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Stanford CoreNLP

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Stanford CoreNLP provides a set of natural language analysis tools written in Java. It can take raw human language text input and give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize and interpret dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases or word dependencies, and indicate which noun phrases refer to the same entities. It was originally developed for English, but now also provides varying levels of support for (Modern Standard) Arabic, (mainland) Chinese, French, German, and Spanish. Stanford CoreNLP is an integrated framework, which make it very easy to apply a bunch of language analysis tools to a piece of text. Starting from plain text, you can run all the tools with just two lines of code. Its analyses provide the foundational building blocks for higher-level and domain-specific text understanding applications. Stanford CoreNLP is a set of stable and well-tested natural language processing tools, widely used by various groups in academia, industry, and government. The tools variously use rule-based, probabilistic machine learning, and deep learning components.

The Stanford CoreNLP code is written in Java and licensed under the GNU General Public License (v3 or later). Note that this is the full GPL, which allows many free uses, but not its use in proprietary software that you distribute to others.

Build Instructions

Several times a year we distribute a new version of the software, which corresponds to a stable commit.

During the time between releases, one can always use the latest, under development version of our code.

Here are some helpful instructions to use the latest code:

Provided build

Sometimes we will provide updated jars here which have the latest version of the code.

At present the current released version of the code is our most recent released jar, though you can always build the very latest from GitHub HEAD yourself.

Build with Ant

  1. Make sure you have Ant installed, details here: http://ant.apache.org/
  2. Compile the code with this command: cd CoreNLP ; ant
  3. Then run this command to build a jar with the latest version of the code: cd CoreNLP/classes ; jar -cf ../stanford-corenlp.jar edu
  4. This will create a new jar called stanford-corenlp.jar in the CoreNLP folder which contains the latest code
  5. The dependencies that work with the latest code are in CoreNLP/lib and CoreNLP/liblocal, so make sure to include those in your CLASSPATH.
  6. When using the latest version of the code make sure to download the latest versions of the corenlp-models, english-models, and english-models-kbp and include them in your CLASSPATH. If you are processing languages other than English, make sure to download the latest version of the models jar for the language you are interested in.

Build with Maven

  1. Make sure you have Maven installed, details here: https://maven.apache.org/
  2. If you run this command in the CoreNLP directory: mvn package , it should run the tests and build this jar file: CoreNLP/target/stanford-corenlp-3.9.2.jar
  3. When using the latest version of the code make sure to download the latest versions of the corenlp-models, english-models, and english-models-kbp and include them in your CLASSPATH. If you are processing languages other than English, make sure to download the latest version of the models jar for the language you are interested in.
  4. If you want to use Stanford CoreNLP as part of a Maven project you need to install the models jars into your Maven repository. Below is a sample command for installing the Spanish models jar. For other languages just change the language name in the command. To install stanford-corenlp-models-current.jar you will need to set -Dclassifier=models. Here is the sample command for Spanish: mvn install:install-file -Dfile=/location/of/stanford-spanish-corenlp-models-current.jar -DgroupId=edu.stanford.nlp -DartifactId=stanford-corenlp -Dversion=3.9.2 -Dclassifier=models-spanish -Dpackaging=jar

Useful resources

You can find releases of Stanford CoreNLP on Maven Central.

You can find more explanation and documentation on the Stanford CoreNLP homepage.

The most recent models associated with the code in the HEAD of this repository can be found here.

Some of the larger (English) models -- like the shift-reduce parser and WikiDict -- are not distributed with our default models jar. The most recent version of these models can be found here.

We distribute resources for other languages as well, including Arabic models, Chinese models, French models, German models, and Spanish models.

For information about making contributions to Stanford CoreNLP, see the file CONTRIBUTING.md.

Questions about CoreNLP can either be posted on StackOverflow with the tag stanford-nlp, or on the mailing lists.

