Companies generate vast amounts of unstructured data every day in almost all business areas. To enable them to make informed decisions based on this, they need to classify, analyze and evaluate the data. The importance of this is demonstrated, for example, by the flood of data that companies receive via customer support tickets. On average, they process 777 tickets per month (Study by Zendesk). In order to learn from customer experiences, it is essential to thoroughly evaluate this data. This is not possible manually.
This is where NLP tools come into play. NLP stands for Natural Language Processing.
With an NLP toolkit, companies can develop their own AI that processes and evaluates unstructured data in an automated way.
This can - to return to our example - sort support requests by topic and then analyze them. In this way, companies can uncover the processes they need to optimize.
We show which NLP open source tools are available on the market, how you can use them and profit from them. And: We explain which NLP toolbox is particularly suitable for setting up your own document processes.
This article was written in German, automatically translated into other languages and editorially reviewed. We welcome feedback at the end of the article.
Definition of NLP Tools
NLP tools are applications and software systems that enable natural language processing and analysis by machines. They form the basis for many modern technologies and applications based on text understanding, language analysis and communication with computers.
NLP software tools are designed to translate human language into a form that computers can understand and process.
Application areas of NLP Tools
NLP Toolkits are used in different areas like
- Text classification, where they can automatically classify texts into categories,
- Sentiment analysis to detect the mood or opinion in texts, as well as
- Named Entity Recognition to identify entities such as people, places, and organizations.
Developers use NLP tools among others
- to create intelligent chatbots that can have natural conversations with users,
- for automated translation services that translate texts between different languages, as well as
- for summarizing programs that put long texts into more compact forms.
In practice, NLP tools play an increasingly important role today in areas such as data analysis, customer interactions, search engine optimization, and automated information processing. They help make natural language and communication accessible to machines.
NLP Tools - 12 Classic Use Cases
In practice, companies can use NLP software tools to develop their own AI for the following functions:
Analyzes the emotional tone in texts to identify moods such as positive, negative, or neutral.
Named Entity Recognition (NER)
Recognizes and extracts named entities such as people, places, organizations, and dates from text.
Automatically assigns texts to categories, such as emails into spam and non-spam.
Translates text from one language to another to enable communication across language barriers.
Automatically generates texts, such as product descriptions or articles, based on given inputs or context.
Question and answer systems
Extracts answers from texts to provide actionable information in response to posed questions.
Engages in conversations with users to assist them with inquiries or problems.
Voice command recognition
Recognizes spoken commands and converts them into actions, e.g. voice assistants like "Hey Google".
Creates compact summaries of longer texts to highlight relevant information.
Speech analysis in social media
Analyzes public opinions and trends on social media to gain insights into user sentiment.
Spell check and grammar check
Identifies and corrects errors in written text to improve quality of communication.
Converts text to spoken language, which is important for accessibility and multimedia content.
NLP Toolkit - 8 important Benefits
Companies benefit from developing their own AI using NLP tools in several ways:
Improved customer service
Companies can use AI-powered chatbots to provide customer service around the clock. These bots can answer customer queries quickly and provide solutions to common problems.
Personalized marketing campaigns
By analyzing customer reviews and social media posts, companies can better understand customer sentiment and develop personalized marketing campaigns that target customer needs and interests.
Efficient data analysis
NLP models can analyze unstructured data, such as texts from social media, and extract relevant information. This helps companies gain insights into trends, opinions, and market developments.
Companies can use NLP to automatically generate reports and analyses. This saves time and resources that would otherwise be spent on manual reporting.
Efficient content creation
NLP can assist in text content creation by summarizing information, paraphrasing text, and analyzing relevant sources to generate high-quality content.
Error detection and quality assurance
AI models can check texts for spelling errors, grammar problems, and inconsistencies to ensure the quality of documents and communications.
Detailed market analysis and competitive analysis
NLP can help to gather relevant information about the market and competitors in order to make informed business decisions. In this way, companies gain a competitive advantage.
Early detection of problems
By monitoring customer feedback and social media, companies can identify potential issues early and respond proactively to protect their reputation.
10 NLP Open Source Tools that Companies should know about
Companies find a variety of NLP open source tools on the market. Which one is the right one is decided by the concrete use case. The following NLP open source tools are particularly common:
TensorFlow is a widely used deep learning framework that can also be used for NLP tasks. It offers a wide range of tools and models, including pre-trained models for text classification and translation. TensorFlow is particularly suitable for developers who want to create customized NLP models.
PyTorch is another popular deep learning framework that is heavily focused on flexibility and usability. It can be used for various NLP tasks, including text classification, named entity recognition, and machine translation. PyTorch is well suited for researchers and developers who prefer a simple, dynamic framework.
NLTK (Natural Language Toolkit)
NLTK is an NLP toolkit based on Python for natural language processing. It provides features such as tokenization, POS tagging, stemming, and sentiment analysis. NLTK is well suited for educational purposes and basic research.
spaCy is an efficient NLP library that is fast and accurate. It provides tokenization, named entity recognition (NER) and dependency analysis. It is well suited for industrial applications and fast text processing.
