Natural Language Processing is the branch of Artificial Intelligence that gives machines the ability to read, understand, and derive meaning from human languages.
Definition :
NLP stands for Natural Language Processing. It is a field of artificial intelligence and computational linguistics that focuses on enabling computers to understand, interpret, and generate human language in a way that is both meaningful and valuable. NLP involves the interaction between computers and human language, encompassing tasks such as language understanding, language generation, language translation, sentiment analysis, and more.
NLP aims to bridge the gap between human communication and computer understanding, allowing machines to process and analyze large amounts of textual data, extract insights, and perform various language-related tasks
NLP combines the field of linguistic and computer science to decrypt language structure and guidelines and to make models that can comprehend, break down and separate significant details from text and speech
Humans interact with each other through various forms of mediums transferring vast amounts of data. This data is very useful in terms of understanding customer behavior and learning customer habits.
This data is mostly unstructured and data scientist uses this data to train machines, to understand human linguistics.
Understanding NLP :
NLP encompasses a wide range of tasks, from language understanding to language generation, and it forms the foundation for various applications that involve processing and analyzing text or speech data.
NLG (Natural Language Generation) and NLU (Natural Language Understanding) are two key components of NLP that focus on different aspects of working with human language.
- Natural Language Generation (NLG): NLG is about generating human-like language from structured data or concepts.
Example of NLG: Imagine an e-commerce platform that generates product descriptions for various items based on their attributes. An NLG system could take information like product specifications, features, and customer reviews, and transform it into a well-written product description.
- Natural Language Understanding (NLU): NLU is about extracting meaning and intent from unstructured human language input.
Example of NLU: Consider a virtual assistant like Siri or Google Assistant. When you ask, "What's the weather like today?", the NLU component of these assistants needs to understand your intent (asking for the weather) and extract relevant information (today's weather forecast) from your input. NLU helps the system grasp the user's intention and respond appropriately.
How NLP works:
NLP algorithm is a big chunk of several small algorithms. In normal condition, a machine is given unstructured data, like a simple sentence, which it needs to interpret and generate human-like answers. Let us understand how NLP works using our sample sentence above
Example sentence: There are townhouses available for rent in Nashville's downtown
1. Tokenization :
The first step in NLP involves preparing the raw text for analysis. This often includes tasks like tokenization (breaking text into words or subword units), removing punctuation, converting text to lowercase, and handling special characters. This step aims to create a structured representation of the text that can be easily processed.
In our case - our sentence will be tokenized as
"There", "are", "townhouses", "available", "for", "rent", "in", "Nashville's", "downtown", "."
During tokenization, the sentence is further neutralized by following below processes
1.1 Removal of Stop Words
At this stage, from the sentence all words which do not add much meaning to our sentences, are removed from the sentence. Some example of stop words are "and", "is", "are", "as", "the" etc..
In our case - our sentence after removing stop-words will look like
"townhouses", "available", "rent", "Nashville's", "downtown", "."
1.2. Stemming
The next step is to further normalize the sentence by identifying Stem words for each token. The machines are made built with a dictionary of every word which can mean the same because of it added prefixes or suffixes.
For example: "ran", "runs" and "running" - for these three different words a STEM word "run" will be defined.
BUT, stemming does not work for every token.
For example, the words "universal" and "university" would not stem from "universe". In such cases, the machine goes through the process of Lemmatization.
1.3 Lemmatization
In this case, the machine takes the token and looks for its meaning from a dictionary and then stems down to its root stem word.
2. Part-of-Speech Tagging:
In this step - each token is assigned a part-of-speech tag to indicate its grammatical role in the sentence:
In our case - our sentence looks like
"townhouses" (NOUN), "available" (ADJective), "rent" (NOUN), \ "Nashville's" (NOUN), "downtown" (NOUN)
3. Semantic Analysis:
The sentence's meaning is interpreted, taking into account the relationships established during syntactic analysis where The structure of the sentence is determined, identifying relationships between words. The sentence conveys that there are townhouses that can be rented in the downtown area of Nashville.
4. Named Entity Recognition (NER):
The NER step identifies named entities (people, places, organizations, etc.) in the sentence:
In case of our sentence:
"Nashville" is recognized as a location entity.
5. Contextual Understanding (Transformer Model):
If a transformer model like BERT or GPT is used, it would analyze the sentence bidirectionally, considering the context of all words. This contextual understanding helps the model grasp the subtle nuances and relationships in the sentence.
Applications of NLP
Language Translation: NLP powers the technology behind language translation services like Google Translate (https://translate.google.com/), allowing people to communicate across language barriers effortlessly.
Sentiment Analysis: Businesses use NLP to gauge public sentiment by analyzing social media posts, customer reviews, product feedback, and news articles, helping them make informed decisions and tailor their strategies.
Chatbots and Virtual Assistants: This is definitely a breakthrough, where now every time you call for assistance, you interact with robots instead of humans. NLP drives the conversational abilities for chatbots on websites, enhancing customer interactions and providing instant assistance.
Text Summarization: NLP algorithms are used to condense lengthy articles, documents, and reports into concise summaries, aiding in information extraction and quick comprehension. For- example Chat GPT
Speech Recognition: Voice-activated devices, like Siri, Alexa, and Hey-Google, are speech-to-text applications that rely on NLP for accurate speech recognition, enabling hands-free communication and transcription.
Named Entity Recognition: NLP helps identify and classify entities such as names of people, organizations, and locations in text, useful for information extraction and knowledge management.
Challenges in NLP
Despite its impressive capabilities, NLP faces several challenges:
Ambiguity of sentences.
Contextual Understanding
For example the word phrase “What?” when used with different emotions can mean different.
- When a person in Shock – will say the phrase “What?” with eyes widely open.
- When a person is confused – will say the phrase “What?” with eyebrows shrunk or tilted.
- When a person is surprised – will say the phrase “What?” with his mouth open and wide eyes.
We, humans, can read facial expressions and phrases to understand a sentence with emotion – but for machines, this is still a challenge and needs more explanation of how to capture words with emotions.
But nevertheless, NLP is a fast-growing sector in AI and on-demand skill. One of the most significant breakthroughs in NLP is the development of transformer models, which have revolutionized the field. Models like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer) have set new benchmarks in tasks like language understanding, machine translation, and text generation. These models leverage vast amounts of training data and extensive computational resources to achieve remarkable performance on a variety of NLP tasks.
NLP techniques often leverage machine learning approaches, including deep learning, recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer models, like the ones used in the GPT series developed by OpenAI.
Conclusion
Natural Language Processing has brought us closer to the dream of seamless communication between humans and machines. Consider translating languages on the fly, or searching via speech to bring personalized customer experience in apps NLP has woven itself into the fabric of modern society.
Let us understand how a simple sentence is broken down to make a machine understand what it means.
