Journal articles: 'Data-to-text'

1

Yang, Sen, and Yang Liu. "Data-to-text Generation via Planning." Journal of Physics: Conference Series 1827, no. 1 (March 1, 2021): 012190. http://dx.doi.org/10.1088/1742-6596/1827/1/012190.

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Puduppully, Ratish, and Mirella Lapata. "Data-to-text Generation with Macro Planning." Transactions of the Association for Computational Linguistics 9 (2021): 510–27. http://dx.doi.org/10.1162/tacl_a_00381.

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Abstract Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or variants thereof. These models generate text that is fluent (but often imprecise) and perform quite poorly at selecting appropriate content and ordering it coherently. To overcome some of these issues, we propose a neural model with a macro planning stage followed by a generation stage reminiscent of traditional methods which embrace separate modules for planning and surface realization. Macro plans represent high level organization of important content such as entities, events, and their interactions; they are learned from data and given as input to the generator. Extensive experiments on two data-to-text benchmarks (RotoWire and MLB) show that our approach outperforms competitive baselines in terms of automatic and human evaluation.

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Zhang, Dell, Jiahao Yuan, Xiaoling Wang, and Adam Foster. "Probabilistic Verb Selection for Data-to-Text Generation." Transactions of the Association for Computational Linguistics 6 (December 2018): 511–27. http://dx.doi.org/10.1162/tacl_a_00038.

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In data-to-text Natural Language Generation (NLG) systems, computers need to find the right words to describe phenomena seen in the data. This paper focuses on the problem of choosing appropriate verbs to express the direction and magnitude of a percentage change (e.g., in stock prices). Rather than simply using the same verbs again and again, we present a principled data-driven approach to this problem based on Shannon’s noisy-channel model so as to bring variation and naturalness into the generated text. Our experiments on three large-scale real-world news corpora demonstrate that the proposed probabilistic model can be learned to accurately imitate human authors’ pattern of usage around verbs, outperforming the state-of-the-art method significantly.

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Rüdiger, Matthias, David Antons, and Torsten Oliver Salge. "From Text to Data: On The Role and Effect of Text Pre-Processing in Text Mining Research." Academy of Management Proceedings 2017, no. 1 (August 2017): 16353. http://dx.doi.org/10.5465/ambpp.2017.16353abstract.

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Iso, Hayate, Yui Uehara, Tatsuya Ishigaki, Hiroshi Noji, Eiji Aramaki, Ichiro Kobayashi, Yusuke Miyao, Naoaki Okazaki, and Hiroya Takamura. "Learning to Select, Track, and Generate for Data-to-Text." Journal of Natural Language Processing 27, no. 3 (September 15, 2020): 599–626. http://dx.doi.org/10.5715/jnlp.27.599.

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Riza, Lala Septem, Muhammad Ridwan, Enjun Junaeti, and Khyrina Airin Fariza Abu Samah. "Development of data-to-text (D2T) on generic data using fuzzy sets." International Journal of Advanced Technology and Engineering Exploration 8, no. 75 (February 28, 2021): 382–90. http://dx.doi.org/10.19101/ijatee.2020.762134.

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Puduppully, Ratish, Li Dong, and Mirella Lapata. "Data-to-Text Generation with Content Selection and Planning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6908–15. http://dx.doi.org/10.1609/aaai.v33i01.33016908.

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Recent advances in data-to-text generation have led to the use of large-scale datasets and neural network models which are trained end-to-end, without explicitly modeling what to say and in what order. In this work, we present a neural network architecture which incorporates content selection and planning without sacrificing end-to-end training. We decompose the generation task into two stages. Given a corpus of data records (paired with descriptive documents), we first generate a content plan highlighting which information should be mentioned and in which order and then generate the document while taking the content plan into account. Automatic and human-based evaluation experiments show that our model1 outperforms strong baselines improving the state-of-the-art on the recently released RotoWIRE dataset.

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Guru, D. S., K. Swarnalatha, N. Vinay Kumar, and Basavaraj S. Anami. "Effective Technique to Reduce the Dimension of Text Data." International Journal of Computer Vision and Image Processing 10, no. 1 (January 2020): 67–85. http://dx.doi.org/10.4018/ijcvip.2020010104.

