Natural Language Processing is a branch of Computer Science that deals with the understanding and processing of natural language, e.g. texts or voice recordings. The goal is for a machine to be able to communicate with humans in the same way that humans have been communicating with each other for centuries.
What are the Areas of NLP?
Learning a new language is not easy for us humans either and requires a lot of time and perseverance. When a machine wants to learn a natural language, it is no different. Therefore, some sub-areas have emerged within Natural Language Processing that are necessary for language to be completely understood.
These subdivisions can also be used independently to solve individual tasks:
- Speech Recognition tries to understand recorded speech and convert it into textual information. This makes it easier for downstream algorithms to process it. However, Speech Recognition can also be used on its own, for example, to convert dictations or lectures into text.
- Part of Speech Tagging is used to recognize the grammatical composition of a sentence and to mark the individual sentence components, such as a noun or a verb.
- Named Entity Recognition tries to find words and sentence components within a text that can be assigned to a predefined class. For example, all phrases in a text section that contain a person’s name or express a time can then be marked.
- Sentiment Analysis classifies the sentiment of a text into different levels. This makes it possible, for example, to automatically detect whether a product review is more positive or more negative.
- Natural Language Generation is a general group of applications that are used to automatically generate new texts that sound as natural as possible. For example, short product texts can be used to create entire marketing descriptions of this product.
What Algorithms do you use for NLP?
Most basic applications of NLP can be implemented with the Python modules spaCy and NLTK. These libraries provide far-reaching models for direct application to a text, without prior training of a custom algorithm. With these modules, part-of-speech tagging or named entity recognition in different languages is easily possible.
The main difference between these two libraries is the orientation. NLTK is primarily intended for developers who want to create a working application with Natural Language Processing modules and are concerned with performance and interoperability. SpaCy, on the other hand, always tries to provide functions that are up to date with the latest literature and may make sacrifices in performance.
For more extensive and complex applications, however, these options are no longer sufficient, for example, if you want to create your own sentiment analysis. Depending on the use case, general machine learning models are still sufficient for this, such as a Convolutional Neural Network (CNN). With the help of tokenizers from spaCy or NLTK, the individual words can be converted into numbers, which in turn the CNN can work with as input. On today’s computers, such models with small neural networks can still be trained relatively quickly and their use should therefore always be examined and possibly tested first.
However, there are also cases in which so-called transformer models are required, which are currently state-of-the-art in the field of Natural Language Processing. They are particularly good at incorporating contextual relationships in texts into the task and therefore deliver better results, for example in machine translation or natural language generation. However, these models are very computationally intensive and require a very long computing time on normal computers.
Fields of Application
The range of possible applications for Natural Language Processing is very broad and new ones are added at regular intervals. The most widespread use cases currently include:
- Machine Translation is the automated translation of a text into another language.
- Chatbots refer to an interface for automated communication between a human and a machine. It must be possible to respond to the human’s questions regarding content.
- Text Summarization is used to sift through large amounts of text faster by simply reading a suitable summary. The latest models, such as GPT-3, can also create different summaries with different levels of difficulty.
This is what you should take with you
- Natural Language Processing is a branch of Computer Science that tries to make natural language understandable and processable for machines.
- The Python modules spaCy and NLTK are the basic building blocks for most applications.
- It is one of the most current topics in machine learning and is experiencing many new innovations.
Explanation of Recurrent Neural Networks and LSTM models with example.
Other Articles on the Topic of Natural Language Processing
- In this article, you will find a list of free tools with which you can directly implement online Natural Language Processing tasks.