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Relevant bibliographies by topics / Unstructured text data

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Academic literature on the topic 'Unstructured text data'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Unstructured text data"

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Nasibah, Husna Mohd Kadir, and Aliman Sharifah. "Text analysis on health product reviews using r approach." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 18, no. 3 (2020): 1303–10. https://doi.org/10.11591/ijeecs.v18.i3.pp1303-1310.

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In the social media, product reviews contain of text, emoticon, numbers and symbols that hard to identify the text summarization. Text analytics is one of the key techniques in exploring the unstructured data. The purpose of this study is solving the unstructured data by sort and summarizes the review data through a Web-Based Text Analytics using R approach. According to the comparative table between studies in Natural Language Processing (NLP) features, it was observed that Web-Based Text Analytics using R approach can analyze the unstructured data by using the data processing package in R. I

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AL-Mashhadany, Abeer K., Dalal N. Hamood, Ahmed T. Sadiq Al-Obaidi, and Waleed K. Al-Mashhadany. "Extracting numerical data from unstructured Arabic texts(ENAT)." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 3 (2021): 1759–70. https://doi.org/10.11591/ijeecs.v21.i3.pp1759-1770.

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Unstructured data becomes challenges because in recent years have observed the ability to gather a massive amount of data from annotated documents. This paper interested with Arabic unstructured text analysis. Manipulating unstructured text and converting it into a form understandable by computer is a high-level aim. An important step to achieve this aim is to understand numerical phrases. This paper aims to extract numerical data from Arabic unstructured text in general. This work attempts to recognize numerical characters phrases, analyze them and then convert them into integer values. The i

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Shastri, Shankarayya, Teligi Math Veeragangadhara Swamy, and Siddalingappa Patil Nagaraja. "Sensing complicated meanings from unstructured data: a novel hybrid approach." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 1 (2024): 711–20. https://doi.org/10.11591/ijece.v14i1.pp711-720.

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The majority of data on computers nowadays is in the form of unstructured data and unstructured text. The inherent ambiguity of natural language makes it incredibly difficult but also highly profitable to find hidden information or comprehend complex semantics in unstructured text. In this paper, we present the combination of natural language processing (NLP) and convolution neural network (CNN) hybrid architecture called automated analysis of unstructured text using machine learning (AAUT-ML) for the detection of complex semantics from unstructured data that enables different users to make un

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Oh, Tae-Jin, and Anthony. "New and Fast Emerging Advance Structure of Text Mining from Unstructured Data." Bonfring International Journal of Industrial Engineering and Management Science 7, no. 2 (2017): 13–16. http://dx.doi.org/10.9756/bijiems.8325.

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Muhammad, Aoun. "Comparative Analysis of Text Mining Techniques for News Article Summarization." LC International Journal of STEM (ISSN: 2708-7123) 4, no. 1 (2023): 52–63. https://doi.org/10.5281/zenodo.7893329.

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Text mining research paper is a scientific study that focuses on the development and application of text mining techniques for extracting valuable information from unstructured textual data. The paper discusses the challenges of working with unstructured data and the need for advanced text mining techniques to address these challenges. The paper outlines the various steps involved in the text mining process, such as data preprocessing, text representation, and feature selection. It discusses the importance of selecting appropriate algorithms for different types of text mining tasks, including

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B, L. Shilpa, and R. Shambhavi B. "Structuring of Unstructured Data from Heterogeneous Sources." Indian Journal of Science and Technology 15, no. 41 (2022): 2188–93. https://doi.org/10.17485/IJST/v15i41.1566.

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Abstract <strong>Objectives:</strong> To develop a new data gathering processing under Big Data Perspectives. To convert unstructured text data into structured format by not missing out any text data available.<strong> Methods:</strong> The unstructured data is preprocessed using modified stemming and tokenization. From the stemming output, the proposed Term Frequency-Inverse Document Frequency (TF-IDF) and N-gram features are derived. Unstructured data is considered from multiple sources like twitter, consumer complaints and news blog. <strong>Findings:</strong> The p

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Zhai, Yanrui, Xiran Zhou, and Honghao Li. "Model and Data Integrated Transfer Learning for Unstructured Map Text Detection." ISPRS International Journal of Geo-Information 12, no. 3 (2023): 106. http://dx.doi.org/10.3390/ijgi12030106.