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In this article, features are selected using feature clustering and ranking of features for imbalanced text data. Initially the text documents are represented in lower dimension using the term class relevance (TCR) method. The class wise clustering is recommended to balance the documents in each class. Subsequently, the clusters are treated as classes and the documents of each cluster are represented in the lower dimensional form using the TCR again. The features are clustered and for each feature cluster the cluster representative is selected and these representatives are used as selected features of the documents. Hence, this proposed model reduces the dimension to a smaller number of features. For selecting the cluster representative, four feature evaluation methods are used and classification is done by using SVM classifier. The performance of the method is compared with the global feature ranking method. The experiment is conducted on two benchmark datasets the Reuters-21578 and the TDT2 dataset. The experimental results show that this method performs well when compared to the other existing works.

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Al Rababaa, Mamoun Suleiman, and Essam Said Hanandeh. "The Automated VSMs to Categorize Arabic Text Data Sets." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 1 (March 31, 2014): 4074–81. http://dx.doi.org/10.24297/ijct.v13i1.2925.

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Text Categorization is one of the most important tasks in information retrieval and data mining. This paper aims at investigating different variations of vector space models (VSMs) using KNN algorithm. we used 242 Arabic abstract documents that were used by (Hmeidi & Kanaan, 1997). The bases of our comparison are the most popular text evaluation measures; we use Recall measure, Precision measure, and F1 measure. The Experimental results against the Saudi data sets reveal that Cosine outperformed over of the Dice and Jaccard coefficients.

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Gkatzia, Dimitra, Oliver Lemon, and Verena Rieser. "Data-to-Text Generation Improves Decision-Making Under Uncertainty." IEEE Computational Intelligence Magazine 12, no. 3 (August 2017): 10–17. http://dx.doi.org/10.1109/mci.2017.2708998.

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Gogo, Ngor, Matthias Daniel, and Alabo Gift. "Giving Structure to Unstructured Text Data by Employing Classification." International Journal of Computer Trends and Technology 69, no. 2 (February 25, 2021): 22–28. http://dx.doi.org/10.14445/22312803/ijctt-v69i2p104.

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12

Che, Aolin, Yalin Liu, Hong Xiao, Hao Wang, Ke Zhang, and Hong-Ning Dai. "Augmented Data Selector to Initiate Text-Based CAPTCHA Attack." Security and Communication Networks 2021 (June 15, 2021): 1–9. http://dx.doi.org/10.1155/2021/9930608.

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In the past decades, due to the low design cost and easy maintenance, text-based CAPTCHAs have been extensively used in constructing security mechanisms for user authentications. With the recent advances in machine/deep learning in recognizing CAPTCHA images, growing attack methods are presented to break text-based CAPTCHAs. These machine learning/deep learning-based attacks often rely on training models on massive volumes of training data. The poorly constructed CAPTCHA data also leads to low accuracy of attacks. To investigate this issue, we propose a simple, generic, and effective preprocessing approach to filter and enhance the original CAPTCHA data set so as to improve the accuracy of the previous attack methods. In particular, the proposed preprocessing approach consists of a data selector and a data augmentor. The data selector can automatically filter out a training data set with training significance. Meanwhile, the data augmentor uses four different image noises to generate different CAPTCHA images. The well-constructed CAPTCHA data set can better train deep learning models to further improve the accuracy rate. Extensive experiments demonstrate that the accuracy rates of five commonly used attack methods after combining our preprocessing approach are 2.62% to 8.31% higher than those without preprocessing approach. Moreover, we also discuss potential research directions for future work.

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13

Bui, Duy Duc An, Guilherme Del Fiol, John F. Hurdle, and Siddhartha Jonnalagadda. "Extractive text summarization system to aid data extraction from full text in systematic review development." Journal of Biomedical Informatics 64 (December 2016): 265–72. http://dx.doi.org/10.1016/j.jbi.2016.10.014.