About

Stanford CoreNLP: A Java suite of core NLP tools.

stanfordnlp.github.io/CoreNLP/

Resources

Readme
GPL-3.0 license

Contributing

Contributing
Activity

Stars

0 stars

Watchers

1 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
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Stanford CoreNLP
Build StatusMaven CentralTwitter
Stanford CoreNLP provides a set of natural language analysis tools written in Java. It can take raw human language text input and give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize and interpret dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases or word dependencies, and indicate which noun phrases refer to the same entities. It was originally developed for English, but now also provides varying levels of support for (Modern Standard) Arabic, (mainland) Chinese, French, German, and Spanish. Stanford CoreNLP is an integrated framework, which make it very easy to apply a bunch of language analysis tools to a piece of text. Starting from plain text, you can run all the tools with just two lines of code. Its analyses provide the foundational building blocks for higher-level and domain-specific text understanding applications. Stanford CoreNLP is a set of stable and well-tested natural language processing tools, widely used by various groups in academia, industry, and government. The tools variously use rule-based, probabilistic machine learning, and deep learning components.
The Stanford CoreNLP code is written in Java and licensed under the GNU General Public License (v3 or later). Note that this is the full GPL, which allows many free uses, but not its use in proprietary software that you distribute to others.
Build Instructions
Several times a year we distribute a new version of the software, which corresponds to a stable commit.
During the time between releases, one can always use the latest, under development version of our code.
Here are some helpful instructions to use the latest code:
Provided build
Sometimes we will provide updated jars here which have the latest version of the code.
At present the current released version of the code is our most recent released jar, though you can always build the very latest from GitHub HEAD yourself.
Build with Ant
  1. Make sure you have Ant installed, details here: http://ant.apache.org/
  2. Compile the code with this command: cd CoreNLP ; ant
  3. Then run this command to build a jar with the latest version of the code: cd CoreNLP/classes ; jar -cf ../stanford-corenlp.jar edu
  4. This will create a new jar called stanford-corenlp.jar in the CoreNLP folder which contains the latest code
  5. The dependencies that work with the latest code are in CoreNLP/lib and CoreNLP/liblocal, so make sure to include those in your CLASSPATH.
  6. When using the latest version of the code make sure to download the latest versions of the corenlp-models, english-models, and english-models-kbp and include them in your CLASSPATH. If you are processing languages other than English, make sure to download the latest version of the models jar for the language you are interested in.
Build with Maven
  1. Make sure you have Maven installed, details here: https://maven.apache.org/
  2. If you run this command in the CoreNLP directory: mvn package , it should run the tests and build this jar file: CoreNLP/target/stanford-corenlp-3.9.2.jar
  3. When using the latest version of the code make sure to download the latest versions of the corenlp-models, english-models, and english-models-kbp and include them in your CLASSPATH. If you are processing languages other than English, make sure to download the latest version of the models jar for the language you are interested in.
  4. If you want to use Stanford CoreNLP as part of a Maven project you need to install the models jars into your Maven repository. Below is a sample command for installing the Spanish models jar. For other languages just change the language name in the command. To install stanford-corenlp-models-current.jar you will need to set -Dclassifier=models. Here is the sample command for Spanish: mvn install:install-file -Dfile=/location/of/stanford-spanish-corenlp-models-current.jar -DgroupId=edu.stanford.nlp -DartifactId=stanford-corenlp -Dversion=3.9.2 -Dclassifier=models-spanish -Dpackaging=jar
Useful resources
You can find releases of Stanford CoreNLP on Maven Central.
You can find more explanation and documentation on the Stanford CoreNLP homepage.
The most recent models associated with the code in the HEAD of this repository can be found here.
Some of the larger (English) models -- like the shift-reduce parser and WikiDict -- are not distributed with our default models jar.
The most recent version of these models can be found here.
We distribute resources for other languages as well, including Arabic models,
Chinese models,
French models,
German models,
and Spanish models.
For information about making contributions to Stanford CoreNLP, see the file CONTRIBUTING.md.
Questions about CoreNLP can either be posted on StackOverflow with the tag stanford-nlp,
