Natural Language Processing and its working
Natural language processing (NLP) is the capability of a computer program to comprehend human language as it is communicated. NLP is an essential part of artificial intelligence (AI) as it assists in understanding, interpreting, and tampering with human language.
It’s challenging to develop NLP applications as computers traditionally require humans to ’speak’ to them in a machine language that is precise, clear, and brilliantly organized programming or at least through a set number of clearly articulated voice commands. Human speech as you would know is short of being precise. The language used can be vague, depending on many varied and complex factors, like speed, slang, accent, intent, regional language, and social context.
In order to bridge the gap between human communication and computer understanding, NLP takes its lessons from different streams of education namely computer science and computational linguistics.
Growth and development of Natural Language Processing
NLP has been around, but an increasing interest in human-to-machine communication has acted as a catalyst for rapid advancement in this technology. When the present availability of huge data, powerful computing, and enhanced algorithms come together, the result is magical.
Have you ever wondered how machines have come to understand human language?
Machines that are skilled to comprehend millions of zeroes and ones and structured to deliver logical action can now easily interact with humans who write, read and speak multiple languages.
A classic example is that of Alexa. Now you can speak to Alexa and say, “Alexa this is my favorite song” and the device playing music will lower the volume and respond. “Ok! Saved.” It then feeds your song beneath the favorite songs’ list. Post that, it adapts the algorithms, playing the same song and other similar songs when you listen to music on that station the next time.
Let’s see how human-machine interaction works. The moment you speak, the machine activates and then understands the unsaid intention of your comment, post which it executes an action and gives you feedback in a well-formed English sentence, all in a jiffy. This complete interaction was made possible by Natural Language Processing along with other Artificial Intelligence elements known as machine learning and deep learning.
How does NLP work?
What Natural language processing does is break down the elemental pieces of language as a first step to functioning. Then it employs different techniques for interpreting human language, across the spectrum of statistical and machine learning methods to rules-based and algorithmic approaches. NLP needs a broad range of approaches because the voice and text-based data vary widely, as do the practical applications.
Basic NLP tasks include two main techniques for analysis to complete NLP tasks and they are
1. Syntactic Analysis
The syntax is the grammar of the sentence. It is reflected in the arrangement of words and it assesses how the natural language aligns with grammatical rules. Computer algorithms help to apply rules of grammar to a group of words and help derive meaning out of these words.
• Syntax analysis adopts varied techniques like
✔ Lemmatization (reducing multiple word forms into one)
✔ Morphological segmentation (dividing words into morphemes or single units)
✔ Word segmentation (divide large text into distinct units)
✔ Part of Speech Tagging ( identifying part of speech of every word)
✔ Parsing (does grammar analysis of sentence)
✔ Sentence Breaking (marking sentence boundaries for large text)
✔ Stemming (finding root form of word forms)
2. Semantic Analysis
The meaning conveyed by text is called the Semantics and analysis of semantics is the toughest aspect of Natural Language Processing as it involves emotions and more intangible feelings and expressions. It’s this part that is yet to be resolved fully in spite of breakthrough technologies and advanced computer algorithms being applied in NLP to understand the meaning and interpretation of words and how sentences are structured.
• Semantic analysis techniques include
✔ Named entity recognition (NER): Identifies and categorizes words into preset groups like names of people and names of places etc.
✔ Word sense disambiguation: deciphering meaning based on the context.
✔ Natural language generation: This technique capitalizes on semantic intentions and uses them to transform them into human language.
All these tasks are like the activities one would have done manually in grade school. Simply speaking, NLP breaks down sentences into shorter, basic pieces, it then attempts to explore relationships between the shortened pieces and understand how these pieces work together to create meaning.
These activities are often used in higher-level NLP capabilities and the most often used are as follows:
• Content categorization: It is a linguistic-based tool that provides document summary including search and indexing, content alerts, and duplication detection.
• Discovering topic and modeling: This tool helps to capture the meaning accurately and perfect themes in text collections. Advanced analytics like optimization and forecasting are applied to probe better.
• Contextual extraction of text: This task automatically pulls structured information from any text-based sources.
• Sentiment analysis: This analysis helps in identifying the mood or subjective opinions within large amounts of text. This includes deciphering average sentiment and opinion mining.
• Text-to-speech and speech-to-text conversion. This is useful to transform the written text into voice command and speech into text and vice versa.
• Document summarization: This automatically generates abstracts of large bodies of text.
• Machine translation: This automatically translates text or speech from one language to another.
In all the above-mentioned tasks that are possible, the central focus is to transform the raw language input by using linguistics and algorithms, enrich the text in such a way that it delivers greater value when processed for the identification or while executing any task asked of it: Thereby, empowering computers to understand language just as we do.
The main objective of Natural Language Processing is to read, decipher, understand, and make sense of human languages in a manner that is valuable. These techniques help machine learning to derive meaning from human languages. As research persists, advancements ahead will empower machines and make them smarter at comprehending human languages, enhancing the functionality of machine-human interactions and applications.