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Nowadays a lot of information are in the text format (books, documents, articles, social media posts, messages, reviews, chat’s conversation, description, website info etc.). Those files contains a lot of valuable information that can support business activities. Insights from text data could be extracted using NLP applications.

Since artificial intelligence (AI) allow modeling of nonlinear cases, it have turned into a very popular and useful tool for solving many different problems such as pattern recognition, machine translation, anomaly detection, decision making, computer vision and many other. It allows to use artificial intelligence algorithms such as neural networks in many areas. Below, in this article we will describe applications of AI based Natural Language Processing (NLP).

neural network

Natural Language Processing (NLP) applications

Natural Language Processing is a suit of techniques and algorithms that give computers the ability to read, understand and derive meaning from the human languages. The challenge with NLP is that computers are built to understand programming languages, which are explicit and highly structured, while natural language is anything but explicit and it’s structure is often not so rigid. Due to those factors, it has always been difficult for machines to grasp the context of natural language. Creating elaborate sets of rules that was started in 1950s could only work for narrow problems and with limited success. But with the help of Machine Learning computers can now cope with the uncertainty of human language.

Some of the most common NLP applications

Sentiment analysis

sentiment analysis

Currently most of the companies monitor their online presence. Consumers talk about brands on social media platforms, voicing both their positive and negative opinions about them. This is a great opportunity for companies, but also a great threat. Not acting fast enough to ever changing public opinion may have huge impact on company bottom line. Sheer amount of posts published everyday on social media platforms makes it impossible for company employees to monitor and react to everything. Luckily NLP helps with such problems with algorithms tailored for sentiment analysis. They can analyze in seconds multitude of posts and classify their polarity as positive, negative or neutral. Such speed enables near real-time analysis of social media. Common example of this is analyzing streams of tweets to detect shifts in the public perception of given brand.

It is worth noting that there is no silver bullet for sentiment analysis, there is no universal sentiment detector. Creation of a detector is a tailored process which requires annotated datasets on which algorithms can learn what is considered positive or negative in given business. Sentiment analysis is commonly used in customer service and marketing.

Text Classification and Categorization (on of the most popular NLP applications)

Text Classification and Categorization could be used in many applications. Examples of those applications are web searching (search engines), language identification, information filtering and readability assessment.

Text categorization and classification can bring automatization and simplification to your applications and companies operations. Classifying large textual data helps in standardizing the platform, make search easier and relevant, and improves user experience by simplifying navigation.

Text summarisation

Length of many documents and articles is an obstacle in finding relevant information fast and efficiently. Often documents don’t indicate clearly what can be found in them, there is no summary written. NLP can help with such problems and automatically generate such summaries. There are two approaches to this task. First – extraction, works with the use of algorithms such as TextRank (related to Google’s PageRank), to find and extract the most important sentences or even paragraphs that capture the essence of the document. Second – abstraction, works a bit different and after finding the essence of a given document, it tries to write a summary and not merely return most important parts of original text. This approach is most similar to what human would do, but it is also a lot more trickier to implement. It is currently still under active research. In most cases extraction-based approach is used in current systems. Text summarisation algorithms are often coupled with search engines so that apart from the full result also a short summary can be seen.

Extraction-based approach doesn’t require training data, but it is a good idea to have some example documents to test and tweak algorithm parameters for the most desirable output. In case of abstraction-based approach training data is required, full documents paired with their summaries – the more the better, so that the algorithm can have enough examples to learn from.

Text summarisation is commonly used in law, medical and HR companies.

Named Entity Recognition

Internet is a rich source of data, mainly textual data. But making use of huge quantities of data is a time consuming tasks. NLP can help with this problem through the use of Named Entity Recognition systems. Named entities are terms that refer to names, organisations, locations, values etc. NER annotates texts – marking where and what type of named entities occurred in it. This step significantly simplifies further use of such data, allowing for easy categorisation of documents based on what entities are present in them, for example into texts about competitors, new legislation or company’s own brands. Named Entity Recognition is of great help for other NLP tasks, making it easier to analyze sentiment only of the opinions concerning given company and not all opinions online or improving automatically generated summaries. On its own NER can be helpful in analyzing popularity of brands, making it easy to monitor the frequency of their mentions online.

There are available tools for Named Entity Recognition which work great for general use cases, but when some more niche entities are of interest, it is necessary to collect and annotate data and train the model for specific cases.

Named Entity Recognition is commonly used in brand monitoring, journalism and finances.

Optical character recognition

Not all information online is presented in textual form. There is multitude of infographics, invitations, posters, document scans etc, that are pictures with the text embedded in them. This fact makes information retrieval and analysis problematic. Fortunately also here NLP can help, for such cases optical character recognition (OCR) algorithms are used. Such algorithms are trained to detect and recognize shapes of letters and numbers and return them in the form of text that can be further analyzed using other text processing techniques.

Of course such algorithms are not perfect, pretrained models are able to detect letters that are clearly visible and in common fonts. For cases with noisy pictures or fancy fonts training a model may be a better solution, even though a huge training set is required, but even then model will not be error-prone.

Optical character recognition is most often used for digitalization of printed documents.

Machine Translation

machine translation

Today, AI technology has a big potential and automates work in various industries and actions. Machine Translation can be called also as a private machine translation engine. That is, the case where translator (human) has his/her own machine translation engine at his/her disposal for translations. Adaptive machine learning translation engine is self-learning. This kind of algorithm adapts and learns in real time as segments are translated using the software.

All changes are therefore made instantly in the text which makes the text more coherent and adapted to customised analysis. Data is the key to this system, as it drives the analysis. To sum up, if the subject matter to be analysed increases, translations will approximate human translations in terms of quality and fluidity.

Summary – NLP applications

The whole idea of technology is to make life simpler. In this article, we described Natural Language Processing applications that could be implemented using AI and Machine Learning techniques. As we showed, there is a lot of NLP based applications that do sentiment analysis, text summarization, named entity recognition and optical character recognition. Those application can solve real business problems and cover business needs. However implementing such solutions without experience and specific knowledge could be very challenging. You could waste your time and resources on the project that could result in what you did not expect. Team of experienced NLP engineers could help you with challenges and deliver solution you want. Ping us a message if you have a need in NLP application development.

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