or on the mailing lists.
About
Stanford CoreNLP: A Java suite of core NLP tools.
stanfordnlp.github.io/CoreNLP/
Resources
Readme
GPL-3.0 license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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Stanford CoreNLP
Build StatusMaven CentralTwitter
Stanford CoreNLP provides a set of natural language analysis tools written in Java. It can take raw human language text input and give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize and interpret dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases or word dependencies, and indicate which noun phrases refer to the same entities. It was originally developed for English, but now also provides varying levels of support for (Modern Standard) Arabic, (mainland) Chinese, French, German, and Spanish. Stanford CoreNLP is an integrated framework, which make it very easy to apply a bunch of language analysis tools to a piece of text. Starting from plain text, you can run all the tools with just two lines of code. Its analyses provide the foundational building blocks for higher-level and domain-specific text understanding applications. Stanford CoreNLP is a set of stable and well-tested natural language processing tools, widely used by various groups in academia, industry, and government. The tools variously use rule-based, probabilistic machine learning, and deep learning components.
The Stanford CoreNLP code is written in Java and licensed under the GNU General Public License (v3 or later). Note that this is the full GPL, which allows many free uses, but not its use in proprietary software that you distribute to others.
Build Instructions
Several times a year we distribute a new version of the software, which corresponds to a stable commit.
During the time between releases, one can always use the latest, under development version of our code.
Here are some helpful instructions to use the latest code:
Provided build
Sometimes we will provide updated jars here which have the latest version of the code.
At present the current released version of the code is our most recent released jar, though you can always build the very latest from GitHub HEAD yourself.
Build with Ant
  1. Make sure you have Ant installed, details here: http://ant.apache.org/
  2. Compile the code with this command: cd CoreNLP ; ant
  3. Then run this command to build a jar with the latest version of the code: cd CoreNLP/classes ; jar -cf ../stanford-corenlp.jar edu
  4. This will create a new jar called stanford-corenlp.jar in the CoreNLP folder which contains the latest code
  5. The dependencies that work with the latest code are in CoreNLP/lib and CoreNLP/liblocal, so make sure to include those in your CLASSPATH.
  6. When using the latest version of the code make sure to download the latest versions of the corenlp-models, english-models, and english-models-kbp and include them in your CLASSPATH. If you are processing languages other than English, make sure to download the latest version of the models jar for the language you are interested in.
Build with Maven
  1. Make sure you have Maven installed, details here: https://maven.apache.org/
  2. If you run this command in the CoreNLP directory: mvn package , it should run the tests and build this jar file: CoreNLP/target/stanford-corenlp-3.9.2.jar
  3. When using the latest version of the code make sure to download the latest versions of the corenlp-models, english-models, and english-models-kbp and include them in your CLASSPATH. If you are processing languages other than English, make sure to download the latest version of the models jar for the language you are interested in.
  4. If you want to use Stanford CoreNLP as part of a Maven project you need to install the models jars into your Maven repository. Below is a sample command for installing the Spanish models jar. For other languages just change the language name in the command. To install stanford-corenlp-models-current.jar you will need to set -Dclassifier=models. Here is the sample command for Spanish: mvn install:install-file -Dfile=/location/of/stanford-spanish-corenlp-models-current.jar -DgroupId=edu.stanford.nlp -DartifactId=stanford-corenlp -Dversion=3.9.2 -Dclassifier=models-spanish -Dpackaging=jar
Useful resources
You can find releases of Stanford CoreNLP on Maven Central.
You can find more explanation and documentation on the Stanford CoreNLP homepage.
The most recent models associated with the code in the HEAD of this repository can be found here.
Some of the larger (English) models -- like the shift-reduce parser and WikiDict -- are not distributed with our default models jar.
The most recent version of these models can be found here.
We distribute resources for other languages as well, including Arabic models,
Chinese models,
French models,
German models,
and Spanish models.
For information about making contributions to Stanford CoreNLP, see the file CONTRIBUTING.md.
Questions about CoreNLP can either be posted on StackOverflow with the tag stanford-nlp,
