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Showing 1 to 20 of 140 for “"Text Data"”.
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Topic models for short text data
… to suffer from sparsity when applied to short text data. The problem is caused by a reduced number of observations available for a reliable inference (i.e.: the words in a document). A popular heuristic utilized to overcome this problem is to perform before training some form of document …
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Time series modeling of text data
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms
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Scientific knowledge extraction from massive text data
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms
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General unsupervised explanatory opinion mining from text data
… to the abundance and rapid growth of opinionated data on the Web, research on opinion mining and summarization techniques has received a lot of attention from industry and academia. Most previous studies on opinion summarization have focused on predicting sentiments of entities and aspect-based …
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Discovery driven analysis on semi-structured text data
Made available in DSpace on 2010-05-19T18:39:57Z (GMT). No. of bitstreams: 4 Hauguel_Samson.pdf: 949045 bytes, checksum: c81da5764eec7003739651851a0439a5 (MD5) 1_Hauguel_Samson.pdf: 949482 bytes, checksum: 64fae416e30bc67f9ab29dac3c487ebd (MD5) 2_Hauguel_Samson.pdf: 949045 bytes, checksum: …
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Extraction of Causal-Association Networks from Unstructured Text Data
… 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 …
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Time series analysis for event detection in text data
… the rise of social media and online newswire, text streams are attracting more and more research interest. These streams are presented in the form of time series by nature, therefore, how to efficiently analyze these time series and extract useful information from them are of great importance. …
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Generative Models for Retrieval of Video, Audio and Text Data
… retrieval, which allows a user to query audio data by an example audio segment of a short duration and to find similar segments. The basic idea of our approach is to first train a hidden Markov model (HMM) using the given example, it is called the theme HMM. The total audio data available is …
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Improving the Prediction Accuracy of Text Data and Attribute Data Mining with Data Preprocessing
<p>Data Mining is the extraction of valuable information from the patterns of data and turning it into useful knowledge. Data preprocessing is an important step in the data mining process. The quality of the data affects the result and accuracy of the data mining results. Hence, Data preprocessing …
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Statistical Modeling to Information Retrieval for Searching from Big Text Data and Higher Order Inference for Reliability
… are carried out and experimented on large-scale text data set. First, we conduct an in-depth study of relationship between information of document length and document relevance to user need. Two statistical methods are proposed which incorporates document length as a substantial weighting factor …
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The nature of the human resource development research–practice gap: Text data mining and topic modeling analysis of three decades of professional and academic literature from 1990 to 2022
… topic modeling (STM) and a 50/50 training–test dataset split approach, this study scrutinizes latent topics and the prevalence in five HRD–related professional journals and five academic journals from 1990 to 2022. The results highlight the multifaceted, evolving, and dynamic nature of the gap …
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Multi-dimensional mining of unstructured data with limited supervision
As one of the most important data forms, unstructured text data plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to healthcare and scientific research. In many emerging applications, people's information needs from text data are …
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Text mining with word embedding for outlier and sentiment analysis
… easy to collect and store massive text data in various domains such as online social networks, medical records and news reports. In contrast to the gigantic volume of text data, human capabilities to read and process text data is limited. Hence, there is an emerging demand for …
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Mining entity and relation structures from text: An effort-light approach
… computerized and information-based society, text data is rich but often also ""messy"". We are inundated with vast amounts of text data, written in different genres (from grammatical news articles and scientific papers to noisy social media posts), covering topics in various domains (e.g., …
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Exploiting knowledge graphs for entity-centric prediction
As a special kind of ``big data'', text data can be regarded as data reported by human sensors. Since humans are far more intelligent than physical sensors, text data contains directly useful information and knowledge about the real world, making it possible to make predictions about real-world …
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Mining Helpdesk Databases For Professional Development Topic Discovery
… road map by which academic institutions can use text data mining techniques to derive technology skillset weaknesses and professional development topics from the site’s technical support helpdesk database. The methods employed were described in detail and applied to the helpdesk database of an …
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ReviewMiner: a novel system for multi-modal review analysis to provide visualized support for decision making
… the growth of number and length of the review text doesn’t provide easier access to knowledge in the review. In fact as the amount of reviews grow, people are less likely to be able to finish reading the helpful reviews. To tackle the information overload situation in review text data, I …
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Second chance competitive autoencoders for understanding textual data
Every day, an enormous amount of text data is produced. Sources of text data include news, social media, emails, text messages, medical reports, scientific publications, and fiction. To keep track of this data, there are categories, keywords, tags, or labels that are assigned to each text. …
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