships for Rheumatic Diseases: Web-Based Biomedical Textmining an Knowledge Discovery to Assist Medical Decision Making." In 2012 IEEE 36th Annual Computer Software and Applications Conference - COMPSAC 2012. IEEE, 2012. http://dx.doi.org/10.1109/compsac.2012.77.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

M, Saranya, Arockia Xavier Annie R, and Geetha T V. "Relation Extraction between Biomedical Entities from Literature using Semi- Supervised Learning Approach." In 10th International Conference on Natural Language Processing (NLP 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.112306.

Full text
Abstract:
Now-a-days, people around the world are infected by many new diseases. The cost of developing or discovering a new drug for the newly discovered disease is very high and prolonged process. These could be eliminated with the help of already existing resources. To identify the candidates from the existing drugs, we need to extract the relation between the drug, target and disease by textming a large-scale literature. Recently, computational approaches which is used for identifying the relationships between the entities in biomedical domain are appearing as an active area of research for drug discovery as it needs more man power. Due to the limited computational approaches, the relation extraction between drug-gene and genedisease association from the unstructured biomedical documents is very hard. In this work, we proposed a semi-supervised approach named pattern based bootstrapping method to extract the direct relations between drug, gene and disease from the biomedical literature. These direct relationships are used to infer indirect relationships between entities such as drug and disease. Now these indirect relationships are used to determine the new candidates for drug repositioning which in turn will reduce the time and the patient’s risk.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Textmining' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography