NLP (Natural Language Processing) is considered a branch of machine learning dedicated to recognizing, generating, and processing spoken and written human speech. It is located at the intersection of artificial intelligence and linguistics disciplines.
Software engineers develop mechanisms that allow computers and people to interact using natural language. Computers can read, interpret, understand human language, and provide feedback thanks to NLP. As a rule, the processing is based on the level of intelligence of the machine, deciphering human messages into information that is meaningful to it. Many areas of our lives have already implemented these technologies and successfully used them. It is essential to understand the NLP processes and how their algorithms work. It is necessary in order to keep up with the times and use the potential of these technologies to one hundred percent.
Basics of NLP algorithms
The process of machine understanding using natural language processing algorithms can look like this:
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The machine converts words from audio into written text.
The NLP system parses the text into components and understands the context of the conversation and the person’s goals.
Given the results of NLP, the machine determines the command to be executed.
How language processing works
Theretofore, algorithms prescribed a set of reactions to specific words and phrases. The comparison was used for the search. It is not texting recognition and understanding but a response to the entered character set. Such an algorithm would not be able to tell much difference between words.
NLP is a different approach. Algorithms teach not only words and their meanings but also the structure of phrases, the internal logic of the language, and understanding of the context. To understand what the word «it» in the sentence «the man wore a suit, and it was red» refers to, the machine must have an understanding of the properties of the concepts «man» and «suit.» Experts use machine learning algorithms and language analysis methods from fundamental linguistics to teach this to a computer.
Areas of use
The trend towards an expansion in the number of applications created within the framework of this direction is quite understandable. Today, this function is relevant:
the operation of search services;
in targeted marketing;
to recognize speech commands;
in teaching voice assistants, etc.
There are numerous options, and more and more appear every year. The functionality becomes relevant for the gaming sector, working with software and solving other tasks that make it possible to do without using the familiar user interface.
Why do people need natural language processing algorithms?
It is the most trustworthy tool for working with various but widespread machine applications such as online translators and other voice applications. Typically, this includes:
Language translation tools, including Google Translate.
MS Word tools, grammar, and other language tools to check grammatical accuracy.
Automatically generated voice messaging tools are primarily used in call centers and customer service departments.
Mobile or web helpers such as Siri, OK Google, and Alexa.
The list is expanding every year. Therefore, it is vital to understand NLP intricacies to keep up with trends.
What makes NLP so tricky?
NLP is considered one of the most challenging technologies in computer science due to the complex nature of human communication. It is challenging for machines to understand the context of information.
It can be quite an abstract environment that changes the meaning and understanding of speech. The most common illustration is sarcastic remarks used to convey information.
Thus, the machine needs to decipher the words and the contextual meaning to understand the entire message.
Tasks of NLP
Natural language processing is present in everyday interactions with all kinds of machines. Virtual assistants, such as smart speakers or chatbots rely on natural language processing to communicate with people. Other NLP tasks:
Speech recognition. It is done by voice assistants of applications and operating systems, smart speakers, and similar devices. Speech recognition is also used in chatbots, automatic ordering services, automatic generation of subtitles for videos, voice input, and intelligent home control. The computer recognizes what the person said to it and performs the necessary actions following this.
Word processing. A person can also communicate with a computer through written text. For instance, through the same chatbots and assistants. Some programs work simultaneously as both voice and text assistants. An example is assistants in banking applications. The program processes the received text, recognizes it, or classifies it. It then performs actions based on the data it receives.
Information extraction. Specific knowledge can be extracted from text or speech. An example of a task is answering questions in search engines. The algorithm must process the array of input data and remove key elements (words) from it, following which the actual answer to the question will be found. It requires algorithms that can distinguish between context and concepts in the text.
Information analysis to the previous one, but the goal is not to get a specific answer but to analyze the available data according to certain criteria. Machines process the text and determine its emotional coloring, theme, style, genre, etc. The same can be said about voice recording.
Information analysis is often used in various types of analytics and marketing. For instance, you can track the average sentiment of reviews and statements on a given question. Social networks use such algorithms to find and block malicious content. In the future, the computer will probably be able to distinguish fake news from real news and establish the text’s authorship. NLP is also used when collecting information about the user to display personalized advertising or use the information for market analysis.
Algorithms — the basis of natural language processing
Natural language processing is based on algorithms for converting ambiguous data into comprehensive information for machines to build understanding. These algorithms use different natural language rules to complete the task.
When the information arrives at the computer, it uses a different set of algorithms to understand the context value associated with the command. It then collects the appropriate data needed to fulfill the request.
However, sometimes the computer provides unclear results because it cannot understand the contextual meaning of the command. For example, Facebook posts generally cannot be translated correctly due to poor algorithms.
How text is processed
Algorithms do not work with raw data. Most of the process is preparing text or speech and converting them into a form accessible to the computer.
Data that is useless to the machine is removed from the text. These are most punctuation marks, special characters, brackets, tags, etc. Some characters may be significant in specific cases. For instance, currency signs make sense in a text about the economy.
After preprocessing, the output is a set of prepared words. But algorithms work with numeric data, not pure text. Therefore, vectors are created from the incoming information — they represent it as a set of numerical values.
Popular vectorization options are «bag of words» and «bag of N-grams». In the first type, words are encoded into numbers. Only the number of lexical units in the text is considered, not their location and context. N-grams are groups of N words. The algorithm fills the «bag» not with individual lexical units with their frequency but with groups of several formatives, which helps determine the context.
