How is NLP different from machine learning
Natural Language Processing (NLP): functions, tasks and areas of application
Natural Language Processing (NLP) is an artificial intelligence method that enables computers to understand human natural language. Areas of application are e.g. chatbots, text mining and digital assistants such as Alexa or Siri.
Natural language processing is not an easy task of machine learning, because languages and relationships are often complex and, due to their ambiguity, not easy for the computer to understand.
This article provides an introduction to natural language processing and where it can be used.
- What is natural language processing?
- What are the tasks of natural language processing?
- What are the uses of natural language processing?
- What tools are there for natural language processing?
- What are the challenges in language processing?
Key facts at a glance:
- Natural Language Processing is aArtificial intelligence branch and deals with theanalysis, theunderstandingand theGenerationof natural language
- The functions include theSentiment analysis, part of speech tagging, named entity recognition and speech recognition
- In addition to natural language processing, there are also areasNatural Language Understanding (NLU) andNatural Language Generation (NLG)
- NLP helps Identify diseases, the Improve customer service or Classify texts
- In addition, askSpam filter, word processing, text mining, call forwarding using IVR systems andEmail routing further areas of responsibility of NLP
What is natural language processing?
Natural Language Processing (NLP), also known as computational linguistics or linguistic data processing in German, is a branch of artificial intelligence and machine learning that deals with the analysis, understanding and generation of words and sentences (natural language). With natural language processing, we canPeople communicate naturally with computersso that they understand our human language.
Natural language processing can do bothspoken and written language recognize, analyze and extract the meaning for further processing. To do this, it is necessary that not just individual words, but complete textual contexts and facts are understood. To recognize these text meanings,one collects huge amounts of data in advance and derives patterns from them using algorithms. Machine learning and other big data technologies as important drivers of natural language processing contribute to this.
In the beginning, the term natural language processing only referred to the readability of computer systems. Other aspects of linguistics are now being taken into account. To theSubcategories of Natural Language Processing belongNatural Language Understanding asNatural Language Generation. The differences and meanings of these terms are shown below.
Process of a natural language processing workflow
In order for Natural Language Processing to be successful in practice, individual important steps must be observed in advance. In addition to preprocessing the data, feature creation, modeling and evaluation play a decisive role. The following graphic illustrates the process of a natural language processing workflow:
What are the differences between NLP, NLU and NLG?
In the context of natural language processing, terms appear again and again that are similar but nevertheless differ. Because these terms overlap, they are often confused in practice. Nevertheless, the following differences in the terms should be noted:
- Natural Language Processing (NLP) = Natural language processing:
In the context of natural language processing, unstructured language data is converted into a structured data format. This should enable machines to identify and understand language and text in order to subsequently generate relevant answers.
- Natural Language Understanding (NLU) = Understanding natural language:
Natural Language Understanding deals with the pure understanding of natural language. NLU primarily focuses on machine reading comprehension. For this purpose, grammar and context are mainly analyzed in order to identify the meaning and significance of a sentence.
- Natural Language Generation (NLG) = Generation of natural language:
Natural Language Generation is about the concrete generation of text modules. This means that NLG mainly deals with the construction of texts. On the basis of an existing data record, a machine can construct texts in different languages.
What are the tasks of natural language processing?
The analysis of human language is extremely challenging for machines. Texts and language data are full of ambiguity and irregularities. Various Natural Language Processing (NLP) tasks break human language down into elements that a computer can understand. Natural language processing tasks include:
- voice recognition: Speech recognition, deals with the possibility of producing reliable text data from speech data. Speech recognition is required for all applications that deal with the analysis of voice commands. Because people communicate in a wide variety of ways, speech recognition poses a major challenge. Here, differences in speech type, speed, clarity or different accents and intonation as well as accents and incorrect grammar have to be taken into account.
- Part of speech tagging: This function of Natural Language Processing is also called grammatical tagging. The aim is to correctly identify and understand certain words or pieces of text depending on their use or context. For example, thanks to Part of Speech Tagging, it is possible to identify the word “make” differently in the following sentences. On the one hand “make” can be perceived as a verb (I can make a smoothie) and on the other hand as a noun (What make of watch do you own).
- Named Entity Recognition (NEM):NEM is concerned with the task of recognizing words or text modules in different contexts. For example, thanks to NEM, it is possible that the term “Cologne” is identified as a place and “Jonas” as the name.
