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I saw the labelme tool and was wondering if there is a similar tool for labelling short documents? Preferably in Python.

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  • $\begingroup$ Could you provide some more explanation about the tool and what you are searching for? $\endgroup$
    – Archie
    Commented Oct 12, 2017 at 19:09

3 Answers 3

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There are many. A couple of the most popular:

  • ML-Annotate - Supports binary, multi-label and multi-class labeling.
  • TagEditor - A Windows application that uses spaCy
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Yes, there are several tools available for annotating short documents in Python. Some popular options include:

  • Prodigy: Prodigy is an annotation tool that allows you to label short documents and other text data quickly and easily. It is written in Python and has a simple, user-friendly interface. You can use Prodigy to label text data for tasks such as named entity recognition, sentiment analysis, and topic classification.

  • Brat: Brat is an open-source annotation tool that is widely used for annotating short documents and other text data. It is written in JavaScript and has a web-based interface, but you can also use it from Python using the brat_client library.

  • Doccano: Doccano is another open-source annotation tool that is written in Python and has a web-based interface. It is designed specifically for labeling text data and supports a wide range of annotation tasks, including named entity recognition, part-of-speech tagging, and sentiment analysis.

  • scribe: scribe is a Python library that provides tools for labeling text data. It includes a command-line interface and a web-based annotation tool, and it is designed to be easy to use and customize.

Hope this helps!

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Prodigy: Prodigy is a powerful annotation tool that can be used to label short documents and other text data. It has a user-friendly interface and a range of features that make it easy to label text data quickly and accurately. Prodigy can be used in Python through its API.

spaCy: spaCy is a popular natural language processing library that includes tools for labeling and annotating text data. It has a range of functions for tokenizing, tagging, parsing, and visualizing text data, and it can be used in Python to label short documents and other text data.

NLTK: The Natural Language Toolkit (NLTK) is a comprehensive library for working with human language data. It includes tools for tokenizing, tagging, and labeling text data, as well as functions for working with a wide range of linguistic data. NLTK can be used in Python to label short documents and other text data.

GATE: GATE (General Architecture for Text Engineering) is a powerful platform for building and deploying text processing systems. It includes tools for annotating and labeling text data, and it can be used in Python to label short documents and other text data.

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