Academic literature on the topic 'Text modeling'
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Journal articles on the topic "Text modeling"
Bell, Timothy, Ian H. Witten, and John G. Cleary. "Modeling for text compression." ACM Computing Surveys 21, no. 4 (December 1989): 557–91. http://dx.doi.org/10.1145/76894.76896.
Full textLeboeuf, J. ‐N, D. K. Lee, B. A. Carreras, N. Dominguez, J. H. Harris, C. L. Hedrick, C. Hidalgo, et al. "TEXT tokamak edge turbulence modeling." Physics of Fluids B: Plasma Physics 3, no. 8 (August 1991): 2291–99. http://dx.doi.org/10.1063/1.859596.
Full textCHEN, YE-SHO. "ZIPF'S LAWS IN TEXT MODELING." International Journal of General Systems 15, no. 3 (August 1989): 233–52. http://dx.doi.org/10.1080/03081078908935048.
Full textKartika, Rica, and Yulianti Rasyid. "PENGARUH TEKNIK PEMODELAN TERHADAP KETERAMPILAN MENULIS TEKS PROSEDUR SISWA KELAS VII SMP NEGERI 14 PADANG." Pendidikan Bahasa Indonesia 8, no. 2 (June 10, 2019): 81. http://dx.doi.org/10.24036/104518-019883.
Full textUtami, Mimi Putri, Andria Catri Thamsin, and Mohammad Hafrison. "PENGARUH TEKNIK PEMODELAN TERHADAP KETERAMPILAN MENULIS TEKS PROSEDUR KOMPLEKS SISWA KELAS XI SMKN 1 TAPAN." Pendidikan Bahasa Indonesia 8, no. 1 (March 17, 2019): 121. http://dx.doi.org/10.24036/103925-019883.
Full textRashid, Junaid, Syed Muhammad Adnan Shah, and Aun Irtaza. "Fuzzy topic modeling approach for text mining over short text." Information Processing & Management 56, no. 6 (November 2019): 102060. http://dx.doi.org/10.1016/j.ipm.2019.102060.
Full text庞, 劲羽. "Polarity-GuidedShort Text Sentiment Analysis Modeling." Instrumentation and Equipments 08, no. 04 (2020): 124–30. http://dx.doi.org/10.12677/iae.2020.84016.
Full textStrok, Fedor. "Modeling Text Similarity with Parse Thickets." Procedia Computer Science 31 (2014): 1012–21. http://dx.doi.org/10.1016/j.procs.2014.05.354.
Full textKino, Yasunobu. "Conceptual Modeling supported by Text Analysis." Procedia Computer Science 126 (2018): 1387–94. http://dx.doi.org/10.1016/j.procs.2018.08.090.
Full textSHIEBER, STUART M., and RANI NELKEN. "Abbreviated text input using language modeling." Natural Language Engineering 13, no. 2 (July 6, 2006): 165–83. http://dx.doi.org/10.1017/s1351324906004311.
Full textDissertations / Theses on the topic "Text modeling"
Sauper, Christina (Christina Joan). "Content modeling for social media text." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/75648.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 129-136).
This thesis focuses on machine learning methods for extracting information from user-generated content. Instances of this data such as product and restaurant reviews have become increasingly valuable and influential in daily decision making. In this work, I consider a range of extraction tasks such as sentiment analysis and aspect-based review aggregation. These tasks have been well studied in the context of newswire documents, but the informal and colloquial nature of social media poses significant new challenges. The key idea behind our approach is to automatically induce the content structure of individual documents given a large, noisy collection of user-generated content. This structure enables us to model the connection between individual documents and effectively aggregate their content. The models I propose demonstrate that content structure can be utilized at both document and phrase level to aid in standard text analysis tasks. At the document level, I capture this idea by joining the original task features with global contextual information. The coupling of the content model and the task-specific model allows the two components to mutually influence each other during learning. At the phrase level, I utilize a generative Bayesian topic model where a set of properties and corresponding attribute tendencies are represented as hidden variables. The model explains how the observed text arises from the latent variables, thereby connecting text fragments with corresponding properties and attributes.
by Christina Sauper.
Ph.D.
Harrysson, Mattias. "Neural probabilistic topic modeling of short and messy text." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189532.