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The emergence of the third information wave makes extensive maps available to be generated by volunteered ways, never specially designed and generated by professional institutes alone. These large-scale images-based volunteered maps created by the public provide plentiful geographical information regarding a place while posing a challenge for recognizing the unstructured text in these maps for previous approaches to standard map text detection. Map text or map annotations denote the critical element of map content. To achieve the detection of unstructured map text, this paper proposed an integ

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Shastri, Shankarayya, Veeragangadhara Swamy Teligi Math, and Patil Nagaraja Siddalingappa. "Sensing complicated meanings from unstructured data: a novel hybrid approach." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 1 (2024): 711. http://dx.doi.org/10.11591/ijece.v14i1.pp711-720.

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The majority of data on computers nowadays is in the form of unstructured data and unstructured text. The inherent ambiguity of natural language makes it incredibly difficult but also highly profitable to find hidden information or comprehend complex semantics in unstructured text. In this paper, we present the combination of natural language processing (NLP) and convolution neural network (CNN) hybrid architecture called automated analysis of unstructured text using machine learning (AAUT-ML) for the detection of complex semantics from unstructured data that enables different users to make un

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Ali, Hameed Yassir, A. Mohammed Ali, Abdul-Jabbar Alkhazraji Adel, Emad Hameed Mustafa, Saad Talib Mohammed, and Faeq Ali Mohanad. "Sentimental classification analysis of polarity multi-view textual data using data mining techniques." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 5526–34. https://doi.org/10.11591/ijece.v10i5.pp5526-5534.

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The data and information available in most community environments is complex in nature. Sentimental data resources may possibly consist of textual data collected from multiple information sources with different representations and usually handled by different analytical models. These types of data resource characteristics can form multi-view polarity textual data. However, knowledge creation from this type of sentimental textual data requires considerable analytical efforts and capabilities. In particular, data mining practices can provide exceptional results in handling textual data formats.

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Côté, Jean, John H. Salmela, Abderrahim Baria, and Storm J. Russell. "Organizing and Interpreting Unstructured Qualitative Data." Sport Psychologist 7, no. 2 (1993): 127–37. http://dx.doi.org/10.1123/tsp.7.2.127.

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In the last several years there has been an increase in the amount of qualitative research using in-depth interviews and comprehensive content analyses in sport psychology. However, no explicit method has been provided to deal with the large amount of unstructured data. This article provides common guidelines for organizing and interpreting unstructured data. Two main operations are suggested and discussed: first, coding meaningful text segments, or creating tags, and second, regrouping similar text segments, or creating categories. Furthermore, software programs for the microcomputer are pres

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Dissertations / Theses on the topic "Unstructured text data"

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Popescu, Ana-Maria. "Information extraction from unstructured web text /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/6935.

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Olsson, Jennny. "Using Elasticsearch for full-text searches on unstructured data." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-395654.

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In order to perform effective searches on large amounts of data it is not viable to simply scan through all of said data. A well established solution for this problem is to generate an index based on the data. This report compares different libraries for establishing such an index and a prototype was implemented to enable full-text searches on an existing database. The libraries considered include Elasticsearch, Solr, Sphinx and Xapian. The database in question consists of audit logs generated by a software for online management of financial trade. The author implemented a prototype using the

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Bojduj, Brett N. "Extraction of Causal-Association Networks from Unstructured Text Data." DigitalCommons@CalPoly, 2009. https://digitalcommons.calpoly.edu/theses/138.

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Causality is an expression of the interactions between variables in a system. Humans often explicitly express causal relations through natural language, so extracting these relations can provide insight into how a system functions. This thesis presents a system that uses a grammar parser to extract causes and effects from unstructured text through a simple, pre-deﬁned grammar pattern. By ﬁltering out non-causal sentences before the extraction process begins, the presented methodology is able to achieve a precision of 85.91% and a recall of 73.99%. The polarity of the extracted relations is

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Sequeira, José Francisco Rodrigues. "Automatic knowledge base construction from unstructured text." Master's thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/17910.

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Mestrado em Engenharia de Computadores e Telemática<br>Taking into account the overwhelming number of biomedical publications being produced, the effort required for a user to efficiently explore those publications in order to establish relationships between a wide range of concepts is staggering. This dissertation presents GRACE, a web-based platform that provides an advanced graphical exploration interface that allows users to traverse the biomedical domain in order to find explicit and latent associations between annotated biomedical concepts belonging to a variety of semantic types

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Vikholm, Oskar. "Dealing with unstructured data : A study about information quality and measurement." Thesis, Uppsala universitet, Institutionen för informatik och media, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-255214.

