NLP tutorial provides basic and advanced concepts of the NLP tutorial. Our NLP tutorial is designed for beginners and professionals.
What is NLP?
NLP stands for Natural Language Processing, which is a part of Computer Science, Human language, and Artificial Intelligence. It is the technology that is used by machines to understand, analyse, manipulate, and interpret human's languages. It helps developers to organize knowledge for performing tasks such as translation, automatic summarization, Named Entity Recognition (NER), speech recognition, relationship extraction, and topic segmentation.
History of NLP
(1940-1960) - Focused on Machine Translation (MT)
The Natural Languages Processing started in the year 1940s.
1948 - In the Year 1948, the first recognisable NLP application was introduced in Birkbeck College, London.
1950s - In the Year 1950s, there was a conflicting view between linguistics and computer science. Now, Chomsky developed his first book syntactic structures and claimed that language is generative in nature.
In 1957, Chomsky also introduced the idea of Generative Grammar, which is rule based descriptions of syntactic structures.
(1960-1980) - Flavored with Artificial Intelligence (AI)
In the year 1960 to 1980, the key developments were:
Augmented Transition Networks (ATN)
Augmented Transition Networks is a finite state machine that is capable of recognizing regular languages.
Case Grammar was developed by Linguist Charles J. Fillmore in the year 1968. Case Grammar uses languages such as English to express the relationship between nouns and verbs by using the preposition.
In Case Grammar, case roles can be defined to link certain kinds of verbs and objects.
For example: "Neha broke the mirror with the hammer". In this example case grammar identify Neha as an agent, mirror as a theme, and hammer as an instrument.
In the year 1960 to 1980, key systems were:
SHRDLU is a program written by Terry Winograd in 1968-70. It helps users to communicate with the computer and moving objects. It can handle instructions such as "pick up the green boll" and also answer the questions like "What is inside the black box." The main importance of SHRDLU is that it shows those syntax, semantics, and reasoning about the world that can be combined to produce a system that understands a natural language.
LUNAR is the classic example of a Natural Language database interface system that is used ATNs and Woods' Procedural Semantics. It was capable of translating elaborate natural language expressions into database queries and handle 78% of requests without errors.
1980 - Current
Till the year 1980, natural language processing systems were based on complex sets of hand-written rules. After 1980, NLP introduced machine learning algorithms for language processing.
In the beginning of the year 1990s, NLP started growing faster and achieved good process accuracy, especially in English Grammar. In 1990 also, an electronic text introduced, which provided a good resource for training and examining natural language programs. Other factors may include the availability of computers with fast CPUs and more memory. The major factor behind the advancement of natural language processing was the Internet.
Now, modern NLP consists of various applications, like speech recognition, machine translation, and machine text reading. When we combine all these applications then it allows the artificial intelligence to gain knowledge of the world. Let's consider the example of AMAZON ALEXA, using this robot you can ask the question to Alexa, and it will reply to you.
Advantages of NLP
Disadvantages of NLP
A list of disadvantages of NLP is given below:
Components of NLP
There are the following two components of NLP -
1. Natural Language Understanding (NLU)
Natural Language Understanding (NLU) helps the machine to understand and analyse human language by extracting the metadata from content such as concepts, entities, keywords, emotion, relations, and semantic roles.
NLU mainly used in Business applications to understand the customer's problem in both spoken and written language.
NLU involves the following tasks -
2. Natural Language Generation (NLG)
Natural Language Generation (NLG) acts as a translator that converts the computerized data into natural language representation. It mainly involves Text planning, Sentence planning, and Text Realization.
Note: The NLU is difficult than NLG.
Difference between NLU and NLG
Applications of NLP
There are the following applications of NLP -
1. Question Answering
Question Answering focuses on building systems that automatically answer the questions asked by humans in a natural language.
2. Spam Detection
Spam detection is used to detect unwanted e-mails getting to a user's inbox.
3. Sentiment Analysis
Sentiment Analysis is also known as opinion mining. It is used on the web to analyse the attitude, behaviour, and emotional state of the sender. This application is implemented through a combination of NLP (Natural Language Processing) and statistics by assigning the values to the text (positive, negative, or natural), identify the mood of the context (happy, sad, angry, etc.)
4. Machine Translation
Machine translation is used to translate text or speech from one natural language to another natural language.
Example: Google Translator
5. Spelling correction
Microsoft Corporation provides word processor software like MS-word, PowerPoint for the spelling correction.
6. Speech Recognition
Speech recognition is used for converting spoken words into text. It is used in applications, such as mobile, home automation, video recovery, dictating to Microsoft Word, voice biometrics, voice user interface, and so on.
Implementing the Chatbot is one of the important applications of NLP. It is used by many companies to provide the customer's chat services.
