Natural Language Processing (NLP) is applying Machine Learning models to text and language. Teaching machines to grasp what’s said within the spoken and written word is the focus of Natural Language Processing.
Every time when you dictate something into your iPhone/Android device it truly is then converted to text, that’s an NLP set of rules in action.
In this blog, we are going to cover everything about:
What Is Natural Language Processing?
- Natural language processing (NLP) refers to the branch of computer science and more specifically, the department of AI, concerned with giving computers the capacity to recognize the text and spoken phrases in a great deal the same way people can.
- We can use NLP to developing systems like machine translation, speech recognition, spam detection, sentiment analysis, text simplifications, and plenty more.
- Few Real-Time examples include:
- Google Translate
- Grammarly
- Cortana
- Alexa
- OK Google
How Does Machine Understand Text?
- We do know that machines only understand numbers i.e. 010101 not words or sentences.
- So, before building Natural language processing models, we want to specialize in an intermediate step, which is the text representation.
- Text representation was created on a basic idea, which is one-hot encodings.
- In one-hot encodings, a sentence is represented as a matrix of shape (NxN) where N represents the number of particular tokens within the sentence.
- For example, in the below picture, each word is expressed as sparse vectors except one cell (occurrences of the word in the sentence).
Parts of NLP (Natural Language Processing)
1) Lexical Analysis: With Lexical Analysis, we divide a complete part of the text into paragraphs, sentences, and words, which involves identifying and analyzing the structure of words.
2) Syntactic evaluation: The syntactic analysis includes the evaluation of phrases in a sentence for grammar and arranging phrases in a way that suggests the connection of many of the phrases. as an example, the sentence “the shop is going to the house” does no longer passes.
3) Semantic evaluation: Semantic evaluation attracts the precise meaning for the words, and it analyzes the textual content meaningfulness. Sentences that include “hot ice-cream” do now not skip.
4) Disclosure Integration: Disclosure integration takes into consideration the context of the text. It considers the which means of the sentence before it ends. as an instance: “He works at Google.” in this sentence, “he” needs to be referenced within the sentence earlier than it.
5) Pragmatic evaluation: Pragmatic evaluation offers normal conversation and interpretation of language. It offers with deriving meaningful use of language in diverse conditions.
Natural Language Processing Algorithms
Before diving into the coding part, we will first understand the concepts beneath them. The foremost frequently used algorithms in Natural language processing when defining the vocabulary of terms:
1) Bag of Words
- Is a regularly used model that permits us to count all words in a piece of text. Basically, it makes an occurrence matrix for the sentence or document, disregarding grammar, and word order.
- These word occurrences or frequencies are then used as features for training a classifier.
2) Tokenization
- The process of separating the running text into sentences and words is termed Tokenization. Basically, it’s the task of cutting a text into items called tokens, and at the same time push away certain characters, like punctuation.
3) Lemmatization
- Lemmatization settles words into their dictionary form for which it needs detailed dictionaries in which the algorithm can view and link words to their corresponding form.
4) Stemming
- In this process, we slicing the end or the beginning of words with the purpose of removing affixes.
- The issue is that affixes can make or expand new forms of the same word or even make new words themselves.
Python Library For Text Processing
- In Python, Natural Language Toolkit (NLTK) is a known open-source package, which allows us to run all common Natural language processing tasks.
- It comes with easy-to-use interfaces and a suite of text processing libraries like tokenization, tagging, classification, and semantic reasoning.
Text Preprocessing
It is required to preprocess the data before making models and analysis. In simple words preprocessing convert raw text data into a logical format for the computer.
1) Let’s first create a data frame (df) to do some hands-on preprocessing of text data.
2) Then make text in lowercase, to make all the data in a uniform format.
3) Punctuation doesn’t add any additional information. This step shortens the size of the data and therefore increases computational efficiency.
4) Changing numbers into words or removing numbers. Eliminate numbers if they are not significant or change to words.
5) Stop words are very natural words in the sentence that carry no meaning or less meaning compared to other keywords. If we eliminate the words that are less commonly used, we can focus on meaningful keywords instead.
6) Tokenizing text an essential step in text preprocessing where the text is split into basic meaningful units.
7) At the end of Text preprocessing, we apply lemmatization which extracting a root word by considering the vocabulary.
8) Text Preprocessing is completed, now you can use this data for more complex NLP tasks.
Related References
- Introduction to Recurrent Neural Networks (RNN)
- Introduction To Data Science and Machine Learning
- Deep Learning Vs Machine Learning
- Introduction to Artificial Neural Network in Python
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