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Many organizations have realized that the growing amount of unstructured text may contain information that can be used for different purposes, such as making decisions. Organizations can by using so-called text mining tools, extract information from text documents. For example within military and intelligence activities it is important to go through reports and look for entities such as names of people, events, and the relationships in-between them when criminal or other interesting activities are being investigated and mapped. This study explores how information quality can be measured and wh

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Coetsee, Dirko. "Conditional random fields for noisy text normalisation." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/96064.

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Thesis (MScEng) -- Stellenbosch University, 2014.<br>ENGLISH ABSTRACT: The increasing popularity of microblogging services such as Twitter means that more and more unstructured data is available for analysis. The informal language usage in these media presents a problem for traditional text mining and natural language processing tools. We develop a pre-processor to normalise this noisy text so that useful information can be extracted with standard tools. A system consisting of a tokeniser, out-of-vocabulary token identifier, correct candidate generator, and N-gram language model is propo

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Hill, Geoffrey. "Sensemaking in Big Data: Conceptual and Empirical Approaches to Actionable Knowledge Generation from Unstructured Text Streams." Kent State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=kent1433597354.

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Alshaer, Mohammad. "An Efficient Framework for Processing and Analyzing Unstructured Text to Discover Delivery Delay and Optimization of Route Planning in Realtime." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSE1105/document.

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L'Internet des objets, ou IdO (en anglais Internet of Things, ou IoT) conduit à un changement de paradigme du secteur de la logistique. L'avènement de l'IoT a modifié l'écosystème de la gestion des services logistiques. Les fournisseurs de services logistiques utilisent aujourd'hui des technologies de capteurs telles que le GPS ou la télémétrie pour collecter des données en temps réel pendant la livraison. La collecte en temps réel des données permet aux fournisseurs de services de suivre et de gérer efficacement leur processus d'expédition. Le principal avantage de la collecte de données en t

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Xiong, Hui. "Combining Subject Expert Experimental Data with Standard Data in Bayesian Mixture Modeling." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1312214048.

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Minhas, Saliha Z. "A corpus driven computational intelligence framework for deception detection in financial text." Thesis, University of Stirling, 2016. http://hdl.handle.net/1893/25345.

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Financial fraud rampages onwards seemingly uncontained. The annual cost of fraud in the UK is estimated to be as high as £193bn a year [1] . From a data science perspective and hitherto less explored this thesis demonstrates how the use of linguistic features to drive data mining algorithms can aid in unravelling fraud. To this end, the spotlight is turned on Financial Statement Fraud (FSF), known to be the costliest type of fraud [2]. A new corpus of 6.3 million words is composed of102 annual reports/10-K (narrative sections) from firms formally indicted for FSF juxtaposed with 306 non-fraud

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Books on the topic "Unstructured text data"

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Feldman, Ronen. The text mining handbook: Advanced approaches in analyzing unstructured data. Cambridge University Press, 2007.

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1965-, Sanger James, ed. The text mining handbook: Advanced approaches in analyzing unstructured data. Cambridge University Press, 2006.

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Struhl, Steven. Practical Text Analytics: Interpreting Text and Unstructured Data for Business Intelligence. Kogan Page, 2016.

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Struhl, Steven. Practical Text Analytics: Interpreting Text and Unstructured Data for Business Intelligence. Kogan Page, Limited, 2015.

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Practical Text Analytics: Interpreting Text and Unstructured Data for Business Intelligence. Kogan Page, 2015.

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Feldman, Ronen, and James Sanger. Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press, 2002.

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Feldman, Ronen, and James Sanger. Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press, 2006.

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Feldman, Ronen, and James Sanger. Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press, 2009.

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Feldman, Ronen, and James Sanger. Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press, 2007.

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Indurkhya, Nitin, Tong Zhang, F. J. Damerau, and Sholom M. M. Weiss. Text Mining: Predictive Methods for Analyzing Unstructured Information. Springer, 2010.

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Book chapters on the topic "Unstructured text data"

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Yu, Chong Ho Alex. "Text Mining Structure the Unstructured." In Data Mining and Exploration. CRC Press, 2022. http://dx.doi.org/10.1201/9781003153658-11.

