As consumers, our everyday lives are overwhelmed by applications that, in order to make our digital experience more intuitive and rewarding, are powered by NLP. The most common ones come in the form of voice-operated GPS systems, digital assistants and chatbots, among others, but NLP also plays an increasingly important role in simplifying critical business processes.
What is natural language processing (NLP)?
Natural language processing (NLP) is a field of artificial intelligence (AI) that uses cognitive machines to understand, interpret and analyze human language in all its forms. Merging computational linguistics with intelligent statistical models allows computers to process and respond to text or voice data themselves, with the intent and sentiment of the speaker or writer.
How does NLP work and why does it matter?
Using algorithms based on machine learning methods, natural language processing is capable of automatically and almost instantaneously reading and extracting relevant data from unstructured content. Once it has determined which of them are significant for exploitation, NLP will analyze, standardize and summarize them in clean and comparable formats.
In long articles, for example, NLP finds the main idea and ignores all useless content. But beyond synthesis, it helps to clarify complicated texts and identify connections between them; providing a more accurate understanding of the information and bringing standardization, structure and enrichment to texts and narratives.
By breaking down the time and resource barrier of decrypting unstructured content, NLP enables organizations around the world to extract value from data sets with unprecedented efficiency and precision.
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Key natural language processing applications
Speeding up commercial transactions, increasing employee productivity, and globally expanding operations are just a few of the areas where NLP plays a key data strategy role in today's corporate landscape.
Implementing it in computer programs allows machine translation, automated discovery through semantic search in search engines, or speech recognition to be carried out.
However, applying natural language processing is also possible in a myriad of much less familiar tasks; such as the ability to generate totally realistic human language, as explained by The Conversation. We’ll discuss some of these applications below.
While speech recognition tools identify and interpret words and phrases to then enter the information received in the form of text, it is the NLP that gives meaning to this information, recognizing patterns in speech and inferring its meaning in order to provide useful and coherent answers.
Speech recognition has long been used as a simple dictation device, but it was by complementing it with natural language processing that it became responsible for revolutionizing the way people and machines interact. Over time, natural language processing has developed its ability to understand contextual cues; even responding with humor or in a conceptual way.
By applying NLP to speech recognition tools, companies can create knowledge graphs applied to voice-driven intelligent interfaces. This way, the system becomes more personal and precise, identifying relevant concepts in the user's domain.
Grammatical labeling (POST) consists of assigning a grammatical category to each of the words in a text, thus clarifying the grammar (noun, verb or adjective among others) in a linguistic context.
In order to do this, NLP determines the function of each word separately, relating each concept to adjacent words in a phrase, sentence or paragraph. Once the morphology has been analyzed, it is supported by self-learning algorithms to implement predefined descriptive labels.
One of its main advantages is building language models from a specific linguistic point of view. In doing so, it facilitates the inclusion of increasingly complex sources of information that provide more useful and enriching content.
Big data management
Big data is the main source of constant information that guides millions of companies’ strategies in the form of petabytes stored in the cloud. In addition to being largely unstructured, big data is constantly growing and provides a global perspective on current or projected market trends.
This type of data would be of little use without the aid of natural language processing as a tool to extract the information in a revealing and understandable way. The business intelligence process therefore uses NLP to perform search operations on queries entered in natural language, thus covering all possible scenarios and minimizing statistical errors.
Beyond the official feedback from customers or the market, natural language processing manages to draw a conclusive picture of, for example, whether a particular product or service is being, or will be, welcomed in the target market segments.
Sentiment analysis refers to the use of natural language processing, computational linguistics and text analysis to identify the sentiment of a text string; extracting subjective information from available resources by means of AI.
It is widely used as a tool to elucidate whether a newspaper article, for example, is favorable or unfavorable in relation to a particular topic or trend. Companies also often use it to monitor brand and product sentiment in customer reviews.
By means of neural networks, it is possible to classify positive, negative or neutral inputs using word representations as vectors, to systematically identify, extract, quantify, and study affective states and all kinds of subjective feedback. Having good theoretical knowledge is only the beginning: NLP simplifies the burden of complex tasks with the possibility of schematizing their practical meaning.
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Pangeanic: a strategic view of natural language processing
The aforementioned applications are just a few of the countless opportunities that natural language processing provides at present and in the immediate future. We agree with Jaxenter that we're at the tip of the iceberg in terms of what NLP has to offer.
Its implementation is gaining ground in the humanization and democratization of artificial intelligence; both for companies and their customers. New technologies are creating smarter consumers and much more complex demand patterns, so the only way to maintain a strategic competitive advantage is to make them our allies.