or on the mailing lists.
About
Stanford CoreNLP: A Java suite of core NLP tools.
stanfordnlp.github.io/CoreNLP/
Resources
Readme
GPL-3.0 license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Stanford CoreNLP
Build StatusMaven CentralTwitter
Stanford CoreNLP provides a set of natural language analysis tools written in Java. It can take raw human language text input and give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize and interpret dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases or word dependencies, and indicate which noun phrases refer to the same entities. It was originally developed for English, but now also provides varying levels of support for (Modern Standard) Arabic, (mainland) Chinese, French, German, and Spanish. Stanford CoreNLP is an integrated framework, which make it very easy to apply a bunch of language analysis tools to a piece of text. Starting from plain text, you can run all the tools with just two lines of code. Its analyses provide the foundational building blocks for higher-level and domain-specific text understanding applications. Stanford CoreNLP is a set of stable and well-tested natural language processing tools, widely used by various groups in academia, industry, and government. The tools variously use rule-based, probabilistic machine learning, and deep learning components.
The Stanford CoreNLP code is written in Java and licensed under the GNU General Public License (v3 or later). Note that this is the full GPL, which allows many free uses, but not its use in proprietary software that you distribute to others.
Build Instructions
Several times a year we distribute a new version of the software, which corresponds to a stable commit.
During the time between releases, one can always use the latest, under development version of our code.
Here are some helpful instructions to use the latest code:
Provided build
Sometimes we will provide updated jars here which have the latest version of the code.
At present the current released version of the code is our most recent released jar, though you can always build the very latest from GitHub HEAD yourself.
Build with Ant
  1. Make sure you have Ant installed, details here: http://ant.apache.org/
  2. Compile the code with this command: cd CoreNLP ; ant
  3. Then run this command to build a jar with the latest version of the code: cd CoreNLP/classes ; jar -cf ../stanford-corenlp.jar edu
  4. This will create a new jar called stanford-corenlp.jar in the CoreNLP folder which contains the latest code
  5. The dependencies that work with the latest code are in CoreNLP/lib and CoreNLP/liblocal, so make sure to include those in your CLASSPATH.
  6. When using the latest version of the code make sure to download the latest versions of the corenlp-models, english-models, and english-models-kbp and include them in your CLASSPATH. If you are processing languages other than English, make sure to download the latest version of the models jar for the language you are interested in.
Build with Maven
  1. Make sure you have Maven installed, details here: https://maven.apache.org/
  2. If you run this command in the CoreNLP directory: mvn package , it should run the tests and build this jar file: CoreNLP/target/stanford-corenlp-3.9.2.jar
  3. When using the latest version of the code make sure to download the latest versions of the corenlp-models, english-models, and english-models-kbp and include them in your CLASSPATH. If you are processing languages other than English, make sure to download the latest version of the models jar for the language you are interested in.
  4. If you want to use Stanford CoreNLP as part of a Maven project you need to install the models jars into your Maven repository. Below is a sample command for installing the Spanish models jar. For other languages just change the language name in the command. To install stanford-corenlp-models-current.jar you will need to set -Dclassifier=models. Here is the sample command for Spanish: mvn install:install-file -Dfile=/location/of/stanford-spanish-corenlp-models-current.jar -DgroupId=edu.stanford.nlp -DartifactId=stanford-corenlp -Dversion=3.9.2 -Dclassifier=models-spanish -Dpackaging=jar
Useful resources
You can find releases of Stanford CoreNLP on Maven Central.
You can find more explanation and documentation on the Stanford CoreNLP homepage.
The most recent models associated with the code in the HEAD of this repository can be found here.
Some of the larger (English) models -- like the shift-reduce parser and WikiDict -- are not distributed with our default models jar.
The most recent version of these models can be found here.
We distribute resources for other languages as well, including Arabic models,
Chinese models,
French models,
German models,
and Spanish models.
For information about making contributions to Stanford CoreNLP, see the file CONTRIBUTING.md.
Questions about CoreNLP can either be posted on StackOverflow with the tag stanford-nlp,
or on the mailing lists.
