Natural Language Processing: what it is, how it works, and how to leverage tt in Business

Natural Language Processing


Natural Language Processing (NLP) is a field of artificial intelligence (AI) that enables the understanding and manipulation of human language (natural language). Thanks to this tool, software can interpret, analyze, and generate texts in a manner very similar to how a human would.

Early techniques for syntactic and semantic analysis were heavily limited by the computing tools available. Today, with increased computing power and the use of increasingly sophisticated machine learning models, NLP has reached such high levels of functionality that it allows Business Intelligence companies to develop unprecedented Advanced Analytics solutions.

What is Natural Language Processing?

Natural Language Processing, or NLP, is a branch of artificial intelligence focused on understanding and interpreting human language in a natural way. This technology is essential for natural language analysis, allowing computers to understand, interpret, and generate text similarly to humans. The importance of NLP in the business context is undeniable, as it allows organizations to analyze large volumes of textual data efficiently and effectively.

At the core of NLP are several processes and techniques that enable the understanding, manipulation, and generation of natural language, that is, the way humans communicate with each other, understanding spoken and written language with all its nuances and ambiguities.

  • Tokenization: this is the process of dividing a text into smaller units called tokens. Tokens can be words, phrases, or even individual characters. Tokenization is a fundamental step for a machine to analyze and manipulate text.

  • Stemming: this is a technique used to reduce words to their base form, called the “root” or “stem”. For example, the terms “cats” and “cat” would be reduced to the root “cat”.

It’s evident that this alone is not sufficient, considering the complexity of human language, such as ambiguity (human language can be interpreted in different ways, and words can have multiple meanings depending on the context) or contextuality (sentences can acquire different meanings depending on the context in which they are placed).

How Does Natural Language Processing Work?

NLP uses a wide range of techniques and models to accurately analyze and generate text.

Techniques in Natural Language Processing

  • Text Classification: this technique assigns labels or categories to documents. It is widely used for tasks such as classifying emails as spam or not spam, categorizing documents into different topics, or determining the emotions associated with a text. Classification techniques use machine learning algorithms to extract relevant features from texts and train models that can make accurate predictions.

  • Information Extraction: this involves recognizing and extracting structured information from unstructured texts. For example, it can be used to identify names of people, places, organizations, or dates within a document. This technique leverages syntactic and semantic analysis methods to identify entities and relationships within the text.

  • Text Generation: this technique allows software to produce text automatically. It can be used to create automatic summaries, answer questions, or generate any written production. Text generation is based on language models that learn the probabilities of word sequences based on a training data set. These models often use neural networks to capture complex relationships between words and generate coherent text.

Machine Learning Models in Natural Language Processing

  • Neural Networks: these are mathematical models inspired by the functioning of the human brain. In NLP, neural networks can be used for tasks such as text classification, automatic translation, or sentiment analysis. Neural networks allow machines to learn from training data and improve their predictions as they gain experience.

  • Transformers: this type of neural network architecture has proven particularly effective in the field of NLP. This architecture is based on attention, a mechanism that allows the network to focus on relevant parts of the text during processing. Examples of Transformer models include BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer).

Applications of Natural Language Processing

NLP is opening new avenues in Business Intelligence (BI) by providing tools and technologies for analyzing and interpreting textual data.

Through NLP, it is possible to extract useful information from unstructured textual data, such as business reports, financial documents, emails, and other texts. This information extraction allows for identifying trends, relevant topics, and gaining deeper insights into business data. For instance, NLP can automatically extract key financial figures from annual reports or identify mentions of specific products in customer reviews.

One of the great advantages is that NLP allows for efficient and automated processing and analysis of large volumes of textual data, enabling the identification of relevant information, discovering connections, uncovering patterns, and generating valuable insights that support business decisions. For this reason, NLP is often used to improve the efficiency of information search and access: NLP-based search systems allow users to formulate questions in natural language and obtain relevant results. This simplifies the identification of specific information, reduces search time, and improves data accessibility.

A direct consequence is the use of NLP systems to generate automated reports that summarize key information from large amounts of textual data. In summary, the ability to process and understand natural language has opened new opportunities for data analysis and improved the efficiency and accuracy of business strategies.


NLP in Business

Natural Language Processing is already widely used by companies to improve operational efficiency, better understand customers, and offer personalized services.

Automated Customer Support

Companies use NLP to implement chatbots and virtual assistants that can automatically and immediately respond to customer inquiries. Virtual assistants can understand users’ natural language and provide relevant answers. This improves customer experience, reduces wait times, and manages a high volume of requests without human intervention.

Customer Feedback Analysis

NLP is used to analyze sentiment expressed in textual data, such as customer reviews, social media comments, or survey feedback. Companies can understand customer opinions, detect positive or negative sentiments towards products or services, and identify any issues. Sentiment Analysis allows companies to adapt their strategies, improve customer experience, and make informed decisions to enhance products and services.

Automated Document Processing

NLP allows for the automated processing of documents, such as contracts, invoices, or reports, as well as technical guides, improving document management efficiency and reducing human errors. For example, data can be automatically extracted from documents to improve internal technical support services or create complete customer records.

Automatic Translation

With NLP, companies can simplify the process of global expansion, overcoming language barriers and reaching a wider audience. Automatic translation can be used to translate documents, internal communications, web content, and communications with international customers.

Introducing NLP in Business

To introduce Natural Language Processing systems in companies, several key elements are necessary, such as defining clear objectives, proper management of business data, and fostering a data-driven culture.

Additionally, technical resources and expertise in data science are required: this can include acquiring NLP tools or platforms, having adequate hardware and software resources, and accessing expert personnel or training employees internally on these skills.

Another aspect involves the ability to evaluate and choose available NLP models to find those that best meet the company’s needs. Sometimes, it requires testing on trial data or implementing a trial-and-error process to identify the best solution. Closely related is the issue of integration and development of solutions: once models are selected, they need to be integrated into existing business solutions. This may require collaboration between software development teams, data science teams, and other relevant stakeholders.

The introduction of NLP systems requires a strategic approach, collaboration between different business teams, and a deep understanding of the tools and software available on the market.

We realize Business Intelligence & Advanced Analytics solutions to transform simple data into information of freat strategic value.


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