- Sentiment analysis:The sentiment analysis makes it possible to identify and interpret attitudes, emotions and preferences within text passages. In addition, different forms of speech such as sarcasm or irony can be extracted and recognized.
- Machine translation:Thanks to the machine translation function, texts can be automatically translated into another language. Thanks to NLP algorithms, it is possible to reproduce extensive texts in different languages. In addition, modern translation programs can record the language in which the text is entered so that the desired translation is then carried out automatically.
- Document summary:This function enables the user to automatically generate summaries of large texts. Especially in areas where there are huge amounts of text, it is helpful that the essential findings are concisely summarized.
- Speech-to-text and text-to-speech conversion:As part of this function, Natural Language Processing is used to convert text into acoustic speech output. In addition, this process can also be done the other way around, so that spoken language is transformed into written text.
What are the uses of natural language processing?
The processing of natural language is a driving force behind artificial intelligence. This results in countless modern applications in everyday life. Below are some examples that are possible thanks to Natural Language Processing:
The pioneers of virtual assistants include Siri (Apple) and Alexa (Amazon). These use speech recognition to filter out commands and wishes from spoken language. In addition, the generation of natural language plays an important role with regard to the corresponding responses of the system.
Identification of diseases
Thanks to natural language processing, it is possible for diseases in medicine to be recognized at an early stage. Based on electronic health data and the identification of a patient's spoken language, the system can detect deficiencies in diseases such as cardiovascular disease, depression or schizophrenia.
Analyze customer opinions
Natural language processing can be used within social networks to identify customer moods and anomalies. Relevant data such as comments on a product or service are extracted in order to obtain information and background information on the mood of a customer. This enables companies to act early and minimize customer churn.
Natural language processing is also used to summarize huge databases in the form of texts. As a result, large passages of text can be shortened to the essential content, which saves time. To do this, one uses semantic inferences and natural language generation (NLG) to give existing summaries a useful context.
A spam filter works on the basis of NLP processes, so junk mail can be found. If you look at the individual subject lines of those emails that ended up in the spam folder, you can quickly see a similarity.
Naturally generated e-mails are compared with typical words from spam e-mails. In this way, the system gradually learns to differentiate between a spam and meaningful email.
Speech-to-test conversion can also be used in everyday life to convert mailbox recordings from missed phone calls so that they can be forwarded as text messages to your e-mail inbox. Above all, this has the enormous advantage that employees do not have to act time-dependent and still receive important messages, even if they come in by phone.
But even if you use the integrated search bar of a website, various NLP methods are used for search, topic modeling or content categorization.
Word processing in customer service
In the area of a request from a customer, a natural language processing system can help to identify the concerns and the mood (sentiment analysis) of the customer. As a result, various decisions can be derived as required, such as:
- At Standard inquiries with a problem that can be solved quickly, the customer continues to communicate with the machine or the computer. The purpose of this is to ensure that the customer receives a response quickly and that the company can use valuable resources such as employees for other purposes.
- However, is it a complex question, the system forwards the request to an employee. This guarantees the customer a personal and individual processing of the problem in the further course.
Telephone calls can also be used for natural language processing. A system transcribes the audio files and generates a text. An algorithm then analyzes this text and classifies phone calls according to topic.
This classification enables the company to offer the customer different solutions for different problems, which creates a strong customer focus.
Text classification of emails
For many companies, the mass of information in the area of e-mail traffic leads to problems. However, natural language processing enables companies to classify e-mails in such a way that the company has a better overview.
NLP technologies are able to scan various e-mails and then determine the urgency of this e-mail. This means that e-mails with a high level of urgency receive a special priority and are quickly forwarded to customer service staff.
The system mainly scans words like “answer”, “confirm”, “urgent” and learns from this process to distinguish urgent e-mails from less urgent ones. Different concerns such as termination, change of address or complaints can be differentiated.
Call forwarding with IVR systems
You've probably already called customer support and had to say a certain word to be forwarded to a specific department. Most likely, you spoke to an interactive voice response system that processed your request. These IVR technologies recognize spoken language and convert these phrases into text modules.
Since the customer can determine a certain selection of his concerns in advance, he does not have to first listen to a number of the following options and then choose from them.
Because IVR systems act with a quick, solution-oriented approach that asks the customer directly about their specific problem.
Huge companies like American Airlines were able to exploit enormous potential within customer service. By revising their existing IVR system, the number of incoming customer calls was increased by 5% and at the same time costs of several million euros were saved.
