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Twitterner

TwitterNER is an open-source named-entity recognition system for noisy Twitter text, built for the WNUT 2016 task. Its Python interface accepts tokenized text and returns entity spans with labels; the repository also contains notebooks and preprocessing materials for the project.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Twitterner — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you need a repository-based Python workflow for extracting entities from noisy Twitter-style text and can work with its documented dependency and vector setup.

Who it is for

  • Developers adding named-entity extraction to Python workflows for Twitter-style text.
  • Researchers or practitioners exploring the WNUT 2016 noisy-text recognition task and its associated notebooks.

Documented uses

  • Identify labeled entity spans in tokenized social-media posts.
  • Extract locations and other named entities from Twitter-style text for downstream processing.
  • Review the project’s WNUT 2016 model experiments and preprocessing materials.
Before you choose

Requirements & limits

The repository is public and specifies the GPL-3.0 license.

  • The interface expects tokenized input; the README uses its Twitter text tokenizer before calling the entity extractor.
  • Setup includes downloading Stanford Twitter GloVe vectors in addition to installing the requirements.
  • The README points to an API package within NoisyNLP and to Run Experiment.ipynb for usage details; it does not describe a hosted service.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up and extract entities from a sample post

Install the documented dependencies and use the Python interface to identify entity spans in tokenized Twitter-style text.

Before you start

  • Python environment with pip
  • Repository checkout
  • Requirements listed in requirements.txt
  • Stanford Twitter GloVe vectors downloaded and unpacked into the data directory

Steps

  1. Install dependencies with the command below.

    Documented command
    pip install -r requirements.txt

  2. Download and unzip glove.twitter.27B.zip in the data directory as shown in the README.

  3. From NoisyNLP, import TwitterNER and tokenizeRawTweetText, then initialize TwitterNER().

  4. Tokenize a post and pass the resulting tokens to ner.get_entities(tokens) to obtain labeled spans.

Expected outcome

The README example returns token spans labeled LOCATION for Chicago and Florida.

Guide limitations

  • The example demonstrates location extraction on one sample; it does not establish broader accuracy or coverage.
  • The README points to Run Experiment.ipynb for API usage and does not document a hosted endpoint.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
17 October 2026
Original website title
napsternxg/TwitterNER · Official homepage title · retained in its source language
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