In the last few years, Natural Language Processing (NLP) has become the focus of Artificial Intelligence with the introduction of game-changing new applications and models. This article will also provide a brief on AI certification tests. So, keep reading to know more about the current rage.
Technically, Natural Language Processing (NLP) is the union of Artificial Intelligence (AI), Computer Science, and human language (Generally, English). It is the study through which you can teach computers how human beings inherently write, talk, and speak.
Now that the technical definition is out there, what do we really mean by NLP, which forms an important part of AI and Machine Learning courses?
Real-world Examples of NLP
Without realizing it, you’ve come across NLP more often than you’d admit. The virtual assistants on your smartphones like Google Assistant and Siri are the most common examples of NLP. They carefully listen to what you say and process that information in a language intelligible to their systems.
So, generally, NLP can be used for Voice Commands, Text-to-Speech, Chatbots, Search Engines, Language Translation, Sentiment Analysis, and Spelling Checks.
What is Natural Language Processing?
Humans generally communicate with each other using natural language. Processing refers to the action of converting data into a form that can be easily understood by machines. Combining these two terms, we get Natural Language Processing which helps computers communicate with people in their natural language.
How does NLP work?
We use NLP software for pre-processing sentences in a natural language and structure that can be used for interpretation by machines.
Must-know Terms in NLP
Let us understand the most important concepts in NLP through the example of this sentence: “She doesn’t sing, but my son is a vocalist.”
- Tokenization: In this process, a whole text is divided into tokens. A word tokenizer separates words, and the sentence tokenizer separates sentences.
Example: Word-tokenize: “She”, “does”, “not”, “sing”, “but”, “my”, “son”, “is”, “a”, “vocalist”.
- Stopwords: These words don’t add any meaning to the given sentence. The library “nltk.corpus” contains a list of stopwords. By importing this library, you can derive a sentence without stopwords.
Example: Stopwords: “she”, “doesn’t”, “but”, “my”, “but”, “is”, “a”.
- Part of Speech Tagging (POS Tagging): Here, the words are tagged based on the part of speech they represent.
- She: Personal pronoun
- Does: Verb
- Not: Negative particle
- Sing: Verb
- But: Conjunction
- My: Possessive pronoun
- Son: Noun
- Is: Preposition
- A: Article
- Vocalist: Noun
- Bag of Words: Once the sentence is cleaned, it’s converted into vectors (numerical representation) for feeding into the ML model. We do this using predefined python libraries.
- Stemming: Here, the words are reduced to the root form.
Example: In this sentence, only “does” is converted to “do.”
- WordNet: This is the dictionary for English that is made for NLP. You can use it to find synonyms and antonyms.
- Lemmatizer: It works similarly to stemming except for returning a word that makes sense.
- Stemming: Vocalist - vocal
- Lemmatizing: Vocalist - voice
Importance of NLP for AI Certification
NLP is essential for AI as it makes human language legible for machines. This process helps in creating structured data for software performing text analytics, speech recognition, etc.
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