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Shang, Chao, Anand Panangadan, and Viktor K. Prasanna. "Event Extraction from Unstructured Text Data." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22849-5_38.

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Augenstein, Isabelle, Sebastian Padó, and Sebastian Rudolph. "LODifier: Generating Linked Data from Unstructured Text." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30284-8_21.

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Rajman, Martin, and Romaric Besançon. "Text Mining - Knowledge extraction from unstructured textual data." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72253-0_64.

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Ferraro, Antonino, Antonio Galli, Valerio La Gatta, Mario Minocchi, Vincenzo Moscato, and Marco Postiglione. "Few Shot NER on Augmented Unstructured Text from Cardiology Records." In Advances in Internet, Data & Web Technologies. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53555-0_1.

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Bagheri, Ayoub, Anastasia Giachanou, Pablo Mosteiro, and Suzan Verberne. "Natural Language Processing and Text Mining (Turning Unstructured Data into Structured)." In Clinical Applications of Artificial Intelligence in Real-World Data. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-36678-9_5.

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Repke, Tim, and Ralf Krestel. "Extraction and Representation of Financial Entities from Text." In Data Science for Economics and Finance. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66891-4_11.

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AbstractIn our modern society, almost all events, processes, and decisions in a corporation are documented by internal written communication, legal filings, or business and financial news. The valuable knowledge in such collections is not directly accessible by computers as they mostly consist of unstructured text. This chapter provides an overview of corpora commonly used in research and highlights related work and state-of-the-art approaches to extract and represent financial entities and relations.The second part of this chapter considers applications based on knowledge graphs of automatica

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Ji, Yanqing, Yun Tian, Fangyang Shen, and John Tran. "Mining Associations Between Two Categories Using Unstructured Text Data in Cloud." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77028-4_70.

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Eybers, Sunet, and Helgard Kahts. "In Search of Insight from Unstructured Text Data: Towards an Identification of Text Mining Techniques." In Lecture Notes in Networks and Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93677-8_52.

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Ye, Rui, Rui Ge, Fengting Yuchi, Jingyi Chai, Yanfeng Wang, and Siheng Chen. "Leveraging Unstructured Text Data for Federated Instruction Tuning of Large Language Models." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-82240-7_9.

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Conference papers on the topic "Unstructured text data"

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Singh, Kulvinder, Deepanshu Jindal, and Ankit Panigrahi. "Automated Data Extraction from Unstructured Text Using Machine Learning Algorithms." In 2024 International Conference on Advances in Computing, Communication and Materials (ICACCM). IEEE, 2024. https://doi.org/10.1109/icaccm61117.2024.11059107.

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Pépin, Ian, Furkan Alaca, and Farhana Zulkernine. "Privacy-Preserving Multi-Party Keyword-Based Classification of Unstructured Text Data." In 2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT). IEEE, 2024. http://dx.doi.org/10.1109/dcoss-iot61029.2024.00037.

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S, Harisha, and Subrhamanya Bhat. "Comparative Analysis on Classification of Unstructured Text data and Specific Summarization." In 2024 Second International Conference on Advances in Information Technology (ICAIT). IEEE, 2024. http://dx.doi.org/10.1109/icait61638.2024.10690829.

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Zhang, Tianlun, Guofang Zhang, Guoqiang Yang, Chen Cui, Shaoshi Shen, and Ye Xia. "A Natural Language Processing-based Approach to Bridge Structure Information Extraction." In IABSE Symposium, Tokyo 2025: Environmentally Friendly Technologies and Structures: Focusing on Sustainable Approaches. International Association for Bridge and Structural Engineering (IABSE), 2025. https://doi.org/10.2749/tokyo.2025.1539.

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<p>As transportation infrastructure continues to develop and age, ensuring the safety and stability of bridges is a growing concern. Bridge inspection reports contain a large amount of detailed information about the structural attributes and condition ratings of bridges. However, these reports are usually presented in an unstructured textual format, making manually extracting valuable information from them time-consuming and error-prone. Thus, efficient and accurate extraction of inspection data has become the key to improving bridge management. In this paper, an information extraction m

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Jain, Alpa, AnHai Doan, and Luis Gravano. "SQL Queries Over Unstructured Text Databases." In 2007 IEEE 23rd International Conference on Data Engineering. IEEE, 2007. http://dx.doi.org/10.1109/icde.2007.368986.