Application of machine learning algorithms
Using vectorization, you can estimate how often words occur in the text. But most actual problems are more complicated than just determining the frequency — advanced machine learning algorithms are needed here. Depending on a particular task type, a separate model is created and configured.
Structure of the algorithms
Modern machine learning uses neural networks, modeled on the human brain, which utilize artificial neurons to transmit signals. The learning process itself consists of a review of numerous examples. Certain tasks that neural networks perform to improve natural language processing are very similar to what we do when learning a new language. If we structure the NLP algorithm, you get the following sequence.
First, you need to translate the information into a format convenient for the operation of NLP algorithms (symbols, words). If the message comes in an audio file, speech recognition is performed (reception and processing of a sound wave). If in the form of an image — optical character recognition (Optical Character Recognition).
Then, if necessary, typos in the text are corrected (spell-checking). Then go two types of the morphological analysis of the received string: lemmatization (the process of bringing the word to a standard form, for instance, in the singular nominative case for a noun) or stemming (obtaining the stem of the terms).
Next comes the semantic analysis of the text. Using various machine learning algorithms (RNN, CNN, etc.), a particular representation of the text is obtained (usually in the form of a vector), with which you can determine the meaning of the text.
In the case of chatbots, we must be able to determine the meaning of a phrase using machine learning and maintain the context of the dialogue throughout the conversation. The need to take into account the context of the dialogue makes chatbots one of the most high-level tasks in NLP, which uses a large set of algorithms (classification, text sentiment analysis, algorithms for predicting the next step of the dialogue), as well as information storage tools (storage in RAM, relational and non-relational DBMS).
Various NLP methods allow for solving the above problems — Python (programming language) is widely used for implementation. But before diving into lines of code, it’s essential to understand the concepts behind these natural language processing techniques.
This method breaks up the text into sentences and words — that is, into parts called tokens. Certain characters, such as punctuation marks, must be discarded in this process.
Removing stop words
To free up space in the database and reduce text processing time, uninformative words that are of no value to NLP are removed from it. These are pronouns, prepositions, articles, and so on. There is no ubiquitous list of stop words. You can choose a collection of them in advance, expand the list later, or even create from scratch.
It is the process of finding the root of a word by removing its affixes, that is, prefixes or suffixes attached to the basis of the word. The problem is that affixes can create new forms of the same word (like the «e» suffix in the word faster) or even new words (like the «ist» suffix in the word guitarist). Moreover, it is essential to understand that the stem of a term is not always equal to its root.
It aims to facilitate a word to its basic form and group various forms of the same word. For example, verbs in the past tense change in the present («he walked» and «he is going»). Although it seems connected to the stemming process, lemmatization takes a different approach to finding root forms.
This method assumes that each document consists of a combination of topics, and a set of some words defines each topic. What does this mean? If we discover hidden themes, we can reveal the meaning of the texts more fully. How to do it? Build a topic model of a collection of text documents to let the system understand what topics each text belongs to and what words form each topic.
Essentially, topic modeling is a technique of discovering hidden structures in sets of texts or documents. It is beneficial for classifying texts and building recommender systems (for instance, recommending books to you based on past choices).
Difficulties in using NLP technologies
Natural language processing uses machine learning to teach computers to understand and translate human language. However, there are many problems that arise when using these technologies:
sequential processing of words;
inability to retain a large amount of information in memory;
susceptibility to the vanishing/exploding gradient problem;
impossibility of parallel processing of data.
In addition, popular processing methods often misunderstand the context, which requires additional careful tuning of the algorithms.
Most of these problems are solved by large language models, but there are several difficulties. The first is their availability. Like GPT-3 or BERT, a large language model is challenging to train, but large companies are increasingly making them available to the public.
Furthermore, many models work only with popular languages, ignoring unique dialects. It affects the ability of voice algorithms to recognize different accents.
Numerous algorithms cannot cope with handwritten fonts when processing text documents using optical character recognition technology.
Evolution from NLP to NLU
The next stage is launched when natural language processing is performed using various methods. The received meanings are collected together and converted into a structure understandable to the machine. And this takes into account not only linguistic factors but also the last conversation, the speaker’s proximity to the microphone, and a personalized profile.
Natural language understanding (NLU) is a subfield of NLP gaining popularity due to its potential in cognitive systems and artificial intelligence applications. It is difficult to understand where the border between NLP and NLU lies. Though the latter goes beyond the structural understanding of the language. NLU algorithms must solve the complex problem of semantic interpretation, that is, understanding spoken or written text with all the subtleties, context, and inferences that we humans can make.
The evolution of NLP towards NLU can be essential both in business and in everyday life. As the volume of shapeless information continues to grow, we will benefit from the tireless ability of computers to help us make sense of it all.
Natural Language Processing is the core of human-machine communication and uses various techniques to improve task performance. It is still in development and requires breakthroughs to make machines smarter and achieve human interaction perfection. It uses numerous algorithms and processes: tokenization — splitting text into tokens, so-called individual components — words, sentences, or phrases; tagging parts of speech — identifying parts of speech in each sentence to apply grammatical rules; lemmatization and stemming — bringing words to a single form, etc. NLP algorithms are the future of technology. It is the next step in developing our civilization. When it reaches its peak, the world of economy, business, service, and culture will change and become more efficient.