About
Stanford CoreNLP: A Java suite of core NLP tools.
stanfordnlp.github.io/CoreNLP/
Resources
Readme
GPL-3.0 license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Stanford CoreNLP
Build StatusMaven CentralTwitter
Stanford CoreNLP provides a set of natural language analysis tools written in Java. It can take raw human language text input and give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize and interpret dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases or word dependencies, and indicate which noun phrases refer to the same entities. It was originally developed for English, but now also provides varying levels of support for (Modern Standard) Arabic, (mainland) Chinese, French, German, and Spanish. Stanford CoreNLP is an integrated framework, which make it very easy to apply a bunch of language analysis tools to a piece of text. Starting from plain text, you can run all the tools with just two lines of code. Its analyses provide the foundational building blocks for higher-level and domain-specific text understanding applications. Stanford CoreNLP is a set of stable and well-tested natural language processing tools, widely used by various groups in academia, industry, and government. The tools variously use rule-based, probabilistic machine learning, and deep learning components.
The Stanford CoreNLP code is written in Java and licensed under the GNU General Public License (v3 or later). Note that this is the full GPL, which allows many free uses, but not its use in proprietary software that you distribute to others.
Build Instructions
Several times a year we distribute a new version of the software, which corresponds to a stable commit.
During the time between releases, one can always use the latest, under development version of our code.
Here are some helpful instructions to use the latest code:
Provided build
Sometimes we will provide updated jars here which have the latest version of the code.
At present the current released version of the code is our most recent released jar, though you can always build the very latest from GitHub HEAD yourself.
Build with Ant
  1. Make sure you have Ant installed, details here: http://ant.apache.org/
  2. Compile the code with this command: cd CoreNLP ; ant
  3. Then run this command to build a jar with the latest version of the code: cd CoreNLP/classes ; jar -cf ../stanford-corenlp.jar edu
  4. This will create a new jar called stanford-corenlp.jar in the CoreNLP folder which contains the latest code
  5. The dependencies that work with the latest code are in CoreNLP/lib and CoreNLP/liblocal, so make sure to include those in your CLASSPATH.
  6. When using the latest version of the code make sure to download the latest versions of the corenlp-models, english-models, and english-models-kbp and include them in your CLASSPATH. If you are processing languages other than English, make sure to download the latest version of the models jar for the language you are interested in.
Build with Maven
  1. Make sure you have Maven installed, details here: https://maven.apache.org/
  2. If you run this command in the CoreNLP directory: mvn package , it should run the tests and build this jar file: CoreNLP/target/stanford-corenlp-3.9.2.jar
  3. When using the latest version of the code make sure to download the latest versions of the corenlp-models, english-models, and english-models-kbp and include them in your CLASSPATH. If you are processing languages other than English, make sure to download the latest version of the models jar for the language you are interested in.
  4. If you want to use Stanford CoreNLP as part of a Maven project you need to install the models jars into your Maven repository. Below is a sample command for installing the Spanish models jar. For other languages just change the language name in the command. To install stanford-corenlp-models-current.jar you will need to set -Dclassifier=models. Here is the sample command for Spanish: mvn install:install-file -Dfile=/location/of/stanford-spanish-corenlp-models-current.jar -DgroupId=edu.stanford.nlp -DartifactId=stanford-corenlp -Dversion=3.9.2 -Dclassifier=models-spanish -Dpackaging=jar
Useful resources
You can find releases of Stanford CoreNLP on Maven Central.
You can find more explanation and documentation on the Stanford CoreNLP homepage.
The most recent models associated with the code in the HEAD of this repository can be found here.
Some of the larger (English) models -- like the shift-reduce parser and WikiDict -- are not distributed with our default models jar.
The most recent version of these models can be found here.
We distribute resources for other languages as well, including Arabic models,
Chinese models,
French models,
German models,
and Spanish models.
For information about making contributions to Stanford CoreNLP, see the file CONTRIBUTING.md.
Questions about CoreNLP can either be posted on StackOverflow with the tag stanford-nlp,
or on the mailing lists.
About
Stanford CoreNLP: A Java suite of core NLP tools.
