Natural Language Processing (NLP) - an introduction

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What is Natural Language Processing (NLP)?

Natural language processing (NLP) combines techniques from linguistics, computer science and artificial intelligence (AI) with the objective to computationally analyze, understand and process language data to create value and actionable insights from it.
Further subfields are natural language generation and the implementation of conversational interfaces. Recently, the application of AI techniques to NLP tasks has driven NLP research and use cases.

What does Natural Language Processing (NLP) transform and optimize?

With these methods, valuable information, which was not accessible yet, can be extracted from textual data. This data can be used to improve process quality and efficiency or provide whole new services. Email requests can be forwarded automatically to the appropriate department. This shortens response times and reduces insufficient processing of customer inquiries. One can automatically identify unsatisfied customers and product problems by analyzing social media data and take corresponding countermeasures at an early stage.
Chatbots can automate and thus optimize customer support, creating a new customer experience.

Natural Language Processing - Application Areas

Natural Language Processing already plays an important role in many industries and areas, such as healthcare, customer support, sales & marketing.

Application areas of Natural Language Processing (NLP)

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that focuses on enabling computers to understand and process human language.
The key word is data. Using data, systems are trained until they can generalize, abstract and judge unknown data.

Who doesn't know them? Digital Assistents and Chatbots like Siri, Alexa, Cortana, Facebook M and Google Assistant

Who doesn't know them? Natural Language Processing (NLP) is currently most widely used in Apple Siri, Amazon Alexa, Microsoft Cortana, Facebook M and most recently Google Assistant. Digital language assistants and chatbots are not only on the advance in everyday life. Today, they also play an increasingly important role in many companies.

Cost Efficiency through Natural Language Processing (NLP) Chatbots in Customer Support

Spam filters in email communication and speech recognition software with speech-to-text conversion are practical everyday NLP techniques that have been in use for many years. Meanwhile, Natural Language Processing (NLP) applications are becoming more and more diverse and increase rapidly.
Search queries, appointments, reservations, orders, complaints can already be made without a contact person or screen.
Across all industries, chatbots are increasingly used in customer support. Chatbots are digital assistants that can be used by all types of companies. Artificial intelligence (AI) enables this virtual assistant to communicate with customers in a natural way via telephone, chat or e-mail. They answer questions quickly and efficiently, make recommendations and give detailed instructions on how to dos. Nowadays, many customers expect chatbot NLP systems.
First-class, service-oriented customer service is a key differentiator and offers new opportunities and competitive advantages. In the age of digitalization this means for companies:

- increase in service quality
- increased customer satisfaction
- increased responsiveness
- 24/7 provision of information
- reduction of error rates
- cost efficiency

Intelligent analysis of text data with Natural Language Processing (NLP)

Natural Language Processing (NLP) is also used for text mining, especially linguistic text analysis and sentiment analysis. This involves the automatic evaluation of texts, news and blogs with the aim of identifying and analyzing moods (positive, negative or neutral), emotions (happy, angry, angry or sad), opinions, evaluations and tendencies.

Natural Language Processing (NLP) in Medicine

Natural Language Processing (NLP) is also used successfully in the healthcare sector. The aims are

- effective research
- improvement of clinical treatment standards
- automated evaluation of treatment result

The basis for this is the structured access to the content, i.e. the knowledge of the corresponding documents such as doctor's letters, pathology reports and clinical reports and the corresponding processing.

Natural Language Processing (NLP) for Sales and Marketing Strategies

Natural Language Processing (NLP) provides insight into the preferences and buying behavior of prospects and customers. Through a mechanical analysis of the mood in social media such as Facebook or Twitter, the attitude of customers towards the company, a campaign or a product can be determined in real time. This knowledge opens up completely new possibilities for the effective planning and implementation of marketing strategies and measures.

