Data Science Consultant at almaBetter
Today we hear the name ChatGPT from most of the people around us. It has become a personal assistant to many. You must have wondered how chatbots like ChatGPT can correctly understand and respond to human language.
The answer is Natural Language Processing (NLP), which is all about teaching machines to understand human language. Just like we communicate with each other, NLP allows computers to communicate with humans using natural language. For example, a virtual assistant like Siri or Alexa tries to understand what humans say and respond appropriately.
NLP is used in a variety of applications, such as sentiment analysis, language translation, text classification, and more.
Table of Contents
NLP or Natural Language Processing, is a technology that helps computers understand, interpret, and generate human language. It allows computers to communicate with humans using natural language, just like we do with each other. The goal of NLP is to make it easier for people to communicate with computers and for computers to understand and process human language.
The field of NLP can be categorized into three main parts:
Speech Recognition: The conversion of spoken language into written text.
Natural Language Understanding (NLU): The capacity of computers to comprehend human language.
Natural Language Generation (NLG): The production of human-like language by a computer.
Some of the applications of NLP are as follows:
NLP is a fast-expanding discipline used in different industries, including healthcare, education, e-commerce, and customer service. With advances in NLP, computers can now interpret and process human languages in ways that can be utilized for a variety of applications, such as speech recognition, language translation, question answering, and more.
The Natural Language Processing (NLP) pipeline includes the mentioned components:
Each step is important and requires careful consideration to ensure that the final model is accurate and effective.
Let us go through the Feature Transformations, which are a part of pre-processing techniques with the help of python example codes using the NLP libraries in Python.
Word tokenization involves breaking down a sentence into individual words, also known as tokens. This is typically performed by splitting the sentence into spaces, although other methods can also be used. Punctuation marks are also considered tokens, as they have a distinct meaning and provide important information for NLP tasks.
For example, given the input sentence: “I love visiting the park.”
The word tokenization step would result in the following list of tokens:
Here is an example code
By tokenizing the words, it becomes possible to perform subsequent NLP tasks, such as part-of-speech tagging, on individual words. Word tokenization is an important step in NLP that enables the computer to process text data and enable a deeper understanding of the language.
Stemming is a process in Natural Language Processing (NLP) that involves reducing words to their root form. The goal of stemming is to reduce words to a common form so that they can be analyzed and compared in a more meaningful way.
For example, the words “running,” “runner,” and “ran” are all related to the concept of “run.” Stemming can reduce these words to their root form, “run,” making it easier to perform tasks such as text classification and information retrieval.
Stemming algorithms work by removing suffixes from words, such as -ing, -ed, -es. This results in a stemmed word that is often not an actual word but a base form that can be used to represent the word in various NLP tasks.
The below python example code will help better.
There are several different stemming algorithms, including the Porter Stemmer, Snowball Stemmer, and Lancaster Stemmer. The choice of stemmer will depend on the specific NLP task being performed.
It involves identifying and removing common words such as “the”, “a”, “an”, “and”, “of”, that are unlikely to carry significant meaning in the text. These words are commonly referred to as “stop words” because they can be “stopped” or removed without affecting the overall meaning of the text.
For example, given the sentence: “I am going to the store to buy groceries.”
The stop words identification step would result in the following list of tokens and lemmas after removing stop words:
The following code will help understand better.
By removing stop words, it becomes possible to reduce the size of the text data and focus on the most meaningful words in the text. Stop word identification is widely used in NLP models such as text classification, information retrieval, and text analysis. It helps to improve the efficiency of NLP models and to enable more effective analysis of the text data.
There are many more feature transformation steps Named Entity Recognition (NER), Predicting the Parts of Speech for each token, and Finding noun phrases that can be performed to improve the training process of the data.
Bots: Chatbots help clients get to the point fast by answering questions and referring them to relevant resources and goods at all hours of the day and night. Chatbots must be fast, smart, and simple to use in order to be effective. Chatbots use NLP to interpret language through text or voice recognition interactions.
Supporting Invisible UI: Human communication, both verbal and written, is involved in almost every connection we have with technology. Amazon’s Echo is just one example of how humans will increasingly interact with technology in the future. An invisible or zero user interface will rely on direct communication between the user and the computer, whether by speech, text, or combination. NLP assists in making this concept a reality.
Smarter Search: One aspect of NLP’s future that we have been considering at Expert System for a long time is improved search. Instead of focusing on keywords or themes, smarter search allows a chatbot to understand a customer’s request and enable “search like we talk” capabilities (much like we could question Siri). Google recently announced the addition of natural language processing (NLP) capabilities to Google Drive, allowing users to search for documents and information using natural language.
To get started with NLP using Python, here is a list of a few projects that can be built to enhance our knowledge and gain more expertise in the field of NLP.
NLP methods are utilized in a vast range of applications like search engines, sentiment analysis, text summarization, question answering, machine translation, and more. NLP is a dynamic field with continuous advancements, particularly in deep learning, which has significantly improved NLP performance. Despite this progress, NLP remains a challenging field that requires a strong understanding of both computational and linguistic principles.
Join AlmaBetter’s Full Stack Data Science program to learn more about NLP.