Found insideThis two-volume set LNAI 12163 and 12164 constitutes the refereed proceedings of the 21th International Conference on Artificial Intelligence in Education, AIED 2020, held in Ifrane, Morocco, in July 2020.* The 49 full papers presented ... Found inside – Page 850... or noted by using Bert Embeddings as introduced by the authors in [19]. ... Moratanch N, Gopalan C (2017) A survey on extractive text summarization. The R Book is aimed at undergraduates, postgraduates and professionals in science, engineering and medicine. It is also ideal for students and professionals in statistics, economics, geography and the social sciences. The aim of ITNEC 2020 is to provide a platform for researchers, engineers, academicians as well as industrial professionals from all over the world to present their research results and development activities in Information Technology, ... Describes recent academic and industrial applications of topic models with the goal of launching a young researcher capable of building their own applications of topic models. Found inside – Page 194The idea of augmenting a literature search with machine learning was discussed in ... Self-Tuned Descriptive Document Clustering using a Predictive Network, ... Found inside – Page 437We cluster the claims in an offline operation with hierarchical clustering. For each claim, we calculate its embeddings using BERT [11]1. Found insideCrises aren't real objective events. Instead, Spector demonstrates they are claims of urgency imposed by leaders to assert power and exert control. Found inside – Page 197Cross-Domain Text Classification Based on BERT Model Kuan Zhang, ... Finally, the k-means and mean-shift clustering are used to extract the data feature ... Found insideUsing clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what natural language processing is, the promise of deep learning in the field, how to clean and prepare text data for modeling, and how ... Found inside – Page 117The model employs the BERT encoder to yield contextualized document representation. ... aiming to group cohesion words in a document into one cluster [20]. Found insideThe book presents high quality papers presented at 2nd International Conference on Intelligent Computing, Communication & Devices (ICCD 2016) organized by Interscience Institute of Management and Technology (IIMT), Bhubaneswar, Odisha, ... Found insideSummary Mahout in Action is a hands-on introduction to machine learning with Apache Mahout. Following real-world examples, the book presents practical use cases and then illustrates how Mahout can be applied to solve them. Found inside – Page 54Document classification: models' accuracy (%). ... 46.3 — — BERT 67.6 52.5 87.7 78.2 RoBERTa 66.6 53.0 86.5 77.5 XLM-RoBERTa 69.2 52.5 76.2 74.2 DistilBERT ... Found inside – Page 205An incremental clustering algorithm is applied to weighted trigram representations ... Also, the system is capable of processing untrained documents with an ... Found insideThe authors also provide a collection of old and new open questions. This book is intended as a textbook for a graduate course or as a learning tool for researchers. Found inside – Page 365Google researchers, we decided to use BERT as our feature extractor to extract ... data mainly documents and cluster them into distinct topics based on the ... Found inside – Page 46Songnian Li, Suzana Dragicevic, Bert Veenendaal. When the correct clustering result is ... Cluster numbers --> Time:0.191354036331 Seconds
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