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14

Rodzvilla, John. "Deep Text: Using Text Analytics to Conquer Information Overload, Get Real Value From Social Media, and Add Big(ger) Text to Big Data." Journal of Web Librarianship 11, no. 2 (April 3, 2017): 148–49. http://dx.doi.org/10.1080/19322909.2017.1302273.

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15

Streuber, Sonja. "Deep Text: Using Text Analytics To Conquer Information Overload, Get Real Value From Social Media, and Add Big(Ger) text to Big Data." Public Services Quarterly 13, no. 3 (July 3, 2017): 179–81. http://dx.doi.org/10.1080/15228959.2017.1370065.

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16

Bayat, Behrooz. "Deep Text: Using Text Analytics to Conquer Information Overload, Get Real Value from Social Media, and Add Big(ger) Text to Big Data." Electronic Library 35, no. 6 (November 6, 2017): 1269–70. http://dx.doi.org/10.1108/el-09-2017-0188.

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17

Raghupathi, Viju, Yilu Zhou, and Wullianallur Raghupathi. "Exploring Big Data Analytic Approaches to Cancer Blog Text Analysis." International Journal of Healthcare Information Systems and Informatics 14, no. 4 (October 2019): 1–20. http://dx.doi.org/10.4018/ijhisi.2019100101.

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In this article, the authors explore the potential of a big data analytics approach to unstructured text analytics of cancer blogs. The application is developed using Cloudera platform's Hadoop MapReduce framework. It uses several text analytics algorithms, including word count, word association, clustering, and classification, to identify and analyze the patterns and keywords in cancer blog postings. This article establishes an exploratory approach to involving big data analytics methods in developing text analytics applications for the analysis of cancer blogs. Additional insights are extracted through various means, including the development of categories or keywords contained in the blogs, the development of a taxonomy, and the examination of relationships among the categories. The application has the potential for generalizability and implementation with health content in other blogs and social media. It can provide insight and decision support for cancer management and facilitate efficient and relevant searches for information related to cancer.

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18

Lorenzana, A., M. Tyagi, Q. C. Wang, R. Chawla, and S. Nigam. "USING TEXT NOTES FROM CALL CENTER DATA TO PREDICT HOSPITALIZATION." Value in Health 19, no. 3 (May 2016): A87. http://dx.doi.org/10.1016/j.jval.2016.03.1801.

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19

Marchant-Shapiro, Andrew. "Complex Data, Simple Tools: An Introduction to Text Retrieval Packages." IASSIST Quarterly 15, no. 1 (July 31, 1991): 36. http://dx.doi.org/10.29173/iq555.

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20

Tao, Cui, Dezhao Song, Deepak Sharma, and Christopher G. Chute. "Semantator: Semantic annotator for converting biomedical text to linked data." Journal of Biomedical Informatics 46, no. 5 (October 2013): 882–93. http://dx.doi.org/10.1016/j.jbi.2013.07.003.

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21

Jang, Jungsun, Hyungjong Noh, Yeonsoo Lee, Soo-Min Pantel, and Haechang Rim. "Narrative context-based data-to-text generation for ambient intelligence." Journal of Ambient Intelligence and Humanized Computing 11, no. 4 (January 17, 2019): 1421–29. http://dx.doi.org/10.1007/s12652-019-01176-7.

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22

Buts, Jan, and Henry Jones. "From text to data mediality in corpus-based translation studies." MonTI. Monografías de Traducción e Interpretación, no. 13 (2021): 301–29. http://dx.doi.org/10.6035/monti.2021.13.10.

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23

Maria Jones, G., S. Godfrey Winster, and P. Valarmathie. "Integrated Approach to Detect Cyberbullying Text: Mobile Device Forensics Data." Computer Systems Science and Engineering 40, no. 3 (2022): 963–78. http://dx.doi.org/10.32604/csse.2022.019483.

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24

Fortino, Andres, Roy Lowrance, Qitong Zhong, and WeiChieh Huang. "RightJob: Application of Text Data Mining to Curriculum Selection and Development." Academy of Management Proceedings 2019, no. 1 (August 1, 2019): 10848. http://dx.doi.org/10.5465/ambpp.2019.10848abstract.