stanfordnlp.github.io/CoreNLP/
Resources
Readme
GPL-3.0 license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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Stanford CoreNLP
Build StatusMaven CentralTwitter
Stanford CoreNLP provides a set of natural language analysis tools written in Java. It can take raw human language text input and give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize and interpret dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases or word dependencies, and indicate which noun phrases refer to the same entities. It was originally developed for English, but now also provides varying levels of support for (Modern Standard) Arabic, (mainland) Chinese, French, German, and Spanish. Stanford CoreNLP is an integrated framework, which make it very easy to apply a bunch of language analysis tools to a piece of text. Starting from plain text, you can run all the tools with just two lines of code. Its analyses provide the foundational building blocks for higher-level and domain-specific text understanding applications. Stanford CoreNLP is a set of stable and well-tested natural language processing tools, widely used by various groups in academia, industry, and government. The tools variously use rule-based, probabilistic machine learning, and deep learning components.
The Stanford CoreNLP code is written in Java and licensed under the GNU General Public License (v3 or later). Note that this is the full GPL, which allows many free uses, but not its use in proprietary software that you distribute to others.
Build Instructions
Several times a year we distribute a new version of the software, which corresponds to a stable commit.
During the time between releases, one can always use the latest, under development version of our code.
Here are some helpful instructions to use the latest code:
Provided build
Sometimes we will provide updated jars here which have the latest version of the code.
At present the current released version of the code is our most recent released jar, though you can always build the very latest from GitHub HEAD yourself.
Build with Ant
  1. Make sure you have Ant installed, details here: http://ant.apache.org/
  2. Compile the code with this command: cd CoreNLP ; ant
  3. Then run this command to build a jar with the latest version of the code: cd CoreNLP/classes ; jar -cf ../stanford-corenlp.jar edu
  4. This will create a new jar called stanford-corenlp.jar in the CoreNLP folder which contains the latest code
  5. The dependencies that work with the latest code are in CoreNLP/lib and CoreNLP/liblocal, so make sure to include those in your CLASSPATH.
  6. When using the latest version of the code make sure to download the latest versions of the corenlp-models, english-models, and english-models-kbp and include them in your CLASSPATH. If you are processing languages other than English, make sure to download the latest version of the models jar for the language you are interested in.
Build with Maven
  1. Make sure you have Maven installed, details here: https://maven.apache.org/
  2. If you run this command in the CoreNLP directory: mvn package , it should run the tests and build this jar file: CoreNLP/target/stanford-corenlp-3.9.2.jar
  3. When using the latest version of the code make sure to download the latest versions of the corenlp-models, english-models, and english-models-kbp and include them in your CLASSPATH. If you are processing languages other than English, make sure to download the latest version of the models jar for the language you are interested in.
  4. If you want to use Stanford CoreNLP as part of a Maven project you need to install the models jars into your Maven repository. Below is a sample command for installing the Spanish models jar. For other languages just change the language name in the command. To install stanford-corenlp-models-current.jar you will need to set -Dclassifier=models. Here is the sample command for Spanish: mvn install:install-file -Dfile=/location/of/stanford-spanish-corenlp-models-current.jar -DgroupId=edu.stanford.nlp -DartifactId=stanford-corenlp -Dversion=3.9.2 -Dclassifier=models-spanish -Dpackaging=jar
Useful resources
You can find releases of Stanford CoreNLP on Maven Central.
You can find more explanation and documentation on the Stanford CoreNLP homepage.
The most recent models associated with the code in the HEAD of this repository can be found here.
Some of the larger (English) models -- like the shift-reduce parser and WikiDict -- are not distributed with our default models jar.
The most recent version of these models can be found here.
We distribute resources for other languages as well, including Arabic models,
Chinese models,
French models,
German models,
and Spanish models.
For information about making contributions to Stanford CoreNLP, see the file CONTRIBUTING.md.
Questions about CoreNLP can either be posted on StackOverflow with the tag stanford-nlp,
or on the mailing lists.
About
Stanford CoreNLP: A Java suite of core NLP tools.
