Skip to content

Latest commit

 

History

History
165 lines (96 loc) · 5.49 KB

README.md

File metadata and controls

165 lines (96 loc) · 5.49 KB
Raw

Hits]

Natural-Language-Processing-NLP-Python

Basics of NLP in Python Using spaCy Library

What is NLP?

  • Capability of a Computer to Understand Human Language Or SPeech thereby Interpeting it and doing the required Task.

  • NLP is a Subset of AI - Works on Enabling Computer Systems to Effectively Understand and Process Natural Human Language by developing Computer Programs to Analyze and Process Massive amounts of Natural Language Data.

  • Mainly concerned with Gaining Useful Knowledge and Insights from the Raw Textual Data.

Components of NLP :

  1. NLU : Natural Language Understanding

  2. NLG : Natural Language Generation

What is NLU?

  • Focuses on Understanding anf Observing the Meaning of given User Input. Also Involves in Classifying it into Proper Intents and Entity.

  • Intents : Refers to the Verbs of the User Activities. Mainly used when we want to capture a user Request, or Perform any Action.

  • Entity : Refers to Noun or the Content for which that Action(or Intent) is Performed.

  • Example : Play(Intent) Kishore Kumar Songs(Entity).

3 Tasks or NLU:

  1. Reading Aspect of NLP.
  2. Mapping the given Input into Variable and Usable Representation.
  3. Analysis of Different Language aspects based on Grammar.

Areas of Application of NLU:

  1. Sentiment Analysis : Describing Positive and Negative Indexes.
  2. Topic Classification : Tweet, Message or an E-Mail Type Detection.
  3. Entity Detection : Detecting Nouns in a Text such as Locations, Games or Names, Etc.

Examples of Real World Devices using NLU:

  1. Alexa

  2. Siri

  3. Google Assistant

What is NLG?

  • Described as Translator that Converts the Structured Computer Data into the Corresponding Natural language Representation.

  • Involves the Task of Content or Text Planning, Lexicalization, Sentence Planning, Aggregation, Text Realization.

  • Involves in Establishing Natural Language Outputs from Non-Linguistic Inputs. Deals with Process of Generating Language or Writing Aspect of NLP.

Areas where NLG Comes to Play:

  1. Generating Analytical Reports in Natural Language like English, Spanish, Etc.

  2. Enabling Chatbots to interact in a more Efficient way.

  3. Automated Contents Writing such as Aticles, Stories, Etc.

Applications of NLP:

  • Automated Question - Answering
  • ChatBots
  • Sentiment Analysis
  • Spam Message Detection
  • Machine Translation (e.g Google Translator)
  • Spelling and Grammar Correction
  • Speech-To-Text Conversion & Speech Recognition
  • Information Retrieval & Web Searching

Some Libraries for NLP

  • spaCy

  • scikit-learn

  • Natural language Toolkit(NLTK)

  • Textblob

  • Quepy

Token Attributes:

What is a Token?

  • Token is Defined as each and Every Word along with Punctuations and Symbols present in a Sentence.

  • Example : "Hello! Welcome to my Github Profile." Here Every Word and Punctuation is Defined as Tokens.

Token Attributes

Tag Description
.text The Original Word Text
.lemma_ The Base form of the Word
.pos_ the Simple Part-Of-Speech
.shape_ The Word Shape - Capitalizations, Punctuation, Digits.
.dep_ The Syntactic Dependency - i.e The Relation Between the Tokens.
.is_alpha Is the Token an Alpha Character?
.is_stop Is the Token a Part of Stop list? Is it the Most Common Words of the Language?
.tag_ Detailed Part-Of-Speech

To Perform NLP We require the Following :

  1. Anaconda Navigator - Download Anaconda Navigator Individual Edition. Click Here to Download. Choose Python 3.7

  2. Install spaCy : Follow the Below Steps:

  • Run Anaconda Command Prompt as Administrator

  • Type conda install -c conda-forge spacy

or

pip install -U spacy

  • A list of Packages will be Displayed and Asks our Approval to Proceed Further. Just type y and press Enter

  • Now Download the Specific Model you want, Based on English language

Type the Command python -m spacy download en for Default English Model(50MB).

For Large Library Use:

Type the Command python -m download en_core_web_lg

Then

Type the Command python -m spacy link en_core_web_lg en1 to link the Language model with "en1"

  • You are Good to Go once you see the You can now load the model via spacy.load('en1') in the Anaconda Command Prompt.

Now we can Load the Large Library Model using "en1".

Basics of NLP using Python

  • Library Used : spaCy
Name File
NLP Basic NLP_spaCy_Basics.ipynb

** Will be Updating it Further. Keep Checking! **