# CoreNLP — Stanford Natural Language Processing Toolkit

> Production-grade Java NLP toolkit for tokenization, parsing, NER, sentiment analysis, and coreference resolution from Stanford University.

## Install

Save as a script file and run:

# CoreNLP — Stanford Natural Language Processing Toolkit

## Quick Use
```bash
# Download and run CoreNLP server
wget https://nlp.stanford.edu/software/stanford-corenlp-latest.zip
unzip stanford-corenlp-latest.zip && cd stanford-corenlp-*
java -mx4g -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer -port 9000

# Query from command line
curl -d "The quick brown fox jumps over the lazy dog." 
  "http://localhost:9000/?annotators=tokenize,pos,ner&outputFormat=json"
```

## Introduction
CoreNLP is a battle-tested Java NLP framework created by the Stanford NLP Group. It provides a unified pipeline for applying linguistic annotations — from basic tokenization to deep parsing, named entity recognition, sentiment analysis, and coreference resolution — to arbitrary text in multiple languages.

## What CoreNLP Does
- Runs a full NLP pipeline (tokenize, sentence-split, POS-tag, lemmatize, NER, parse, sentiment, coref) in a single pass
- Provides a REST server so any language can call it over HTTP with JSON output
- Supports rule-based, statistical, and neural models for each annotator
- Handles multiple human languages including English, Chinese, Arabic, French, German, and Spanish
- Offers both programmatic Java API and command-line interface for batch processing

## Architecture Overview
CoreNLP is built around an annotation pipeline: a document enters as raw text and flows through a configurable chain of annotators, each adding a layer of linguistic structure. Annotators are loaded lazily and can be mixed and matched via a properties file. Under the hood, models range from CRF-based sequence taggers to shift-reduce constituency parsers and a neural-network coreference resolver. The optional server mode wraps the pipeline behind a Jetty HTTP endpoint.

## Self-Hosting & Configuration
- Requires Java 8 or later; download the zip from the Stanford NLP website and run with `java -cp`
- Memory allocation is critical — use `-mx4g` or higher for English models; Chinese models may need 8 GB+
- Customize annotators via a `corenlp.properties` file or URL query parameters
- Deploy as a Docker container using community-maintained images for reproducible environments
- For production, place behind a reverse proxy and limit concurrent requests to control memory usage

## Key Features
- Over 15 years of academic research backing each annotator with peer-reviewed accuracy benchmarks
- Deterministic rule-based components (TokensRegex, Semgrex, Tregex) for pattern-based extraction
- Multilingual support covering 6+ languages with downloadable model jars
- Temporal expression normalization (SUTime) for extracting and grounding dates and times
- Relation extraction and knowledge-base population annotators for structured information extraction

## Comparison with Similar Tools
- **spaCy** — Python-native, faster for production NLP but fewer deep-linguistic annotators than CoreNLP
- **Stanza** — Stanford's own Python library built on neural models; lighter than CoreNLP but lacks rule-based components
- **NLTK** — Educational toolkit with wide coverage but slower and less production-ready
- **Hugging Face Transformers** — Excels at transformer-based tasks but requires building your own pipeline around individual models
- **Apache OpenNLP** — Lightweight Java alternative with smaller model footprint but fewer annotators

## FAQ
**Q: Is CoreNLP free to use commercially?**
A: CoreNLP is released under the GPL v2 license. Commercial licensing is available through Stanford for proprietary use.

**Q: How does CoreNLP compare to modern transformer models?**
A: CoreNLP's neural components are competitive on standard benchmarks. For cutting-edge accuracy on specific tasks, fine-tuned transformers may outperform it, but CoreNLP offers a ready-made, integrated pipeline.

**Q: Can I use CoreNLP from Python?**
A: Yes — either call the CoreNLP server via HTTP from Python or use the Stanza library, which provides a Python interface to CoreNLP.

**Q: What is the minimum hardware requirement?**
A: English models need roughly 4 GB of RAM. A modern multi-core CPU is recommended; GPU acceleration is not used by the default pipeline.

## Sources
- https://github.com/stanfordnlp/CoreNLP
- https://stanfordnlp.github.io/CoreNLP/

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