10 Easy NLP & NLU Tools for Tagging Data

Natural language processing (NLP) is an extremely powerful form of machine learning (ML.) This technique allows machines to interact with human language. This tech exists in many popular forms of AI. For example, virtual assistants like Siri and Alexa were built with these technologies.

Natural language understanding (NLU) is a subset of these techniques. NLU focuses on using machines to understand the meaning of the written text. As a result, humans are able to ask machines questions in a natural way. It also allows users to create structured data from unstructured text. This includes product reviews, tweets, or support tickets.

NLP and NLU are powerful time-saving tools. However, in order to create effective models, you have to start with good quality data. This data is created by manually tagging large amounts of text. This is how data scientists teach the machine to categorize text. This process is extremely time-consuming, requiring a person to go through each example and apply tags.

Many tools exist to make this process less of a headache. Read on to learn about a few of the easiest and best NLP & NLU data tagging tools.

1. GATE (General Architecture for Text Engineering)

GATE is a free, open-source project that has been around for 15 years. During this time the project has created a number of powerful applications for language processing tasks. These programs help with labeling, processing, benchmarking, and much more.

GATE aims to take out many of the engineering challenges involved in creating a language processing workflow. Their primary product, GATE Developer, is a desktop Java application. They also provide an online service called GATE cloud.

2. Apache UIMA (Unstructured Information Management Applications)

UIMA is a full framework aimed at organizing language processing projects. This is an open-source project licensed under Apache’s license. UIMA works for a wide range of language processing tasks and can extract a huge variety of information types.

3. brat (Browser-Based Rapid Annotation Tool)

brat is another free tool for data labeling. It provides a browser-based experience for annotating text. It simplifies many NLP annotation tasks. brat is a popular tool for this sort of work and has a good support community. brat also provides integration with external resources including Wikipedia.

This tool allows companies to set up servers where many users can contribute annotations. However, this does require some server management and technical experience.

4. WebAnno

WebAnno is another web-based tool for NLP annotation tasks. This tool provides similar features to brat. For example, it enables groups to set up a server where many users can complete annotation tasks. WebAnno is a general-purpose annotation tool. It allows for many different roles and provides project management tools.

5. TagEditor

TagEditor is an open-source project built on the Python SpaCy library. This is a popular NLP library that provides powerful text processing. This tool adds a Graphical User Interface (GUI) to the annotation steps involved in using SpaCy.

This tool makes it far easier for programmers to create labeled datasets. However, it still requires a good understanding of programming.

6. Doccano

Doccano is a web-based annotation tool. It provides an attractive UI for a few essential annotation tasks. It is open-source and hosted on Github. Anyone can download and run it on their own server for free.

It is less adaptable than tools like brat and WebAnno. It also does not allow for more complex annotation tasks. Duccano does not support relationships between words and nested classifications. However, most production models cannot use this sort of data anyways.

What Docanno lacks in customization, it makes up for in simplicity. A simple display allows annotators to select text and choose from a list of keyboard shortcuts to apply an annotation.

Try it out now at their live demo.

7. Stanford CoreNLP

Stanford’s CoreNLP project is an academic project focused on cutting edge NLP research. They have developed a number of tools to simplify the NLP process. This includes a text annotation tool.

CoreNLP is a Java program, making it easy to run on most desktop computers. It provides advanced annotation features like dependencies. However, as a desktop application, it limits you to one user at a time. This makes large scale annotation projects difficult.

8. WordFreak

WordFreak is a desktop application that streamlines text tagging and annotation. It is a Java program and, therefore, you can run it on most desktop computers including Windows, Mac OSX and Linux. It provides similar functionality to tools like TagEditor. However, it is not integrated with modern NLP libraries and tools.

9. Bella

Bella is an NLP labeling tool written in JavaScript. It provides a simple web interface to label text data. However, it is targeted towards developers who are comfortable with tools such as docker, Node Package Manager (NPM), and the command line. It aims to help data scientists retrain NLP models. Additionally, it provides a GUI and a database to manage NLP datasets.

10. swivl

One of the most user-friendly options to label text for ML training is swivlStudio. Data labeling is the most labor-intensive process in ML. Many systems make it difficult to let end-users contribute to training. However, swivl provides an integrated system that simplifies this process. It integrates the entire workflow from data tagging through customer engagement.

What’s more, swivl includes a friendly user interface for users with no programming experience. It allows users to train models with a simple point and click interface. Guided data tagging tools make suggestions to decrease the annotator’s workload.

The other feature that distinguishes swivl is the focus on human-in-the-loop (HitL) design. This is a strategy that combines the advantages of human and machine intelligence. By continually adding feedback from users to the ML model, swivl can achieve far greater accuracy. swivlStudio is built around this principle. This ensures that models are continually improving and adapting.

swivl’s integrated workflow makes it easy for companies to use. They include a simple-to-use system that allows businesses to use NLP for customer success. swivlStudio is a polished solution for businesses of any size. These tools will greatly reduce time spent on customer service as your company grows.

Have you talked to Hoover yet? If not, click on the floating orange owl and start chatting! There you can learn more about how our natural language processing tools can streamline your business!

Join our mailing list today to receive a newsletter covering the latest trends in natural language processing and much more!

‍

‍