Full textAtt utforska enorma mängder användargenererad data med ämnen postulerar ett nytt sätt att hitta användbar information. Ämnena antas vara “gömda” och måste “avtäckas” med statistiska metoder såsom ämnesmodellering. Dock är användargenererad data generellt sätt kort och stökig t.ex. informella chattkonversationer, mycket slangord och “brus” som kan vara URL:er eller andra former av pseudo-text. Denna typ av data är svår att bearbeta för de flesta algoritmer i naturligt språk, inklusive ämnesmodellering. Det här arbetet har försökt hitta den metod som objektivt ger dem bättre ämnena ur kort och stökig text i en jämförande studie. De metoder som jämfördes var latent Dirichlet allocation (LDA), Re-organized LDA (RO-LDA), Gaussian Mixture Model (GMM) with distributed representation of words samt en egen metod med namnet Neural Probabilistic Topic Modeling (NPTM) baserat på tidigare arbeten. Den slutsats som kan dras är att NPTM har en tendens att ge bättre ämnen på kort och stökig text jämfört med LDA och RO-LDA. GMM lyckades inte ge några meningsfulla resultat alls. Resultaten är mindre bevisande eftersom NPTM har problem med långa körtider vilket innebär att tillräckligt många stickprov inte kunde erhållas för ett statistiskt test.
Reynolds, Douglas A. "A Gaussian mixture modeling approach to text-independent speaker identification." Diss., Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/16903.
Full textSad, Hamed. "Text entry interfaces on mobile devices : modeling, design and evaluation." Lorient, 2009. http://www.theses.fr/2009LORIS153.
Full textCette thèse concerne la saisie de texte sur les dispositifs mobiles qui est un domaine très actif de l'interaction home-machine (IHM) depuis quelques années. Cette recherche traite plus particulièrement de l'évaluation des méthodes de saisie de texte. Nous abordons les deux approches principales de l'évaluation : l'évaluation expérimentale et l’évaluation par modélisation. Une plateforme pour l'évaluation expérimentale est présentée. Elle vise à faciliter, rendre plus rapide et plus reproductive l'évaluation. Cette plateforme qui intègre de nombreuses méthodes de saisie rend possible leur comparaison et simplifie grandement la conception et le développement d'une nouvelle idée pour la saisie de texte. Enfin, la plateforme inclut des outils pour l'évaluation, comme un outil d'aide à la création d'un corpus de test représentatif de la langue cible ou un outil pour automatiser l'analyse des performances sur la base des métriques standard du domaine. Nous proposons également un cadre (framework) pour décrire, classifier et modéliser les opérations impliquées dans la saisie de texte sur dispositifs mobiles. À la base, nous distinguons deux étapes : la planification et l’exécution. La première étape correspond au processus mental de planification des actions physiques requises pour saisir un mot avec une méthode donnée ; la deuxième étape concerne le processus moteur de production du texte à partir des actions disponibles pour l’utilisateur. On propose, dans cette thèse, des mesures pour une évaluation théorique de ces deux phases de la saisie. L’évaluation théorique s’appuie sur des modèles de la performance humaine pour l’exécution des différentes tâches impliquées dans la saisie. Nous avons étudié en particulier deux tâches fréquemment utilisées dans la phase d'exécution : la sélection d’un mot dans une liste de mots et le pointage et défilement par une interaction basée sur l’inclinaison (tilt). Nous présentons un algorithme et des recommandations pour la conception de claviers ambigus efficaces. Un modèle de performance pour la sélection de mot dans une liste est proposé qui fait suite à une étude expérimentale. Un autre modèle prédit le temps d’exécution du ciblage et du défilement par inclinaison sur un dispositif mobile. Enfin, nous proposons de nouvelles directions originales pour la saisie de texte qui concernent la phase de planification. L’approche exploite notre « connaissance du monde » ainsi que la nature syntaxique des mots du message. Nous nous affranchissons le plus possible d’une saisie de texte « lettre par lettre », pour suivre une approche pictographique où les mots les plus fréquents sont Page 8 Résumé Text entry interfaces on mobile devices: Modeling, design and evaluation, PhD thesis 2009 directement accessibles à partir d’une représentation graphique. L’approche proposée exploite également la syntaxe de la langue pour permettre à l'utilisateur de filtrer gestuellement le mot désiré selon sa catégorie grammaticale. Cette approche pictographique et syntaxique utilise un moteur de prédiction et un codage du lexique spécifiques qui assurent une structure de données efficace et adaptée aux performances limitées des dispositifs mobiles
Cheng, Chi Wa. "Probabilistic topic modeling and classification probabilistic PCA for text corpora." HKBU Institutional Repository, 2011. http://repository.hkbu.edu.hk/etd_ra/1263.