stanfordnlp.github.io/CoreNLP/
Resources
Readme
GPL-3.0 license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Stanford CoreNLP
Build StatusMaven CentralTwitter
Stanford CoreNLP provides a set of natural language analysis tools written in Java. It can take raw human language text input and give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize and interpret dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases or word dependencies, and indicate which noun phrases refer to the same entities. It was originally developed for English, but now also provides varying levels of support for (Modern Standard) Arabic, (mainland) Chinese, French, German, and Spanish. Stanford CoreNLP is an integrated framework, which make it very easy to apply a bunch of language analysis tools to a piece of text. Starting from plain text, you can run all the tools with just two lines of code. Its analyses provide the foundational building blocks for higher-level and domain-specific text understanding applications. Stanford CoreNLP is a set of stable and well-tested natural language processing tools, widely used by various groups in academia, industry, and government. The tools variously use rule-based, probabilistic machine learning, and deep learning components.
The Stanford CoreNLP code is written in Java and licensed under the GNU General Public License (v3 or later). Note that this is the full GPL, which allows many free uses, but not its use in proprietary software that you distribute to others.
Build Instructions
Several times a year we distribute a new version of the software, which corresponds to a stable commit.
During the time between releases, one can always use the latest, under development version of our code.
Here are some helpful instructions to use the latest code:
Provided build
Sometimes we will provide updated jars here which have the latest version of the code.
At present the current released version of the code is our most recent released jar, though you can always build the very latest from GitHub HEAD yourself.
Build with Ant
  1. Make sure you have Ant installed, details here: http://ant.apache.org/
  2. Compile the code with this command: cd CoreNLP ; ant
  3. Then run this command to build a jar with the latest version of the code: cd CoreNLP/classes ; jar -cf ../stanford-corenlp.jar edu
  4. This will create a new jar called stanford-corenlp.jar in the CoreNLP folder which contains the latest code
  5. The dependencies that work with the latest code are in CoreNLP/lib and CoreNLP/liblocal, so make sure to include those in your CLASSPATH.
  6. When using the latest version of the code make sure to download the latest versions of the corenlp-models, english-models, and english-models-kbp and include them in your CLASSPATH. If you are processing languages other than English, make sure to download the latest version of the models jar for the language you are interested in.
Build with Maven
  1. Make sure you have Maven installed, details here: https://maven.apache.org/
  2. If you run this command in the CoreNLP directory: mvn package , it should run the tests and build this jar file: CoreNLP/target/stanford-corenlp-3.9.2.jar
  3. When using the latest version of the code make sure to download the latest versions of the corenlp-models, english-models, and english-models-kbp and include them in your CLASSPATH. If you are processing languages other than English, make sure to download the latest version of the models jar for the language you are interested in.
  4. If you want to use Stanford CoreNLP as part of a Maven project you need to install the models jars into your Maven repository. Below is a sample command for installing the Spanish models jar. For other languages just change the language name in the command. To install stanford-corenlp-models-current.jar you will need to set -Dclassifier=models. Here is the sample command for Spanish: mvn install:install-file -Dfile=/location/of/stanford-spanish-corenlp-models-current.jar -DgroupId=edu.stanford.nlp -DartifactId=stanford-corenlp -Dversion=3.9.2 -Dclassifier=models-spanish -Dpackaging=jar
Useful resources
You can find releases of Stanford CoreNLP on Maven Central.
You can find more explanation and documentation on the Stanford CoreNLP homepage.
The most recent models associated with the code in the HEAD of this repository can be found here.
Some of the larger (English) models -- like the shift-reduce parser and WikiDict -- are not distributed with our default models jar.
The most recent version of these models can be found here.
We distribute resources for other languages as well, including Arabic models,
Chinese models,
French models,
German models,
and Spanish models.
For information about making contributions to Stanford CoreNLP, see the file CONTRIBUTING.md.
Questions about CoreNLP can either be posted on StackOverflow with the tag stanford-nlp,
or on the mailing lists.
About
Stanford CoreNLP: A Java suite of core NLP tools.
stanfordnlp.github.io/CoreNLP/
Resources
Readme
