Getting Started with Stanford CoreNLP: Tokenization, POS, NER, and Parsing

Natural Language Processing (NLP) aims to enable computers to understand, process, and generate human language. Stanford CoreNLP offers a comprehensive set of language processing tools, and the stanfordcorenlp package provides a Python interface to these tools.

Installation

Install the Python wrapper with: pip install stanfordcorenlp Download the required language models from the official site:

Model download URL: https://nlp.stanford.edu/software/corenlp-backup-download.html

Core Functions

Stanford CoreNLP supports several fundamental NLP operations:

Tokenization (分词)

Part‑of‑Speech tagging (词性标注)

Named Entity Recognition (命名实体识别)

Constituency parsing (句法成分分析)

Dependency parsing (依存句法分析)

1. Tokenization

2. Part‑of‑Speech Tagging

3. Named Entity Recognition

Named Entities: [('我爱', 'O'), ('自然', 'O'), ('语言', 'O'), ('处理', 'O'), ('技术', 'O'), ('！', 'O')]
Named Entities: [('I', 'O'), ('love', 'O'), ('natural', 'O'), ('language', 'O'), ('processing', 'O'), ('technology', 'O'), ('!', 'O')]

4. Constituency Parsing

5. Dependency Parsing

Dependency: [('ROOT', 0, 4), ('nsubj', 4, 1), ('advmod', 4, 2), ('nsubj', 4, 3), ('dobj', 4, 5), ('punct', 4, 6)]
Dependency: [('ROOT', 0, 2), ('nsubj', 2, 1), ('amod', 6, 3), ('compound', 6, 4), ('compound', 6, 5), ('dobj', 2, 6), ('punct', 2, 7)]

The article also provides the official website (https://stanfordnlp.github.io/CoreNLP/) and the GitHub repository (https://github.com/stanfordnlp/CoreNLP) for further reference.