Understanding Natural Language Processing (NLP) Essentials
Introduction to Natural Language Processing
This article aims to cover what natural language processing is in layman's terms with visual and code examples. We will have a different approach in understanding NLP unlike other articles out there for NLP basics.
Let’s start with the human approach to understanding things and gradually get on with mathematical concepts in depth in a series of articles rather than directly jumping into terminologies or use cases of NLP in the Machine Learning and statistical computing domains.
What is Natural Language Processing(NLP)?
Few definitions of NLP:
Natural language processing (NLP) refers to the branch of computer science — and more specifically, the branch of artificial intelligence or AI — concerned with giving computers the ability to understand text and spoken words in much the same way human beings can.
Natural language processing (NLP) is a machine learning technology that gives computers the ability to interpret, manipulate, and comprehend human language.Natural language processing (NLP) is a machine learning technology that gives computers the ability to interpret, manipulate, and comprehend human language.
(Source: https://aws.amazon.com/what-is/nlp/)
In the most basic terminology, Humans use multiple languages to communicate in terms of text and speech. However, still, machines are not so advanced (still far from representing human consciousness mathematically) to understand these languages natively or learn them as humans do.
Natural Language Processing, is a branch of Artificial Intelligence that provides methods via which machines can make some sense of those native languages via mathematical equations and advanced machine learning techniques.
How does NLP actually work?
How Natural Language Processing(NLP) Works? - Scalifi Ai
Let us start by thinking about how did we learn the language that we are communicating in?
There are different strategies for learning and understanding a language depending on if you already know any other language and your age group. To better understand NLP let's consider how a child learns and starts understanding a particular language:
- Children start understanding a language through interaction - not only with their parents and other adults but also with other children. All normal children who grow up in normal households, surrounded by conversation, will acquire the language used around them.
- Our brain continuously tries to understand the meaning of words, what they mean, and what they represent. Over a period of time, this collective knowledge helps us in forming meaningful sentences and express our thoughts.
As machines work on pure numbers and understand only numbers we need a way to convert our language, words, and their correlation into a numerical representation. This is done by building Word Embeddings which try to capture the meaning of words and how they are related to other words.
Introduction to Neural Word Embeddings in NLP
(Source)
Word embeddings are vector representations of a collection of words (vocabulary) which are then used to create a high-level representation of any sentence using those vectors.
The most common word embeddings include Glove and Word2Vec. These embeddings are pre-trained on a large corpus of data with different training methods and models like Bag of Words (BOW) and Skip N-Gram training methods. (In this article we will not get into the depth of how these word embeddings are created and trained)
This means that each word is represented by an array of numbers, if we calculate the cosine similarity between two words that gives us what their semantic similarity is.
Eg: Based on pretrained embedding “glove-twitter-25”
If we calculate the cosine similarity between the words “cat” and “dog” it comes out to be 0.95908207
And if we calculate the cosine similarity between the words “cat” and “france” it comes out to be 0.36348575
This shows the semantic similarity between the chosen words and in turn helps any machine learning model to understand the English language better semantically. We can clearly see that the value for words “cat” and “dog” is way higher (as they are both animals that too four legged) and the value between “cat” and “france” is way lower (as one is an animal and one is a country)
Following are the vector representations of the words discussed above:
Vector representation for word cat NLP - Scalifi Ai
Vector representation for word dog NLP - Scalifi Ai
Vector representation for word France NLP - Scalifi Ai
Let's better understand the above examples with a 3D visualization.
I hope the above plot gives a better understanding of what the vectors represent and how similar words can be easily differentiated if the word embedding is trained properly on a clean and well-processed corpus
The above illustration is just for understanding purposes and has been generated by compressing each word vector to have only 3 values (x, y, z) so that we can visualize it.
This visualization gives us a better understanding of the vector overall despite having only 3 dimensions, hence any machine learning model utilizing pre trained word embeddings gets better semantic understanding of words as they have even more dimensions / granularity to play with. Few pre trained word embeddings go up to 300 values per vector for better granularity and performance.