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25

DARR, FRANK. "STUDENT RESPONSES TO FULL-TEXT DATA RESEARCH IN BUSINESS LAW INSTRUCTION." Journal of Legal Studies Education 11, no. 2 (June 1993): 253–72. http://dx.doi.org/10.1111/j.1744-1722.1993.tb00109.x.

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26

WANG, Zhi-Ming. "Text-To-Visual Speech in Chinese Based on Data-Driven Approach." Journal of Software 16, no. 6 (2005): 1054. http://dx.doi.org/10.1360/jos161054.

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27

Negri, M., G. Pelagatti, and L. Sbattella. "Adapting text to data in documents through a natural language processor." Information Systems 16, no. 1 (January 1991): 35–47. http://dx.doi.org/10.1016/0306-4379(91)90048-e.

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28

Peng, Yonghong, and Xuegong Zhang. "Integrative data mining in systems biology: from text to network mining." Artificial Intelligence in Medicine 41, no. 2 (October 2007): 83–86. http://dx.doi.org/10.1016/j.artmed.2007.08.001.

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29

Takama, Yasufumi, Yuna Tanaka, Yoshiyuki Mori, and Hiroki Shibata. "Treemap-Based Cluster Visualization and its Application to Text Data Analysis." Journal of Advanced Computational Intelligence and Intelligent Informatics 25, no. 4 (July 20, 2021): 498–507. http://dx.doi.org/10.20965/jaciii.2021.p0498.

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This paper proposes Treemap-based visualization for supporting cluster analysis of multi-dimensional data. It is important to grasp data distribution in a target dataset for such tasks as machine learning and cluster analysis. When dealing with multi-dimensional data such as statistical data and document datasets, dimensionality reduction algorithms are usually applied to project original data to lower-dimensional space. However, dimensionality reduction tends to lose the characteristics of data in the original space. In particular, the border between different data groups could not be represented correctly in lower-dimensional space. To overcome this problem, the proposed visualization method applies Fuzzy c-Means to target data and visualizes the result on the basis of the highest and the second-highest membership values with Treemap. Visualizing the information about not only the closest clusters but also the second closest ones is expected to be useful for identifying objects around the border between different clusters, as well as for understanding the relationship between different clusters. A prototype interface is implemented, of which the effectiveness is investigated with a user experiment on a news articles dataset. As another kind of text data, a case study of applying it to a word embedding space is also shown.

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30

Uehara, Yui, and Tatsuya Ishigaki. "Commentary on “Learning with Contrastive Examples for Data-to-Text Generation”." Journal of Natural Language Processing 28, no. 2 (2021): 710–15. http://dx.doi.org/10.5715/jnlp.28.710.

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31

Arumugam, G., and T. Joshva Devadas. "Object Oriented Intelligent Multi-Agent System Data Cleaning Architecture to clean Preference based Text Data." International Journal of Computer Applications 9, no. 8 (November 10, 2010): 34–44. http://dx.doi.org/10.5120/1404-1894.

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32

Xiang, Guanbing, Xiaoyuan Ji, Jianxin Zhou, Yajun Yin, and Xu Shen. "A flexible method for converting non-relational text data to relational data for data acquisition of equipment operation." Procedia Manufacturing 37 (2019): 367–74. http://dx.doi.org/10.1016/j.promfg.2019.12.061.

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33

Saier, Tarek, and Michael Färber. "unarXive: a large scholarly data set with publications’ full-text, annotated in-text citations, and links to metadata." Scientometrics 125, no. 3 (March 2, 2020): 3085–108. http://dx.doi.org/10.1007/s11192-020-03382-z.

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AbstractIn recent years, scholarly data sets have been used for various purposes, such as paper recommendation, citation recommendation, citation context analysis, and citation context-based document summarization. The evaluation of approaches to such tasks and their applicability in real-world scenarios heavily depend on the used data set. However, existing scholarly data sets are limited in several regards. In this paper, we propose a new data set based on all publications from all scientific disciplines available on arXiv.org. Apart from providing the papers’ plain text, in-text citations were annotated via global identifiers. Furthermore, citing and cited publications were linked to the Microsoft Academic Graph, providing access to rich metadata. Our data set consists of over one million documents and 29.2 million citation contexts. The data set, which is made freely available for research purposes, not only can enhance the future evaluation of research paper-based and citation context-based approaches, but also serve as a basis for new ways to analyze in-text citations, as we show prototypically in this article.