Full textRen, Zhaowei. "Analysis and Modeling of the Structure of Semantic Dynamics in Texts." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1512045439740177.
Full textPreece, Daniel Joseph. "Text Identification by Example." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2060.pdf.
Full textBischof, Jonathan Michael. "Interpretable and Scalable Bayesian Models for Advertising and Text." Thesis, Harvard University, 2014. http://dissertations.umi.com/gsas.harvard:11400.
Full textStatistics
Foulds, James Richard. "Latent Variable Modeling for Networks and Text| Algorithms, Models and Evaluation Techniques." Thesis, University of California, Irvine, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3631094.
Full textIn the era of the internet, we are connected to an overwhelming abundance of information. As more facets of our lives become digitized, there is a growing need for automatic tools to help us find the content we care about. To tackle the problem of information overload, a standard machine learning approach is to perform dimensionality reduction, transforming complicated high-dimensional data into a manageable, low-dimensional form. Probabilistic latent variable models provide a powerful and elegant framework for performing this transformation in a principled way. This thesis makes several advances for modeling two of the most ubiquitous types of online information: networks and text data.
Our first contribution is to develop a model for social networks as they vary over time. The model recovers latent feature representations of each individual, and tracks these representations as they change dynamically. We also show how to use text information to interpret these latent features.
Continuing the theme of modeling networks and text data, we next build a model of citation networks. The model finds influential scientific articles and the influence relationships between the articles, potentially opening the door for automated exploratory tools for scientists. The increasing prevalence of web-scale data sets provides both an opportunity and a challenge. With more data we can fit more accurate models, as long as our learning algorithms are up to the task. To meet this challenge, we present an algorithm for learning latent Dirichlet allocation topic models quickly, accurately and at scale. The algorithm leverages stochastic techniques, as well as the collapsed representation of the model. We use it to build a topic model on 4.6 million articles from the open encyclopedia Wikipedia in a matter of hours, and on a corpus of 1740 machine learning articles from the NIPS conference in seconds.
Finally, evaluating the predictive performance of topic models is an important yet computationally difficult task. We develop one algorithm for comparing topic models, and another for measuring the progress of learning algorithms for these models. The latter method achieves better estimates than previous algorithms, in many cases with an order of magnitude less computational effort.
Alsadhan, Majed. "An application of topic modeling algorithms to text analytics in business intelligence." Thesis, Kansas State University, 2014. http://hdl.handle.net/2097/17580.
Full textDepartment of Computing and Information Sciences
Doina Caragea
William H. Hsu
In this work, we focus on the task of clustering businesses in the state of Kansas based on the content of their websites and their business listing information. Our goal is to cluster the businesses and overcome the challenges facing current approaches such as: data noise, low number of clustered businesses, and lack of evaluation approach. We propose an LSA-based approach to analyze the businesses’ data and cluster those businesses by using Bisecting K-Means algorithm. In this approach, we analyze the businesses’ data by using LSA and produce businesses’ representations in a reduced space. We then use the businesses’ representations to cluster the businesses by applying the Bisecting K-Means algorithm. We also apply an existing LDA-based approach to cluster the businesses and compare the results with our proposed LSA-based approach at the end. In this work, we evaluate the results by using a human-expert-based evaluation procedure. At the end, we visualize the clusters produced in this work by using Google Earth and Tableau. According to our evaluation procedure, the LDA-based approach performed slightly bet- ter then the LSA-based approach. However, with the LDA-based approach, there were some limitations which are: low number of clustered businesses, and not being able to produce a hierarchical tree for the clusters. With the LSA-based approach, we were able to cluster all the businesses and produce a hierarchical tree for the clusters.
Books on the topic "Text modeling"
Berry, Michael W. Understanding search engines: Mathematical modeling and text retrieval. 2nd ed. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2006.
Find full textMurray, Browne, ed. Understanding search engines: Mathematical modeling and text retrieval. 2nd ed. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2005.
Find full textMehler, Alexander, Kai-Uwe Kühnberger, Henning Lobin, Harald Lüngen, Angelika Storrer, and Andreas Witt, eds. Modeling, Learning, and Processing of Text Technological Data Structures. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-22613-7.