Natural Language Processing - Building Blocks

Which Natural Language Processing Building Blocks are suitable for your company?
Machine translation
Translating texts from one language to another is a use case that almost every business faces. If one wants to translate the company’s web presence with a great amount of content for example, this task is very costly and time consuming. Machine translation can help to automate this process.
While the first machine translation tools did only slightly more than replacing words from one language to another, which led to grammar mistakes and odd sentences, modern approaches internally build complete semantic representations of the text and then generate a new text in the target language. This generated text respects most grammar rules and reads naturally.
Using machine translation can reduce the workload of translation to checking and correcting the texts generated by the machine compared to writing completely new texts.
Text classification
Text or document classification is a general NLP task which enables many use cases. The goal is to decide for documents or texts to which of some predefined classes they belong. The most known application is probably spam filtering, which has been used for decades.
Another example is genre classification like the assignment of news articles to categories like political, sports, etc. or assigning research articles to relevant fields of research.
Sentiment analysis
Sentiment analysis is a special case of document classification. Reviews or support requests can be classified in categories like positive, negative, or neutral or happy, sad, angry, surprised and so on. In addition to that, entity recognition techniques can then be used to learn what this sentiment relates to.
Consider the review “Amazing phone. The battery lifespan is really disappointing though”. It expresses a positive sentiment overall, but a negative sentiment regarding the battery of the product. Insights about customer satisfaction and demand can be drawn from analyses like this.
Document summarization
Document summarization aims to give a short, human readable summary of longer texts or documents. There are two categories of text summarizers, those which use extraction of the most important sentences of a text and those which abstract the content of the text by generating a summary by themselves, once they have learned what the text is about.
The first approach of extraction is easier to implement and is used for example in the page summary shown by search engines. The second approach of abstraction mostly remains a theoretic possibility to date.
Text similarity
Finding out how similar two texts or documents are can enable use cases such as improved search results or recommendations. For example, on a news page, it might be a good idea to show the user related articles to the one he or she is currently reading in order to keep him or her engaged in the topic.
For search engines, full text search for the exact search terms is relatively easy. Yet, it won’t show the user results which do not include the search terms, but are still relevant to the searched topic. Providing search results with high similarity to the results obtained by full text search might be beneficial for the user.
Text clustering
Like text similarity analysis, clustering uses techniques to compare the similarity of texts. The aim is to then find groups of texts or documents with the same topic.
Spelling correction
To correct the spelling of words in a text can be a use case of its own – because no one wants to publish incorrectly written documents. It can also improve the performance of other NLP methods by cleaning the text of misspelled words (which would be unknown to the algorithm).
We are currently working on more building blocks for even more services in the future 😉

Use Cases

How companies benefit from Natural Language Processing building blocks.
Content management on the web
Let us assume that we operate a web shop with a great variety of products for which we provided rich descriptions in German. Using these descriptions, we can now generate short descriptions for an overview page using document summarization techniques.

To be able to reach a bigger market, we would also like to translate the entire website and content to English. We could hire an entire team to do the translations, which would take weeks to months, or we could automate the translation by using machine translation and have an English version of our shop ready by next week and then let one or two people proofread and correct the imperfections in the automatically generated texts over time.
Both NLP methods can save our business money and give us an advantage in the speed with which we can roll out new features.
Analytics of customer feedback
Our goal is to analyze customer feedback that we receive by email.
1. First, a spam filter will assure that our dataset includes only messages sent by actual customers.
2. Afterwards, we prepare the text data for the upcoming NLP tasks by correcting potential spelling mistakes.
3. Next, we run a sentiment analysis on the texts to detect positive and negative tones and then cluster the data such that we can analyze similar documents together.
4. Now we can run a text summarization over each cluster of messages to see find the main keywords. One possible outcome would be clusters summarized by “design, phone, looks” and “phone, battery, charging”.
5. Now we can analyze how satisfied the customers are with aspects of our product such as the design or the battery by comparing the positive and negative sentiments of the messages contained in each cluster.
For example, 90% of the consumers might like the design, but 52% complain about the battery. Such analytics generate valuable insights and enable informed decisions, e.g. where to allocate R&D budget for the next model.