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34

Simske, Steven, and Marie Vans. "Data Operations and an Application for Translating Russian Speech to French Text." Archiving Conference 2021, no. 1 (June 18, 2021): 102–6. http://dx.doi.org/10.2352/issn.2168-3204.2021.1.0.23.

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In 2006, the French government discretely asked for an assessment of the highest accuracy means available at the time to translate Russian speech into French text. One of us was working with the Grenoble HP site at the time, and so promptly assessed the possibilities using existing speech-to-text and translation software (Nuance and Speechworks). This article describes the surprisingly circuitous route to maximum accuracy (90.3%), and in so doing provides an unexpected insight into discerning the native language of software designed for speech-to-text and translation applications.

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35

Ross, Tyler R., June BlueSpruce, Erin Hertel, Woody Favinger, and Clarissa Hu. "Using SmartTools to Capture Structured Data From Text: The LINCC Case Study." Journal of Patient-Centered Research and Reviews 3, no. 3 (August 15, 2016): 224. http://dx.doi.org/10.17294/2330-0698.1397.

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36

Moseley, LG, and FA Murphy. "Applying lexical and semantic analysis to the exploration of free-text data." Nurse Researcher 4, no. 3 (April 1997): 46–68. http://dx.doi.org/10.7748/nr.4.3.46.s5.

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Moseley, LG, DM Mead, and FA Murphy. "Applying lexical and semantic analysis to the exploration of free-text data." Nurse Researcher 4, no. 3 (April 1997): 46–68. http://dx.doi.org/10.7748/nr1997.04.4.3.46.c5872.

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38

Riedel, Nico, Miriam Kip, and Evgeny Bobrov. "ODDPub – a Text-Mining Algorithm to Detect Data Sharing in Biomedical Publications." Data Science Journal 19, no. 1 (October 29, 2020): 42. http://dx.doi.org/10.5334/dsj-2020-042.

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39

Swathi, Gundala. "Secure data storage in cloud computing to avoiding some cipher text attack." Journal of Information and Optimization Sciences 39, no. 4 (April 9, 2018): 843–55. http://dx.doi.org/10.1080/02522667.2016.1231966.

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40

Edison, Hali, and Hector Carcel. "Text data analysis using Latent Dirichlet Allocation: an application to FOMC transcripts." Applied Economics Letters 28, no. 1 (March 1, 2020): 38–42. http://dx.doi.org/10.1080/13504851.2020.1730748.

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41

Caron, S., and B. Knuteson. "QUAERO@H1: an interface to high- $p_{\text{T}}$ HERA event data." European Physical Journal C 53, no. 1 (November 17, 2007): 167–75. http://dx.doi.org/10.1140/epjc/s10052-007-0468-3.

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42

Currie, Janet, Henrik Kleven, and Esmée Zwiers. "Technology and Big Data Are Changing Economics: Mining Text to Track Methods." AEA Papers and Proceedings 110 (May 1, 2020): 42–48. http://dx.doi.org/10.1257/pandp.20201058.

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The last 40 years have seen huge innovations in computing and in the availability of data. Data derived from millions of administrative records or by using (as we do) new methods of data generation such as text mining are now common. New data often requires new methods, which in turn can inspire new data collection. If history is any guide, some methods will stick and others will prove to be a flash in the pan. However, the larger trends toward demanding greater credibility and transparency from researchers in applied economics and a 'collage' approach to assembling evidence will likely continue.

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43

Al-Nofaie, Safia Meteb Awad, and Adnan Abdul-Aziz Gutub. "Utilizing pseudo-spaces to improve Arabic text steganography for multimedia data communications." Multimedia Tools and Applications 79, no. 1-2 (August 8, 2019): 19–67. http://dx.doi.org/10.1007/s11042-019-08025-x.

