Natural language processing (NLP), a branch of machine learning and artificial intelligence, is transforming the way machines understand and interact with human language. As we enter the information age, this technology is becoming increasingly crucial.
In this article, we will explore what natural language processing is, the process it goes through and some of its applications in the retail sector.
What is Natural Language Processing?
PLN is a discipline that focuses on the ability of machines to read, understand and derive meaning from human language data. It is an intersection between computational linguistics and artificial intelligence that seeks to teach machines how to understand the language we use every day.
This discipline facilitates the processing and extraction of unstructured data in order to generate a new understanding of the data collected. Working with PLN tools requires knowledge of programming languages such as Phyton or Java.
Natural language processing process
Natural language processing consists of 2 main phases:
- Data preparation and processing: preparation and cleaning of text data that machines can understand for further analysis. In this phase, data preparation takes place and the text features that an algorithm can work with are highlighted. How can this be done? We explain some of them.
- Tokenisation: division of the text into relevant words or phrases to facilitate analysis.
- Elimination of stopwords: detection and elimination of common words that do not contribute meaning, such as “and” or “of”.
- Lemmatisation: conversion of words to their base or root form. For example, the words “running” and “runner” could be lemmatised into “running”.
- Algorithm development: using machine learning and deep learning techniques, a model is trained based on the processed data. These models can vary from simpler to more complex models such as recurrent neural networks (RNN) or the GPT-3 model, developed by OpenAI in 2020.
Applications of Natural Language Processing
NLP applications are a powerful tool for improving efficiency, customer experience or even business decision making. Here are some of the uses retailers can make of it:
- Chatbots and virtual assistants: PLN-based chatbots can interact with customers in real time, resolve queries, process orders and offer personalised recommendations.
- Sentiment analysis: companies have the opportunity to analyse customer opinions and comments on social media, websites and other digital platforms to determine perceptions about their products, services or the brand in general.
- Personalised recommendations: by analysing product reviews and customer feedback, PLN provides ecommerce platforms with more accurate and personalised product recommendations.
- Trend analysis: by monitoring online conversations and discussions, PLN helps detect emerging trends in consumer demand or product preferences.
- Improved customer experience: by analysing customer interactions, companies can identify problem areas or common pain points and work to improve those aspects.
A well-known example is ChatGPT
OpenAI has revolutionised technology with its GPT language model and conversational AI. Its official launch was in October 2020 as part of Open AI’s AI platform, sparking a global revolution that has integrated the use of artificial intelligence into services around the world, such as conversational chatbots.
The public launch of ChatGPT 3.5 on 30 November 2022 has put the spotlight on this service that anyone can use for free.
The model is trained using large datasets and then adjusted to generate consistent text according to the questions or commands it receives. This allows it to learn patterns in the language and build an internal representation of linguistic knowledge.
It uses natural language to produce answers, which means it generates text rather than simply selecting an answer from a predefined list. It can even adapt to conversations in real time through the use of short and long term memory, allowing it to remember the context of the conversation.
Challenges for the PLN
Despite its advances, human language has so many ambiguities that it is very difficult to program software that can determine the meaning of speech or text with high accuracy. Some of the challenges of using PLN include:
- Ambiguity of language: Human language is so ambiguous that it can cause doubts of meaning in some cases. A single word can have multiple meanings depending on the context. For example, “bank” can refer to a financial institution as well as a seat.
- Irony and sarcasm: These nuances of human language can be difficult for a machine to identify and understand.
- Cultural aspects: Language is influenced by culture, history and social factors. Incorporating this knowledge into NLP systems is complicated.
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At Cognodata we have built CognoIA, an artificial intelligence accelerator platform based on advanced analytics, machine learning and generative AI that allows us to speed up execution and inject our experience into the projects we carry out. Specifically, we have developed CognoGPT as a module within this platform for the application of capabilities to see, hear, speak, search, understand and accelerate advanced decision making in new use cases. Do you want to know it?