Full textMehler, Alexander. Modeling, Learning, and Processing of Text Technological Data Structures. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2012.
Find full textGoutsos, Dionysis. Modeling discourse topic: Sequential relations and strategies in expository text. Norwood, N.J: Ablex, 1997.
Find full textHartmanis, Juris, Jan Leeuwen, and Gerhard Goos. Interactive Multimedia Documents: Modeling, Authoring, and Implementation Experiences. Berlin: Springer-Verlag Berlin Heidelberg, 1999.
Find full textEvans, W. Bryce. Improving your speech: Here's how : voice and diction, basic phonetics, phonics supplement, speech modeling : a basic text with ... exercises. 2nd ed. Dubuque, Iowa: Kendall/Hunt Pub. Co., 1992.
Find full textAquifer test modeling. Boca Raton, FL: CRC Press, 2007.
Find full textUser modelling in text generation. London: Pinter Publishers, 1993.
Find full textModelling in behavioural ecology: An introductory text. London: Croom Helm, 1986.
Find full textBook chapters on the topic "Text modeling"
Atkinson-Abutridy, John. "Topic Modeling." In Text Analytics, 165–84. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003280996-8.
Full textSong, Xiaoge, Yirui Wu, Wenhai Wang, and Tong Lu. "TK-Text: Multi-shaped Scene Text Detection via Instance Segmentation." In MultiMedia Modeling, 201–13. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37734-2_17.
Full textL. Jockers, Matthew, and Rosamond Thalken. "Topic Modeling." In Text Analysis with R, 211–35. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39643-5_17.
Full textPastor, Moisés, and Francisco Casacuberta. "Pronunciation Modeling." In Text, Speech and Language Technology, 133–48. Dordrecht: Springer Netherlands, 2005. http://dx.doi.org/10.1007/1-4020-2637-4_8.
Full textRayar, Frédéric, and Seiichi Uchida. "Comic Text Detection Using Neural Network Approach." In MultiMedia Modeling, 672–83. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05716-9_60.
Full textQin, Zhengcai, Bin Wu, and Meng Li. "Text Image Deblurring via Intensity Extremums Prior." In MultiMedia Modeling, 505–17. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73603-7_41.
Full textChen, Jianjun, Hongtao Xie, Yue Hu, and Chenggang Yan. "Uyghur Text Localization with Fast Component Detection." In MultiMedia Modeling, 565–77. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73603-7_46.
Full textSong, Jiayu, Qinghua Xu, Wei Liu, Yueran Zu, and Mengdong Chen. "Semantic and Morphological Information Guided Chinese Text Classification." In MultiMedia Modeling, 14–26. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37734-2_2.
Full textGu, Yang, Veda C. Storey, and Carson C. Woo. "Conceptual Modeling for Financial Investment with Text Mining." In Conceptual Modeling, 528–35. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25264-3_39.
Full textAnandarajan, Murugan, Chelsey Hill, and Thomas Nolan. "Cluster Analysis: Modeling Groups in Text." In Practical Text Analytics, 93–115. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95663-3_7.
Full textConference papers on the topic "Text modeling"
Smith, David A., Ryan Cordell, and Elizabeth Maddock Dillon. "Infectious texts: Modeling text reuse in nineteenth-century newspapers." In 2013 IEEE International Conference on Big Data. IEEE, 2013. http://dx.doi.org/10.1109/bigdata.2013.6691675.
Full textElShal, Sarah, Mithila Mathad, Jaak Simm, Jesse Davis, and Yves Moreau. "Topic modeling of biomedical text." In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2016. http://dx.doi.org/10.1109/bibm.2016.7822606.
Full textMisra, Hemant, François Yvon, Joemon M. Jose, and Olivier Cappe. "Text segmentation via topic modeling." In Proceeding of the 18th ACM conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1645953.1646170.
Full textIchkineeva, Dilara. "TEXT THEME MODELING AS A TOOL FOR ANALYZING TEXT COMPREHENSION." In 4th SGEM International Multidisciplinary Scientific Conferences on SOCIAL SCIENCES and ARTS Proceedings. STEF92 Technology, 2017. http://dx.doi.org/10.5593/sgemsocial2017/32/s14.128.
Full textBarzilay, Regina. "Probabilistic approaches for modeling text structure and their application to text-to-text generation." In the 12th European Workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1610195.1610200.
Full text"Text Analysis with Ontology Reasoning." In Third International Symposium on Business Modeling and Software Design. SCITEPRESS - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004774100640073.
Full textKulekci, M. Oguzhan. "Compressed Context Modeling for Text Compression." In 2011 Data Compression Conference (DCC). IEEE, 2011. http://dx.doi.org/10.1109/dcc.2011.44.
Full textFleischmann, Kenneth R., Thomas Clay Templeton, and Jordan Boyd-Graber. "Modeling diverse standpoints in text classification." In the 2011 iConference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1940761.1940863.
Full textYao, Enpeng, Guoqing Zheng, Ou Jin, Shenghua Bao, Kailong Chen, Zhong Su, and Yong Yu. "Probabilistic text modeling with orthogonalized topics." In SIGIR '14: The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2600428.2609471.
Full textSmith, David A., Ryan Cordel, Elizabeth Maddock Dillon, Nick Stramp, and John Wilkerson. "Detecting and modeling local text reuse." In 2014 IEEE/ACM Joint Conference on Digital Libraries (JCDL). IEEE, 2014. http://dx.doi.org/10.1109/jcdl.2014.6970166.
Full textReports on the topic "Text modeling"
Modlo, Yevhenii O., Serhiy O. Semerikov, Pavlo P. Nechypurenko, Stanislav L. Bondarevskyi, Olena M. Bondarevska, and Stanislav T. Tolmachev. The use of mobile Internet devices in the formation of ICT component of bachelors in electromechanics competency in modeling of technical objects. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3264.
Full textMark, David J., Norman W. Scheffner, H. L. Butler, Barry W. Bunch, and Mark S. Dortch. Hydrodynamic and Water Quality Modeling of Lower Green Bay, Wisconsin. Volume 1. Main Text and Appendixes A - E. Fort Belvoir, VA: Defense Technical Information Center, September 1993. http://dx.doi.org/10.21236/ada270195.
Full textGrace, T. M., W. J. Frederick, M. Salcudean, and R. A. Wessel. Black liquor combustion validated recovery boiler modeling: Final year report. Volume 1 (Main text and Appendix I, sections 1--4). Office of Scientific and Technical Information (OSTI), August 1998. http://dx.doi.org/10.2172/296694.
Full textIntrator, T. Collaboration on Modeling of Ion Bernstein Wave Antenna Array and Coupling to Plasma on Tokamak Fusion Text Reactor. Final report. Office of Scientific and Technical Information (OSTI), June 2000. http://dx.doi.org/10.2172/761048.
Full textBilovska, Natalia. TACTICS OF APPROACHING THE AUTHOR CLOSER TO THE READER: INTERACTIVE COOPERATION. Ivan Franko National University of Lviv, February 2022. http://dx.doi.org/10.30970/vjo.2022.51.11408.
Full textKiv, Arnold, Serhiy Semerikov, Vladimir Soloviev, Liubov Kibalnyk, Hanna Danylchuk, and Andriy Matviychuk. Experimental Economics and Machine Learning for Prediction of Emergent Economy Dynamics. [б. в.], August 2019. http://dx.doi.org/10.31812/123456789/3209.
Full textZelenskyi, Arkadii A. Relevance of research of programs for semantic analysis of texts and review of methods of their realization. [б. в.], December 2018. http://dx.doi.org/10.31812/123456789/2884.
Full textKiv, Arnold, Pavlo Hryhoruk, Inesa Khvostina, Victoria Solovieva, Vladimir Soloviev, and Serhiy Semerikov. Machine learning of emerging markets in pandemic times. [б. в.], October 2020. http://dx.doi.org/10.31812/123456789/4122.
Full textКів, Арнольд Юхимович, Володимир Миколайович Соловйов, Сергій Олексійович Семеріков, Hanna B. Danylchuk, Liubov O. Kibalnyk, Andriy V. Matviychuk, Andrii M. Striuk, et al. Machine learning for prediction of emergent economy dynamics. Криворізький державний педагогічний університет, December 2021. http://dx.doi.org/10.31812/123456789/6973.
Full textLi, Jian, Peijing Li, and Jingwen Hu. Digital human modeling in automotive engineering applications: a systematic review and bibliometric mapping. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, October 2022. http://dx.doi.org/10.37766/inplasy2022.10.0094.
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