"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.