Corenlp In Practice
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CoreNLP in Practice
"CoreNLP in Practice" "CoreNLP in Practice" is an authoritative guide for both newcomers and seasoned professionals eager to harness the full capabilities of the Stanford CoreNLP toolkit in complex, real-world NLP workflows. The book begins by orienting readers within the broader landscape of natural language processing, before diving deeply into the modular design, extensibility, and model management features that make CoreNLP a cornerstone for large-scale text analysis. Practical instructions for installing, configuring, and integrating CoreNLP on local, cluster, and cloud environments ensure readers can deploy scalable and robust solutions across diverse infrastructures. Each chapter methodically explores major linguistic annotation tasks—from advanced tokenization and sentence segmentation to intricate part-of-speech tagging, morphological analysis, syntactic and semantic parsing, and high-throughput named entity recognition. Readers learn not only the technical internals and configuration of included models, but also how to custom-train and integrate their own models, optimize for multi-language and noisy data, and build efficient, parallelized processing pipelines. Special attention is paid to integrating CoreNLP with other languages and frameworks, extending its functionality through REST APIs, custom annotators, and the seamless inclusion of neural and pretrained models. Further chapters address information extraction at scale, including relation and event extraction, coreference resolution, and the construction of queryable knowledge graphs. The book concludes with comprehensive coverage of production deployment—covering real-time and batch processing strategies, distributed architecture, monitoring, security, and best practices for testing and quality assurance. Complete with guidance on reproducible research and open-source contribution, "CoreNLP in Practice" is an indispensable resource for building and maintaining state-of-the-art NLP pipelines with confidence and precision.
Machine Learning and Principles and Practice of Knowledge Discovery in Databases
This volume constitutes the papers of several workshops which were held in conjunction with the International Workshops of ECML PKDD 2022 on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022, held in Grenoble, France, during September 19–23, 2022. The 73 revised full papers and 6 short papers presented in this book were carefully reviewed and selected from 143 submissions. ECML PKDD 2022 presents the following workshops: Workshop on Data Science for Social Good (SoGood 2022) Workshop on New Frontiers in Mining Complex Patterns (NFMCP 2022) Workshop on Explainable Knowledge Discovery in Data Mining (XKDD 2022) Workshop on Uplift Modeling (UMOD 2022) Workshop on IoT, Edge and Mobile for Embedded Machine Learning (ITEM 2022) Workshop on Mining Data for Financial Application (MIDAS 2022) Workshop on Machine Learning for Cybersecurity (MLCS 2022) Workshop on Machine Learning for Buildings Energy Management (MLBEM 2022) Workshop on Machine Learning for Pharma and Healthcare Applications (PharML 2022) Workshop on Data Analysis in Life Science (DALS 2022) Workshop on IoT Streams for Predictive Maintenance (IoT-PdM 2022)
Technology in Education. Transforming Educational Practices with Technology
This book constitutes the refereed proceedings of the International Conference on Technology in Education, ICTE 2014, held in Hong Kong, in July 2014. The 18 revised full papers and 4 short papers presented were carefully reviewed and selected from 45 submissions. The papers are organized in topical sections on application of mobile technologies in e-learning; technology advancement in e-learning systems; innovations in e-learning pedagogy; open education and institution e-learning policy.