E-mail routing - using the example of Uber
In order to provide the customer with the best possible end-to-end experience, Uber aims to make customer support easier and more accessible. Uber uses five different communication channels to process the huge number of contact requests. With hundreds of thousands of inquiries from around 400 cities worldwide every day, this represents a significant challenge for the company.
But to meet this challenge, Uber has developed its own system: COTA. COTA is an acronym made up of the terms Customer Obsession Ticket Assistant. It is a tool that uses machine learning and natural language processing techniques to improve customer support.
COTA enables the company to solve customer inquiries quickly and efficiently, which means that more than 90% of incoming inquiries can be processed automatically. Most importantly, this helps customer service representatives improve the speed and accuracy of their work. Ultimately, the customer benefits from an improved customer experience.
What tools are there for natural language processing?
For speech processing, there are a few tools and applications that can help. In the following, you will find out which common tools and methods are available in the field of natural language processing.
Python and Natural Language Toolkit (NLTK)
The well-known programming language Python offers countless tools for dealing with NLP-specific tasks. Some of these can be found in the Natural Language Toolkit (NLTK). This represents aOpen source collection of programs, libraryheken and other resources for creating NLP programs represent.
The Natural Language Processing Toolkit contains some tools for handling the functions listed above and areas of application. There are also libraries for subtasks such as word segmentation, sentence parsing or stemming and lemantization to understand a text.
Statistical NLP, machine learning, and deep learning
The first NLP applications included hand-coded, rule-based systems. Although these could fulfill certain tasks, they were not useful for a wide range of tasks.
Statistical NLP combines computer algorithms with machine learning as well as deep learning models to automatically extract and classify text and language data and then to assign a possible meaning to the elements with a statistical probability.
In the meantime, deep learning models offer the possibility of creating NLP systems that can be used during theLearning the process independently. This allows these systems to develop increasingly precise solutions over time.
What are the challenges in language processing?
Natural language processing is not an easy problem for artificial intelligence, it is mainly due to the nature of human language, which is very complex.
There are many rules and relationships that make it difficult for computers to understand and interpret them correctly. An example is sarcasm, a problem that is extremely difficult for the computer to understand.
Of course, on the other hand, there are also many very simple tasks, such as words or recognizing the plural, that a computer can learn.
To understand natural language, the computer must understand both words and the concepts and rules behind them. For us humans this is often very easy, but for Natural Language Processing it is the big challenge.
Accordingly, challenges arise mainly in the following areas:
In practice, it is often difficult to assess the quality of the model results.For example, interpreting the classification of emotions is far easier than judging summaries. It is therefore important to determine a quality standard, especially in the initial phase of a project. On the one hand, this should be practicable and, on the other hand, it should reflect the problem to be solved.
In addition, questions about the model performance should be clarified. For example, a model that calculates results in large time intervals has different properties than a model that has to answer queries within tenths of a second.
Pre-trained models (transfer learning) have the property that they often perform well in different tasks. This often leads to these models delivering good but not outstanding findings. Depending on the business case, the model should therefore be precisely fine-tuned so that optimal results related to the problem can be generated.
There are two dimensions to fine-tuning. On the one hand, you have to adapt the model to language units. Vocabulary, slang or the dialect play a decisive role. Depending on the industry, these areas can vary greatly from one another.
On the other hand, in addition to fine-tuning, the model should also be specifically tailored to the business problem at hand. To classify emotions, a model has to work differently than when it does. is used to translate texts.
Above all, the constant improvement in computing power has led to the advance of artificial intelligence. Nevertheless, pre-trained models offer the possibility that only a fraction of one's own computing power has to be provided.
Only computer power is required for data processing, which is a fraction of the computing power of an entire training course. And although the effort can be minimized with pre-trained models, a computer should be able to provide a certain amount of computing power. That is why cloud computing is usually used in practice. The costs for cloud computing are usually billed to the minute, according to which a standard data center is usually not worthwhile due to the costs.
It is now a reality that humans and machines communicate with one another. Due to the ever increasing computing power and the large amount of data, the action of natural language processing technologies is getting better and better.
As the various examples show, Natural Language Processing is primarily used for better interaction with the customer. NLP systems are characterized by the fact that they are available at any time and can be scaled as required.
If you have any further questions about the practical application of NLP, please do not hesitate to contact me.
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