# 使用 Homebrew, MacPorts 安装 stanford-corenlp

查看 stanford-corenlp 的安装路径、可执行文件、元数据以及面向 AI 代理工作流的安全说明。

## 安装

```sh
sudo av install brew:stanford-corenlp
```

其他安装命令:

### macOS

- Homebrew (100%):

```sh
brew install stanford-corenlp
```

  证据: local Homebrew formula metadata

- MacPorts (94%):

```sh
sudo port install stanford-corenlp
```

  证据: MacPorts ports tree: textproc/stanford-corenlp/Portfile from https://api.github.com/repos/macports/macports-ports/git/trees/master?recursive=1

## 软件包事实

- **软件包键:** brew:stanford-corenlp
- **软件包管理器:** Homebrew
- **版本:** 4.4.0
- **来源摘要:** Java suite of core NLP tools
- **主页:** <https://stanfordnlp.github.io/CoreNLP/>
- **最后更新:** 2026-06-22T14:06:22-07:00
- **已生成:** 2026-08-03T19:37:03+00:00

## 可执行文件

- corenlp.sh (别名)

## 安装行为

- Bottle: 不可用

## 版本和新鲜度

- 页面生成时间: 2026-08-03
- 管理器版本: 4.4.0
## 项目历史与用法

Stanford CoreNLP is Stanford NLP Group's Java suite for core natural-language-processing annotations such as tokenization, sentence splitting, part-of-speech tagging, named entities, parsing, coreference, sentiment, and relations.

### 项目历史

The official release history says the earliest open-source software now comprising CoreNLP was the Stanford Parser, released on 5 December 2002, and that the first unified public package tying together the Stanford NLP tools was released on 1 November 2010. The project is maintained by the Stanford NLP Group and distributed with Java APIs, command-line usage, a server, model jars, Maven artifacts, and a GitHub repository.

### 采用历史

CoreNLP became a common packaged NLP workhorse because it exposed research-grade annotators through stable Java artifacts and command-line/server entry points. Its documentation explicitly notes that many users call it from JavaScript, Python, and other languages, and it maintains a page of wrappers and clients in many language ecosystems.

### 使用方式

A typical command-line run downloads the distribution and model jars, puts them on the Java classpath, and runs `java edu.stanford.nlp.pipeline.StanfordCoreNLP -file input.txt`. The same package can also be used through Java APIs, a simple API, third-party clients, or the CoreNLP server.

### 为什么软件包爱好者会关心

Package maintainers care about CoreNLP because it packages large model artifacts, Java classpath behavior, and multi-language wrapper expectations into a CLI-friendly distribution. It is the kind of tool where a working package saves users from jar management, model placement, and wrapper-version drift.

### 时间线

- 2002: Stanford Parser is released as the earliest open-source software later comprising CoreNLP.
- 2010: First unified CoreNLP package is publicly released.
- 2014: The CoreNLP system-demonstration paper is published.
- 2020: Version 4.0.0 updates tokenization and models around UDv2.
- 2025: Version 4.5.10 removes patterns/Lucene-related features for security reasons.

### Related projects

- CoreNLP includes or relates to Stanford Parser, Stanford NER, Stanford POS Tagger, dependency parsing, SUTime, TokensRegex, Tregex/Tsurgeon, Stanza clients, Maven artifacts, and model jars.

### 来源

- <https://stanfordnlp.github.io/CoreNLP/: official overview, GitHub link, quickstart, usage modes, license, and citation.>
- <https://stanfordnlp.github.io/CoreNLP/history.html: official release history from 2002 parser origins through CoreNLP releases.>
- <https://stanfordnlp.github.io/CoreNLP/other-languages.html: official page of language clients and wrappers.>


## 安全说明

narrow executable package without higher-risk signals.

- **Geiger 风险:** 绿色 / 低
- narrow executable package without higher-risk signals

## 其他软件包管理器记录

- MacPorts - stanford-corenlp: normalized package name match | MacPorts ports tree: textproc/stanford-corenlp/Portfile from https://api.github.com/repos/macports/macports-ports/git/trees/master?recursive=1


## Combined YAML source

View the package source record on GitHub. [combined/stanford-corenlp.yml](https://github.com/mxcl/pkgdb/blob/main/combined/stanford-corenlp.yml)


## 来源

- pkg.so package database
- Geiger risk classifier
- curated package history
- pkgdb category and tag curation
- external package-manager database matches
- cross-ecosystem install command graph