Conclusion
I hope this article serves as an overview of how machines are able to understand natural languages and perform complex calculations over them for applications like Named Entity Recognition, Auto Correct and Autocomplete tasks, Text generation based on prompts, etc.
In the upcoming articles, we will see how we can train our own embeddings, different ways of training (their pros and cons), and use them in real-life applications.
To have your own NLP space and to get a better understanding of AI Concepts without the compulsion of writing code, you can head onto Scalifi Ai, which is a No-Code platform built for creating artificial intelligent pipelines and services.
Frequently Asked Questions
Natural Language Processing (NLP) is a field of artificial intelligence (AI) that focuses on enabling computers to understand, interpret, and generate human language in a way that is both meaningful and contextually relevant.
NLP algorithms utilize a combination of linguistics, statistics, and machine learning techniques to analyze and process human language. These algorithms break down language into its constituent parts, such as words, phrases, and sentences, and then apply various mathematical models to understand and derive meaning from them.
NLP has numerous applications across various industries, including sentiment analysis, machine translation, chatbots, virtual assistants, text summarization, speech recognition, and information extraction from unstructured data sources like social media, emails, and documents.
Some of the main challenges in NLP include dealing with ambiguity in language, understanding context, handling linguistic nuances, accurately representing meaning, and processing large volumes of data efficiently while maintaining accuracy and relevance.
NLP refers to the broader field of processing and manipulating natural language data, while NLU specifically focuses on the task of understanding the meaning and intent behind human language. NLU is a subset of NLP that deals with comprehension and interpretation.
There are several popular NLP tools and libraries available, including NLTK (Natural Language Toolkit), spaCy, Gensim, Stanford NLP, OpenNLP, CoreNLP, and TensorFlow's NLP module. These tools provide pre-built functions and models for various NLP tasks.
Machine learning plays a crucial role in NLP by providing algorithms and techniques for training models to recognize patterns and structures in language data. Supervised learning, unsupervised learning, and deep learning methods are commonly used in NLP tasks.
Sentiment analysis, a common NLP application, involves analyzing text data to determine the sentiment or emotional tone expressed within it. NLP techniques are used to classify text as positive, negative, or neutral, enabling businesses to understand customer opinions, reviews, and feedback.
The future of NLP holds promising advancements, including more accurate language understanding, improved multilingual capabilities, enhanced context awareness, and the integration of NLP with other AI technologies like computer vision and robotics, leading to more intelligent and natural interactions between humans and machines.
To get started with learning NLP, you can begin by studying fundamental concepts in linguistics, statistics, and machine learning. Then, explore NLP-specific resources such as online courses, tutorials, research papers, and hands-on projects using NLP libraries and tools mentioned earlier. Experimenting with real-world datasets and challenges is also an effective way to gain practical experience in NLP.
While NLP focuses on the practical application of computational techniques to process and understand natural language, computational linguistics is a broader interdisciplinary field that encompasses both the theoretical study of language and the development of computational models for linguistic analysis and processing.
Tokenization is the process of breaking down a piece of text into smaller units, typically words or subwords, known as tokens. This step is essential in NLP tasks as it helps in standardizing and structuring the text data for further analysis and processing.
Named Entity Recognition (NER) is a subtask of information extraction that aims to identify and classify named entities within text into predefined categories such as names of persons, organizations, locations, dates, and numerical expressions. NER is commonly used in applications like entity linking, question answering, and document summarization.
NLP techniques play a crucial role in machine translation systems by enabling computers to automatically translate text from one language to another. These systems employ various approaches such as statistical machine translation, neural machine translation, and transformer models to generate accurate and fluent translations by analyzing and understanding the linguistic structure and context of the input text.
Ethical considerations in NLP include issues related to bias and fairness, privacy and data protection, transparency and accountability, as well as societal impacts such as job displacement and cultural implications. Researchers and practitioners in the field are increasingly aware of these concerns and are actively working towards developing responsible and ethical AI systems.
External References
Natural Language Processing
NLP
AI in Education
AI in Healthcare
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