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Wang, Pei, Chuan Xiao, Jianbin Qin, Wei Wang, Xiaoyang Zhang, and Yoshiharu Ishikawa. "Local Similarity Search for Unstructured Text." In SIGMOD/PODS'16: International Conference on Management of Data. ACM, 2016. http://dx.doi.org/10.1145/2882903.2915211.

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Kraft, Volker. "Analyzing unstructured data: text analytics in JMP." In Teaching Statistics in a Data Rich World. International Association for Statistical Education, 2017. http://dx.doi.org/10.52041/srap.17204.

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As much as 80% of all data is unstructured but still has exploitable information available. For example, unstructured text data could result from comment fields in surveys or incident reports. You want to explore this unstructured text to better understand the information that it contains. Text Mining, based on a transformation of free text into numerical summaries, can pave the way for new findings. This example of the new text mining feature in JMP starts with a multi-step text preparation using techniques like stemming and tokenizing. This data curation is pivotal for the subsequent analysi

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Thakur, Praveen Singh, Mahipal Jadeja, and Satyendra Singh Chouhan. "Text Augmentation based Imbalance Learning for Unstructured Text Data." In 2022 IEEE 4th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA). IEEE, 2022. http://dx.doi.org/10.1109/icccmla56841.2022.9989047.

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Lee, Seong-Ho, Hye-Yeon Yu, and Yun-Gyung Cheong. "Analyzing Movie Scripts as Unstructured Text." In 2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService). IEEE, 2017. http://dx.doi.org/10.1109/bigdataservice.2017.43.

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Kalbhore, Roshni, and Pravin Malviya. "Text Mining Approach for Unstructured Data Using SVM." In International Conference on Science and Engineering for Sustainable Development. Infogain Publication, 2017. http://dx.doi.org/10.24001/ijaems.icsesd2017.60.

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Reports on the topic "Unstructured text data"

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McDonnell, Diarmuid. Text Analysis Using Python. Instats Inc., 2025. https://doi.org/10.61700/4z1kbrydccbkh1944.

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This seminar provides a comprehensive introduction to text analysis using Python, equipping researchers with essential skills to process and analyze unstructured textual data effectively. Participants will gain hands-on experience with Python's robust libraries, enabling them to apply both supervised and unsupervised text analysis techniques to real-world data in fields such as Sociology, Political Science, and Social Policy.

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Lagria, Raymond Freth, and Brenda Quismorio. Analyzing Trends in APEC Using Data Analytics. Philippine Institute for Development Studies, 2022. https://doi.org/10.62986/dp2022.04.

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This paper shows that advanced analytics and traditional statistical techniques on available unstructured and structured data can be utilized to understand the themes put forward in APEC's yearly meetings and how member economies have supported these topics through the conduct of APEC projects. The application of text mining algorithms, such as topic modeling on the proceedings of APEC-level annual meetings, namely, APEC Economic Leaders' Meeting (AELM), APEC Annual Ministerial Meeting (AMM), and Senior Officials' Meeting (SOM), generated themes from the text which insight have been discussed.

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Marra de Artiñano, Ignacio, Franco Riottini Depetris, and Christian Volpe Martincus. Automatic Product Classification in International Trade: Machine Learning and Large Language Models. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0005012.

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Accurately classifying products is essential in international trade. Virtually all countries categorize products into tariff lines using the Harmonized System (HS) nomenclature for both statistical and duty collection purposes. In this paper, we apply and assess several different algorithms to automatically classify products based on text descriptions. To do so, we use agricultural product descriptions from several public agencies, including customs authorities and the United States Department of Agriculture (USDA). We find that while traditional machine learning (ML) models tend to perform we

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Chapman, Ray, Phu Luong, Sung-Chan Kim, and Earl Hayter. Development of three-dimensional wetting and drying algorithm for the Geophysical Scale Transport Multi-Block Hydrodynamic Sediment and Water Quality Transport Modeling System (GSMB). Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41085.

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The Environmental Laboratory (EL) and the Coastal and Hydraulics Laboratory (CHL) have jointly completed a number of large-scale hydrodynamic, sediment and water quality transport studies. EL and CHL have successfully executed these studies utilizing the Geophysical Scale Transport Modeling System (GSMB). The model framework of GSMB is composed of multiple process models as shown in Figure 1. Figure 1 shows that the United States Army Corps of Engineers (USACE) accepted wave, hydrodynamic, sediment and water quality transport models are directly and indirectly linked within the GSMB framework.

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