What is natural language processing and how does it work?
Natural language processing (NLP) is a powerful combination of linguistics and computer science that, through the study of language and the creation of intelligent systems, makes human language as intelligible to machines as it would be for a human being, whether in text or speech format.
As a branch of artificial intelligence (AI), NLP enables computers and machines to understand, interpret and manipulate human language using computational linguistics and statistical models, machine learning methods and deep learning processes.
The knowledge extracted by these technologies is converted into algorithms that teach machines to perform a myriad of tasks that are infinitely valuable to businesses. The more data NLP algorithms receive, the more precise text analysis models become.
NLP approaches and techniques
NLP includes an immense diversity of techniques, from statistical and machine learning methods to algorithmic and rule-based approaches. There is a wide range of approaches due to the variety of text- and speech-based data and according to their practical applications.
Before going over the most relevant NLP techniques, it is important to understand that there are specific tools designed to perform these tasks automatically without having to invest a lot of time and effort in programming them manually.
Syntax and semantics
Syntactic and semantic analysis are two of the main techniques used in NLP. Syntax refers to the use of grammatical rules for arranging words in a sentence to make grammatical sense. Semantics, on the other hand, involve the use and meaning of words by applying algorithms that clarify the meaning of the concepts.
NLP combines traditional rule-based systems with more complex systems based on machine learning methods. It uses statistical methods capable of understanding and making sense of the meaning of the language in question. As they learn to perform tasks based on the training data they receive, they adjust and refine their methods as more data is processed.
H4: Data processing techniques
Being one of the most widely used techniques in semantic processing, tokenization converts sequences of characters, words or paragraphs into inputs for the machine, segmenting large amounts of text in order to process it in a more efficient and meaningful way.
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NLP capabilities and functions
In order to understand the structure and meaning of human language, NLP analyzes different aspects of the source, such as syntax, semantics, pragmatics and morphology, in order to perform tasks that break down text- and speech-based data in a way that makes sense of what the machines are taking in. Some of the highlights include:
Sentiment analysis is an NLP task for which machine learning models are trained to classify the text by opinion polarity (positive, negative or neutral). By extracting the text's subjective qualities, such as attitudes and emotions, NLP is able to identify subjective opinions within large amounts of text. It is very useful when it comes to understanding the reaction of a group of consumers or potential customers in relation to a specific event.
Text classification and summarization
By extracting the most relevant information from a large number of sources, NLP simplifies the data summary process to create an abridged version of a document without losing its key points. This can be based on extraction (contextually extracting phrases and sentences from an existing text) or on abstraction (creating a summary from scratch that renders all the value of the original source).
This function involves automatic translation of text- or speech-based content from one language to another. Machine translation (MT) is increasingly capable of understanding the context and preserving the meaning of complete sentences, thanks to new neural networks and a greater number of available sources of big data.
Other NLP tasks
NLP offers a myriad of tasks in addition to those mentioned above, including speech-to-text and vice versa, text anonymization, document classification, grammatical tagging, word-sense disambiguation or topic modeling.
NLP's pattern recognition and applied statistics have opened the doors to a new level of global communication. It plays a large role in the legal and economic-financial environments, among others, when translating documents, identifying and linking stakeholders and entities in large amounts of data to help meet the General Data Protection Regulation (GDPR) through anonymization or create parallel data for training AI systems.
In addition, NLP is particularly useful in sectors such as insurance, health or industry, helping to accelerate efficiency and digital transformation processes. With speech recognition and speech synthesis, for example, chatbots can be created that are able to have fluent conversations with international users and answer questions automatically.
NLP applications actually include all sectors and make up the core of everyday tools, from translation software, chatbots, spam filters and search engines, to grammar correction software, voice assistants and social media monitoring tools.
How can natural language processing help your business?
What benefits will NLP bring to your company? Here are a number of benefits that will drive your business towards a more efficient use of information and competitive advantages.
Usable information extraction
NLP helps machines to automatically understand and analyze large amounts of unstructured data, such as customer service tickets, online reviews and news reports. By performing fleeting and large-scale analysis, it enables the acquisition of in-depth information in all languages, thus increasing decision-making capabilities.
From market forecasts to annual financial investments, NLP extracts information from news, reports and documents with the ability to transform them into algorithmic decision-making. In this way, the extracted information is converted into a source of strategic determination.
Human parity in translation
NLP is closer than ever to human quality machine translation. It enables understanding and interpreting language and speech structures, allowing natural conversations by including a fundamental meaning that converts the output in an infinitely more coherent and accurate result.
Eliminating language barriers to help people communicate is therefore one of the main objectives of NLP. Through machine learning methods fed by available big data, data science is increasingly able to mimic the way people correct machine-translated text.
Customized language solutions
NLP uses intelligent and deep adaptive technology to accelerate multilingual content processing and knowledge acquisition. NLP algorithms adapt to suit individual needs and criteria, such as the complex and specific language of each industry.
By combining the use of unique human capabilities with the latest developments in NLP software, it is possible to offer customized solutions in different linguistic fields.
Real-time process automation
Artificial intelligence requires a large amount of data and, therefore, data science is necessary to store, manage and clean all that information. NLP can organize tasks to be carried out automatically, performing much faster and more exhaustive searches, classification and analytics.
By dramatically reducing the human effort required for traditionally manual and repetitive tasks, operational efficiency is improved, allowing your business to grow in a scalable way by allocating resources in the most effective way to reduce costs and inefficiencies.
Read more: How to train your machine translation engine
Pangeanic: NLP services for document publishing and translation
Pangeanic and its ECO platform allow you to meet the needs of your customers, regardless of the language, with services such as machine translation, anonymization, data classification, or sentiment analysis, among others.
A fully customizable tool with which you can translate documents and extract deep knowledge from texts, without language or time barriers.