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44

Monkman, Graham G., Michel J. Kaiser, and Kieran Hyder. "Text and data mining of social media to map wildlife recreation activity." Biological Conservation 228 (December 2018): 89–99. http://dx.doi.org/10.1016/j.biocon.2018.10.010.

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45

Zhang, Zhu. "Mining relational data from text: From strictly supervised to weakly supervised learning." Information Systems 33, no. 3 (May 2008): 300–314. http://dx.doi.org/10.1016/j.is.2007.10.002.

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46

Wanner, Franz, Wolfgang Jentner, Tobias Schreck, Andreas Stoffel, Lyubka Sharalieva, and Daniel A. Keim. "Integrated visual analysis of patterns in time series and text data - Workflow and application to financial data analysis." Information Visualization 15, no. 1 (April 1, 2015): 75–90. http://dx.doi.org/10.1177/1473871615576925.

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In this article, we describe a workflow and tool that allows a flexible formation of hypotheses about text features and their combinations, which are significantly connected in time to quantitative phenomena observed in stock data. To support such an analysis, we combine the analysis steps of frequent quantitative and text-oriented data using an existing a priori method. First, based on heuristics, we extract interesting intervals and patterns in large time series data. The visual analysis supports the analyst in exploring parameter combinations and their results. The identified time series patterns are then input for the second analysis step, in which all identified intervals of interest are analyzed for frequent patterns co-occurring with financial news. An a priori method supports the discovery of such sequential temporal patterns. Then, various text features such as the degree of sentence nesting, noun phrase complexity, and the vocabulary richness, are extracted from the news items to obtain meta-patterns. Meta-patterns are defined by a specific combination of text features which significantly differ from the text features of the remaining news data. Our approach combines a portfolio of visualization and analysis techniques, including time, cluster, and sequence visualization and analysis functionality. We provide a case study and an evaluation on financial data where we identify important future work. The workflow could be generalized to other application domains such as data analysis of smart grids, cyber physical systems, or the security of critical infrastructure, where the data consist of a combination of quantitative and textual time series data.

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47

OKABE, Yasunori, and Yuji NAKANO. "The theory of $\text{KM}_{2}\text{O}$-Langevin equations and its applications to data analysis (I): Stationary analysis." Hokkaido Mathematical Journal 20, no. 1 (February 1991): 45–90. http://dx.doi.org/10.14492/hokmj/1381413801.

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48

Sadikin, Mujiono, Mohamad Ivan Fanany, and T. Basaruddin. "A New Data Representation Based on Training Data Characteristics to Extract Drug Name Entity in Medical Text." Computational Intelligence and Neuroscience 2016 (2016): 1–16. http://dx.doi.org/10.1155/2016/3483528.

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One essential task in information extraction from the medical corpus is drug name recognition. Compared with text sources come from other domains, the medical text mining poses more challenges, for example, more unstructured text, the fast growing of new terms addition, a wide range of name variation for the same drug, the lack of labeled dataset sources and external knowledge, and the multiple token representations for a single drug name. Although many approaches have been proposed to overwhelm the task, some problems remained with poor F-score performance (less than 0.75). This paper presents a new treatment in data representation techniques to overcome some of those challenges. We propose three data representation techniques based on the characteristics of word distribution and word similarities as a result of word embedding training. The first technique is evaluated with the standard NN model, that is, MLP. The second technique involves two deep network classifiers, that is, DBN and SAE. The third technique represents the sentence as a sequence that is evaluated with a recurrent NN model, that is, LSTM. In extracting the drug name entities, the third technique gives the best F-score performance compared to the state of the art, with its average F-score being 0.8645.

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49

Scott, Donia, Catalina Hallett, and Rachel Fettiplace. "Data-to-text summarisation of patient records: Using computer-generated summaries to access patient histories." Patient Education and Counseling 92, no. 2 (August 2013): 153–59. http://dx.doi.org/10.1016/j.pec.2013.04.019.

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50

OHYA, Kazushi, and Syun TSUCHIYA. "A proposal to use text-based data models for pre-processes of making databases and data preservation." Journal of Information Processing and Management 43, no. 2 (2000): 119–30. http://dx.doi.org/10.1241/johokanri.43.119.

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Journal articles on the topic 'Data-to-text'

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