Stanford CoreNLP

Stanford CoreNLP Files

Stanford CoreNLP, a Java suite of core NLP tools

This is an exact mirror of the Stanford CoreNLP project, hosted at https://github.com/stanfordnlp/CoreNLP. SourceForge is not affiliated with Stanford CoreNLP. For more information, see the SourceForge Open Source Mirror Directory.

The interactive file manager requires Javascript. Please enable it or use sftp or scp.
You may still browse the files here.

Name	Modified	Size	InfoDownloads / Week
Parent folder
README.md	2024-04-19	1.3 kB	0
v4.5.7 - Constituency to Dependency Converter Upgrades source code.tar.gz	2024-04-19	51.5 MB	0
v4.5.7 - Constituency to Dependency Converter Upgrades source code.zip	2024-04-19	53.0 MB	1
Totals: 3 Items		104.5 MB	1

UD converter upgrades

Inspired by https://github.com/UniversalDependencies/docs/issues/717, although the work is not finished

Add an option to use the PTBCorrector, which fixes many (although not all) incorrect POS tags https://github.com/stanfordnlp/CoreNLP/commit/5e57eaba40897ee93b69ed3f11bda511f6b427d8
Treat sort of the same as kind of https://github.com/stanfordnlp/CoreNLP/commit/bc4acf11d165c4185121ff501c26b354a05a2477
en masse is flat https://github.com/stanfordnlp/CoreNLP/commit/cb338cd57fdcd9ef0fc1aa1fe2fa563d578fea15
dinna is an MWT https://github.com/stanfordnlp/CoreNLP/commit/1dd746cfea4f82e3b1c161bcc95c457f0d8a2618
Use AUX as the POS in the converter when appropriate https://github.com/stanfordnlp/CoreNLP/commit/30f2f8e7d92492a152dd5fc8b85327860b44cc2a
Fix (heh) all but and whether or not https://github.com/stanfordnlp/CoreNLP/commit/25136768ee22e5431051d756c4c63c41af00de99
Dependency dep -> ccomp for fronted say verbs https://github.com/stanfordnlp/CoreNLP/commit/a76a854ce249ae028eec010b1a48d68748d59a61

Parser evaluation improvements

Include the F1 scores of each tree when scoring a constituency dataset https://github.com/stanfordnlp/CoreNLP/commit/2725b06fa96400e9c25e314b5e16b18720764ab2

Source: README.md, updated 2024-04-19

You Might Also Like

Gen AI apps are built with MongoDB Atlas Icon

Gen AI apps are built with MongoDB Atlas

The database for AI-powered applications.

MongoDB Atlas is the developer-friendly database used to build, scale, and run gen AI and LLM-powered apps—without needing a separate vector database. Atlas offers built-in vector search, global availability across 115+ regions, and flexible document modeling. Start building AI apps faster, all in one place.

Start Free

Simple, Secure Domain Registration Icon

Simple, Secure Domain Registration

Get your domain at wholesale price. Cloudflare offers simple, secure registration with no markups, plus free DNS, CDN, and SSL integration.

Register or renew your domain and pay only what we pay. No markups, hidden fees, or surprise add-ons. Choose from over 400 TLDs (.com, .ai, .dev). Every domain is integrated with Cloudflare's industry-leading DNS, CDN, and free SSL to make your site faster and more secure. Simple, secure, at-cost domain registration.

Sign up for free

Recommended Projects

Stanza
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Stanza is a Python natural language analysis...
Medical Treebank
This project hosts linguistic annotations and guidelines for clinical text. We plan to include several types of annotation (Token, POS and Parse) in WordFreak format on clinical notes originally from the i2b2/VA NLP challenges. The guidelines are copyrighted, but free for the community to use....
PHP Client For NLP Cloud
NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, dialogue summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases...
OLiA
MOVED TO https://github.com/acoli-repo/olia. The Ontologies of Linguistic Annotations (OLiA) provide an OWL/DL taxonomy of data categories as a reference for linguistic annotation (OLiA Reference Model), plus OWL/DL models for a large number of annotation schemes (OLiA Annotation Models) and...
OPTIMA cidoc-crm Semantic Annotation
The semantic annotation system OPTIMA is the result of Andreas Vlachidis PhD work, (supervised by Prof. Douglas Tudhope, University of Glamorgan, UK). OPTIMA performs the NLP tasks of Named Entity Recognition, Relation Extraction, Negation Detection and Word Sense Disambiguation using...