GPL-3.0 license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
Stanford CoreNLP
Build StatusMaven CentralTwitter
Stanford CoreNLP provides a set of natural language analysis tools written in Java. It can take raw human language text input and give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize and interpret dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases or word dependencies, and indicate which noun phrases refer to the same entities. It was originally developed for English, but now also provides varying levels of support for (Modern Standard) Arabic, (mainland) Chinese, French, German, and Spanish. Stanford CoreNLP is an integrated framework, which make it very easy to apply a bunch of language analysis tools to a piece of text. Starting from plain text, you can run all the tools with just two lines of code. Its analyses provide the foundational building blocks for higher-level and domain-specific text understanding applications. Stanford CoreNLP is a set of stable and well-tested natural language processing tools, widely used by various groups in academia, industry, and government. The tools variously use rule-based, probabilistic machine learning, and deep learning components.
The Stanford CoreNLP code is written in Java and licensed under the GNU General Public License (v3 or later). Note that this is the full GPL, which allows many free uses, but not its use in proprietary software that you distribute to others.
Build Instructions
Several times a year we distribute a new version of the software, which corresponds to a stable commit.
During the time between releases, one can always use the latest, under development version of our code.
Here are some helpful instructions to use the latest code:
Provided build
Sometimes we will provide updated jars here which have the latest version of the code.
At present the current released version of the code is our most recent released jar, though you can always build the very latest from GitHub HEAD yourself.
Build with Ant
  1. Make sure you have Ant installed, details here: http://ant.apache.org/
  2. Compile the code with this command: cd CoreNLP ; ant
  3. Then run this command to build a jar with the latest version of the code: cd CoreNLP/classes ; jar -cf ../stanford-corenlp.jar edu
  4. This will create a new jar called stanford-corenlp.jar in the CoreNLP folder which contains the latest code
  5. The dependencies that work with the latest code are in CoreNLP/lib and CoreNLP/liblocal, so make sure to include those in your CLASSPATH.
  6. When using the latest version of the code make sure to download the latest versions of the corenlp-models, english-models, and english-models-kbp and include them in your CLASSPATH. If you are processing languages other than English, make sure to download the latest version of the models jar for the language you are interested in.
Build with Maven
  1. Make sure you have Maven installed, details here: https://maven.apache.org/
  2. If you run this command in the CoreNLP directory: mvn package , it should run the tests and build this jar file: CoreNLP/target/stanford-corenlp-3.9.2.jar
  3. When using the latest version of the code make sure to download the latest versions of the corenlp-models, english-models, and english-models-kbp and include them in your CLASSPATH. If you are processing languages other than English, make sure to download the latest version of the models jar for the language you are interested in.
  4. If you want to use Stanford CoreNLP as part of a Maven project you need to install the models jars into your Maven repository. Below is a sample command for installing the Spanish models jar. For other languages just change the language name in the command. To install stanford-corenlp-models-current.jar you will need to set -Dclassifier=models. Here is the sample command for Spanish: mvn install:install-file -Dfile=/location/of/stanford-spanish-corenlp-models-current.jar -DgroupId=edu.stanford.nlp -DartifactId=stanford-corenlp -Dversion=3.9.2 -Dclassifier=models-spanish -Dpackaging=jar
Useful resources
You can find releases of Stanford CoreNLP on Maven Central.
You can find more explanation and documentation on the Stanford CoreNLP homepage.
The most recent models associated with the code in the HEAD of this repository can be found here.
Some of the larger (English) models -- like the shift-reduce parser and WikiDict -- are not distributed with our default models jar.
The most recent version of these models can be found here.
We distribute resources for other languages as well, including Arabic models,
Chinese models,
French models,
German models,
and Spanish models.
For information about making contributions to Stanford CoreNLP, see the file CONTRIBUTING.md.
Questions about CoreNLP can either be posted on StackOverflow with the tag stanford-nlp,
or on the mailing lists.
About
Stanford CoreNLP: A Java suite of core NLP tools.
stanfordnlp.github.io/CoreNLP/
Resources
Readme
GPL-3.0 license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages