HOW TO START WITH NLP AS A BEGINNER?

Wondering how your Alexa or Siri is working? How does Gmail classifies emails? That’s NLP(Natural Language Processing). Eager to learn it? Then here is a blog to start with NLP!

NLP — Natural Language Processing is a process that enables the computer to understand human language that is spoken or written. Yes, It is a part of Artificial Intelligence that invokes the language understanding capability of a machine. Ever wondered about the technology behind Siri, Alexa, Multi-Language Chatbots? The general concept of these kinds of stuff is to understand human language. Well, Then Assistants are the only use case of NLP? No, we have other scenarios like understanding doctor’s prescription, summarizing content, Sentiment Analysis, Filtering and classifying emails, Identifying fake news, and a lot more wherever there encounters natural language.

Why should I learn it?

We are living in a world of the digital era and growing AI field. We always rely on things to be simple, fast, and automatic. It is a field that understands human’s informal inputs. So, it is pretty good to learn for an AI career.

A Note for Bright Scope:

1. Companies like Yahoo and Google filter and classify your emails with NLP by analyzing text in emails that flow through their servers and stopping spam before they even enter your inbox.
2. Amazon’s Alexa and Apple’s Siri are examples of intelligent voice-driven interfaces that use NLP to respond to vocal prompts.
3. IBM developed a cognitive assistant that works like a personalized search that learns all about you and reminds them whenever needed.
4. NLP is particularly booming in the healthcare industry. This technology is used to diagnose diseases and bringing the cost down while the healthcare industries are shifting to electronic health records.
5. Stanford University developed Weobot, a chatbot therapist to help people with anxiety and other disorders.
6. Winterlight labs are making improvements in the treatment of Alzheimer’s disease by monitoring cognitive impairment through speech.

Seems like all the Big giants use NLP in their career path.

Eager to learn it? Where do I start then?

Let me just sort it out for you.

There are mainly two components of NLP :

1. Natural Language Understanding

It helps the machine by understanding the human language. It is done by extracting concepts, entities, keywords, semantic roles, emotions, and relations. It causes some ambiguity namely

I) Lexical Ambiguity — When there is more than one sense for a word in a sentence and the meaning of the sentence depends on the sense of the word. This case is called Lexical Ambiguity.

II) Syntactical Ambiguity — When there is more than one meaning for a sequence of words. This case is called Syntactical Ambiguity.

III) Referential Ambiguity — when a word or phrase in the context of a particular sentence, could refer to two or more properties or things. It is sometimes clear from the context which meaning is intended, but not always. This case is called Referential Ambiguity.

2. Natural Language Generation

It acts as a translator that converts computerized data into natural language. It mainly involves Text planning, Sentence planning, and Text Realization.

I) Text Planning — retrieving the relevant content from the knowledge base.

II) Sentencing Planning — It involves choosing required words to form a meaningful word.

III) Text Realization — It maps the sentence plan to a sentence structure.

How does NLP process the Natural language?

Two main techniques involved in Natural Language Processing

1. Syntax Analysis

The syntax analysis is used to reframe words with grammatical sense. It is done by using the following techniques,

1. Parsing — It is the grammatical analysis of a sentence. It breaks the sentence into parts of speech.
2. Word Segmentation — It is the act of taking a string of text and deriving words from it.
3. Sentence breaking — It does the breaking of a sentence in the case of large text.
4. Morphological Segmentation — It divides the word into smaller parts called Morphemes.
5. Stemming — It divides the word with inflection in them to their root forms.

2. Semantic Analysis

It analyzes the meaning of the words given in a sentence and makes use of it. It understands the meaning and structure of sentences. It is done using the following techniques

1. Word Sense Disambiguation — This derives the meaning of the word based on the context given.
2. Named Entity Recognition — This determines the words that can be categorized into a group called entity. It is used to identify a specific word mentioned everywhere in a document. Using the semantics of the text (meaning of the text), it would be able to differentiate between entities that are visually the same.
3. Natural Language Generation — This uses a database (Knowledge base) to determine the semantics behind each word to generate a new text.

How can I deal with NLP on the practical end?

We have three tools to deal with NLP.

1. NLTK

It is an open-source Python module with datasets and tutorials.

2. Genism

It is a Python library for topic modeling and document indexing.

What is Topic Modeling?

It is a kind of statistical model for finding abstract things that occur in the document collection.

What is Document indexing?

It is the process of associating the information with a file, allowing it to be easily found and retrieved later.

3. Intel NLP Architect

It is a Python library for deep learning topologies and techniques.

Despite all the big breakthroughs of NLP there are some challenges to be faced.

1. Precision in output
2. It requires a large amount of trained data for the semantic analysis to be smooth.
3. What if the tone of voice is low? What if the text is ambiguous? This is a challenge for semantic analysis.
4. Evolving use of language

Here is a hands-on which does summarization of the text given.

Click Here !

This is just a start!

Explore more and learn the tech.

Happy Learning!