Gensim specializes in topic modeling and vector space modeling. It can analyze large text corpora and extract topics in documents. It is particularly suitable for processing large amounts of text data.
The Stanford NLP library is an intelligent solution with a wide range of NLP functionalities, including tokenization, POS tagging, NER, and parsing. It is known for its accuracy and is available in several languages.
Apache OpenNLP is a collection of Java-based NLP tools with tools like tokenization, sentiment analysis and chunking. It is well suited for Java developers and integration into Java projects.
TextBlob is a simple NLP library based on NLTK and Pattern. It offers features like sentiment analysis and POS tagging in a user-friendly interface. TextBlob is well suited for beginners in NLP.
Stanford CoreNLP is a powerful tool that supports multiple NLP tasks in over 50 languages. It offers a wide range of features such as NER, sentiment analysis and coreference resolution. It is suitable for a wide range of applications.
MALLET (MAchine Learning for LanguagE Toolkit)
MALLET is an intelligent platform that focuses on machine learning in the NLP domain, including topic modeling and classification. It is especially useful for those who want to develop advanced NLP models.
Advantages and Disadvantages of NLP Tools
The NLP open source tools mentioned have these advantages and disadvantages:
|TensorFlow||- Supports NLP through TensorFlow Text||- Entry can be steep|
|- Large community and resources||- Complexity for some tasks|
|- Supports neural networks||- NLP-specific abstractions are sometimes missing|
|PyTorch||- Flexible and dynamic||- Smaller standard library compared to TensorFlow|
|- Enables rapid prototyping||- Possibly less optimized models|
|- Popular in research||- Documentation not always as comprehensive as with other|
|NLTK||- Comprehensive collection of word processing functions||- Some parts may be obsolete|
|- Large community and extensive resources||- Performance possibly slower than with newer tools|
|SpaCy||- High processing speed||- Less configurable compared to other tools|
|- Prefabricated models for different tasks||- Possibly less adaptable to specific scenarios|
|- Simple API and documentation||- More limited choice of prefabricated models|
|Gensim||- Powerful tools for text vectorization||- Focus is more on topic modeling than NLP per se|
|- Implements popular embedding algorithms||- Less versatility compared to more comprehensive tools|
|Stanford NLP||- Rich set of NLP functionalities||- No easy installation and configuration|
|- Supports many languages||- Resource intensive and slow|
|OpenNLP||- Solid foundation for NLP tasks||- Active development possibly restricted|
|- Relatively easy integration into Java applications||- Less advanced features compared to others|
|TextBlob||- Simple API for basic NLP tasks||- Limited support for more complex tasks|
|- Well suited for beginners||- Possibly less powerful than specialized tools|
|CoreNLP||- Comprehensive collection of NLP tools||- no easy installation|
|- Supports a wide range of languages||- Memory and resource intensive|
|Mallet||- Focused on topic modeling||- Less broad NLP functionalities|
|- Good choice for text categorization||- Possibly less user friendly|
Konfuzio as an efficient NLP Tool for building your own Document Processes
Konfuzio is a powerful and flexible NLP toolkit that enables organizations to develop an AI for building their own document processes. It enables them to automate any form of data capture, analysis and reporting. For this purpose, the Konfuzio SDK has these functions and features:
The SDK enables the extraction of text from various types of documents, including PDFs and images. It uses optical character recognition (OCR) to convert text into machine-readable content.
Using NLP, the SDK can automatically identify important entities such as names, dates, and locations in documents. This helps in the classification and organization of information.
The SDK enables automatic classification of documents into predefined categories. This enables companies to organize and process documents more efficiently.
It recognizes specific keywords or phrases in documents. This can be used to specifically extract or tag certain information.
Companies can combine the functions of the SDK in customized workflows. This enables the automation of complex document processes, adapted to individual requirements.
The SDK can check texts for certain patterns or criteria and thus ensure the quality of the data in the documents.
Integration into existing systems
Developers can seamlessly integrate the SDK APIs into existing software and applications to extend functionality.
The functions of the SDK can be applied to documents in real time, which is particularly advantageous in situation-critical applications.
The SDK can be scaled to handle large volumes of documents to meet enterprise needs.
NLP tools are software programs that analyze, understand and process human speech with artificial intelligence in digital form. The tools play a significant role in transforming written or spoken text into structured data. An NLP toolbox supports machine translation, text analysis, sentiment analysis, and the creation of interactive chatbots, among others. Well-known NLP tools include libraries such as NLTK and SpaCy, and advanced AI models such as Konfuzio.
There are numerous NLP open source tools such as NLTK, SpaCy, Gensim and Transformers. They offer versatile functions, for example for tokenization, POS tagging and named entity recognition. The available tools support NLP development and research through their flexibility and adaptability. Companies can use them to develop their own AI.
The Konfuzio SDK is particularly suitable for building your own document processes. The NLP Toolkit provides efficient text processing, entity and keyword extraction, and sophisticated language understanding. Its powerful features optimize document analysis and enable precise processing of unstructured data.