Relevant bibliographies by topics / TextRazor

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

Academic literature on the topic 'TextRazor'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "TextRazor"

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Jency Jose and Simritha R. "Sentiment Analysis and Topic Classification with LSTM Networks and TextRazor." International Journal of Data Informatics and Intelligent Computing 3, no. 2 (2024): 42–51. http://dx.doi.org/10.59461/ijdiic.v3i2.115.

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In the ever-evolving landscape of social media, where user-generated content shapes digital discourse, the need for nuanced sentiment analysis and topic extraction is paramount. This paper presents a comprehensive approach utilizing advanced Natural Language Processing (NLP) techniques to enhance user experience and foster a healthier digital environment. Leveraging Long Short-Term Memory (LSTM) networks for sentiment analysis and TextRazor for topic extraction, the system provides insights into emotional tones and key themes within social media discussions. Through intuitive visualizations, u
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Thangasamy, Sathiyaraj, Navaneethakrishnan Subalalitha Chinnaudayar, VINOTH ANTHONYSAMY та ін. "A Tamil Lexical Analysis Framework Based on Tolkappiyam Linguistic Rules தொல்காப்பிய மொழியியல் விதிகளை அடிப்படையாகக் கொண்ட தமிழ் சொல்லியல் பகுப்பாய்வுகக் கட்டமைப்பு". Inam: Multi - Disciplinary International E-Journal for Tamil Studies 11, № 42 (2025): 3–12. https://doi.org/10.5281/zenodo.15458462.

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In today's generation, frequent spelling mistakes in Tamil necessitate advanced tools for accurate linguistic analysis and correction. This study proposes a Tamil lexical analysis framework grounded in the linguistic principles of Tolkappiyam, focusing on MeiMayakkam, a rule governing consonant-vowel harmony. Tolkappiyar's twelve rules on word formation have been reclassified into nine categories and further refined into nineteen rules based on consonantal sequences. By applying these rules, we evaluate the validity of Tamil word formations, demonstrated with examples like 'பக்கம்' ('pəkkəm')
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Barbosa, Mayara Lustosa de Oliveira, and Diana Marín-Suelves. "Content and Sentiment Analysis of Autobiographical Narratives of Experienced and Well-Evaluated Teachers in Spain." Education Sciences 14, no. 6 (2024): 642. http://dx.doi.org/10.3390/educsci14060642.

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Analysing the content and feelings of teachers’ autobiographical narratives provides relevant information about teaching processes, challenges in professional development, performance, and identity. In this work, we analysed the narratives of five well-evaluated Spanish teachers with more than 10 years of experience. We used Iramuteq, RStudio, and TextRazor to describe the main themes, analyse the emotions, and check whether evidence-based teaching practices were mentioned. The results showed that emotional aspects were central, with positive feelings dominating. In the content analysis, theme
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Akkala, Abhilasha, and Avinash Seekoli. "TEXTRACTOR-EXTRACT OPTIMAL CHARACTER IN AN IMAGE FILE." Far East Journal of Electronics and Communications 21, no. 1-2 (2019): 21–26. http://dx.doi.org/10.17654/ec021120021.

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Meystre, Stéphane M., Julien Thibault, Shuying Shen, John F. Hurdle, and Brett R. South. "Textractor: a hybrid system for medications and reason for their prescription extraction from clinical text documents." Journal of the American Medical Informatics Association 17, no. 5 (2010): 559–62. http://dx.doi.org/10.1136/jamia.2010.004028.

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СЫМАГУЛОВ, А., Р. МУХАМЕДИЕВ, ЯН И. КУЧИН, С. АБДУЛЛАЕВА та Ф. Н. АБДОЛДИНА. "ОБЛАЧНЫЕ СЕРВИСЫ ДЛЯ ОБРАБОТКИ ТЕКСТОВ НА ЕСТЕСТВЕННОМ ЯЗЫКЕ". Международный научный журнал "Современные информационные технологии и ИТ-образование" 14, № 4 (2018). https://doi.org/10.25559/sitito.14.201804.872-880.

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В работе приведены результаты экспериментов, проведенных с целью сравнительного анализа качества работы существующих облачных сервисов по обработке текстов на русском языке. В статье приводится обзор 10-ти облачных сервисов: TextRazor, RosetteTextAnalytics, EurekaEngine, CloudNaturalLanguage, Texterra, Pullenti, NER-ru, UDPipe, AOT, DeepPavlov. Количественные исследования качества работы выполнены для 6-ти из них. В процессе оценки сервисов анализировалось выполнение таких функций, как частеречная разметка, анализ тональности, распознавание именованных сущностей и категоризация текстов. Для ср
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Ronald, Brisebois Alain Abran Apollinaire Nadembega Philippe N'techobo. "A SEMANTIC METADATA ENRICHMENT SOFTWARE ECOSYSTEM BASED ON TOPIC METADATA ENRICHMENTS." July 29, 2020. https://doi.org/10.5281/zenodo.3965654.

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ABSTRACT As existing computer search engines struggle to understand the meaning of natural language, semantically enriched metadata may improve interest-based search engine capabilities and user satisfaction. This paper presents an enhanced version of the ecosystem focusing on semantic topic metadata detection and enrichments. It is based on a previous paper, a semantic metadata enrichment software ecosystem (SMESE). Through text analysis approaches for topic detection and metadata enrichments this paper propose an algorithm to enhance search engines capabilities and consequently help users fi
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Dissertations / Theses on the topic "TextRazor"

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Månsson, Ingrid Titti. "Från textrader till roman." Thesis, Linnéuniversitetet, Institutionen för språk och litteratur, SOL, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-20282.

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Book chapters on the topic "TextRazor"

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Magalhães, António, and João Paulo Magalhães. "TExtractor: An OSINT Tool to Extract and Analyse Audio/Video Content." In Innovation, Engineering and Entrepreneurship. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91334-6_1.

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Ittoo, Ashwin, Laura Maruster, Hans Wortmann, and Gosse Bouma. "Textractor: A Framework for Extracting Relevant Domain Concepts from Irregular Corporate Textual Datasets." In Business Information Systems. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12814-1_7.

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Meystre Stéphane M., Thibault Julien, Shen Shuying, Hurdle John F., and South Brett R. "Automatically Detecting Medications and the Reason for their Prescription in Clinical Narrative Text Documents." In Studies in Health Technology and Informatics. IOS Press, 2010. https://doi.org/10.3233/978-1-60750-588-4-944.

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An important proportion of the information about the medica tions a patient is taking is mentioned only in narrative text in the electronic health record. Automated information extraction can make this information accessible for decision support, research, or any other automated processing. In the context of the “i2b2 medication extraction challenge,” we have developed a new NLP application called Textractor to automatically extract medications and details about them (e.g., dosage, frequency, reason for their prescription). This application and its evaluation with part of t
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Conference papers on the topic "TextRazor"

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Valderrábanos, Antonio S., Alexander Belskis, and Luis Iraola. "TExtractor." In the second international conference. Association for Computational Linguistics, 2002. http://dx.doi.org/10.3115/1289189.1289237.

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