8. Information extraction
Information extraction is one of the most important applications of NLP. It is used for extracting structured information from unstructured or semi-structured machine-readable documents.
9. Natural Language Understanding (NLU)
It converts a large set of text into more formal representations such as first-order logic structures that are easier for the computer programs to manipulate notations of the natural language processing.
How to build an NLP pipeline
There are the following steps to build an NLP pipeline -
Step1: Sentence Segmentation
Sentence Segment is the first step for building the NLP pipeline. It breaks the paragraph into separate sentences.
Example: Consider the following paragraph -
Independence Day is one of the important festivals for every Indian citizen. It is celebrated on the 15th of August each year ever since India got independence from the British rule. The day celebrates independence in the true sense.
Sentence Segment produces the following result:
Step2: Word Tokenization
Word Tokenizer is used to break the sentence into separate words or tokens.
JavaTpoint offers Corporate Training, Summer Training, Online Training, and Winter Training.
Word Tokenizer generates the following result:
"JavaTpoint", "offers", "Corporate", "Training", "Summer", "Training", "Online", "Training", "and", "Winter", "Training", "."
Stemming is used to normalize words into its base form or root form. For example, celebrates, celebrated and celebrating, all these words are originated with a single root word "celebrate." The big problem with stemming is that sometimes it produces the root word which may not have any meaning.
For Example, intelligence, intelligent, and intelligently, all these words are originated with a single root word "intelligen." In English, the word "intelligen" do not have any meaning.
Step 4: Lemmatization
Lemmatization is quite similar to the Stamming. It is used to group different inflected forms of the word, called Lemma. The main difference between Stemming and lemmatization is that it produces the root word, which has a meaning.
For example: In lemmatization, the words intelligence, intelligent, and intelligently has a root word intelligent, which has a meaning.
Step 5: Identifying Stop Words
In English, there are a lot of words that appear very frequently like "is", "and", "the", and "a". NLP pipelines will flag these words as stop words. Stop words might be filtered out before doing any statistical analysis.
Example: He is a good boy.
Note: When you are building a rock band search engine, then you do not ignore the word "The."
Step 6: Dependency Parsing
Dependency Parsing is used to find that how all the words in the sentence are related to each other.
Step 7: POS tags
POS stands for parts of speech, which includes Noun, verb, adverb, and Adjective. It indicates that how a word functions with its meaning as well as grammatically within the sentences. A word has one or more parts of speech based on the context in which it is used.
Example: "Google" something on the Internet.
In the above example, Google is used as a verb, although it is a proper noun.
Step 8: Named Entity Recognition (NER)
Named Entity Recognition (NER) is the process of detecting the named entity such as person name, movie name, organization name, or location.
Example: Steve Jobs introduced iPhone at the Macworld Conference in San Francisco, California.
Step 9: Chunking
Chunking is used to collect the individual piece of information and grouping them into bigger pieces of sentences.
Phases of NLP
There are the following five phases of NLP:
1. Lexical Analysis and Morphological
The first phase of NLP is the Lexical Analysis. This phase scans the source code as a stream of characters and converts it into meaningful lexemes. It divides the whole text into paragraphs, sentences, and words.
2. Syntactic Analysis (Parsing)
Syntactic Analysis is used to check grammar, word arrangements, and shows the relationship among the words.
Example: Agra goes to the PoonamIn the real world, Agra goes to the Poonam, does not make any sense, so this sentence is rejected by the Syntactic analyzer.
3. Semantic Analysis
Semantic analysis is concerned with the meaning representation. It mainly focuses on the literal meaning of words, phrases, and sentences.
4. Discourse Integration
Discourse Integration depends upon the sentences that proceeds it and also invokes the meaning of the sentences that follow it.
5. Pragmatic Analysis
Pragmatic is the fifth and last phase of NLP. It helps you to discover the intended effect by applying a set of rules that characterize cooperative dialogues.
For Example: "Open the door" is interpreted as a request instead of an order.
Why NLP is difficult?
NLP is difficult because Ambiguity and Uncertainty exist in the language.
There are the following three ambiguity -
Lexical Ambiguity exists in the presence of two or more possible meanings of the sentence within a single word.
Manya is looking for a match.
In the above example, the word match refers to that either Manya is looking for a partner or Manya is looking for a match. (Cricket or other match)
Syntactic Ambiguity exists in the presence of two or more possible meanings within the sentence.
I saw the girl with the binocular.
In the above example, did I have the binoculars? Or did the girl have the binoculars?
Referential Ambiguity exists when you are referring to something using the pronoun.
Example: Kiran went to Sunita. She said, "I am hungry."
In the above sentence, you do not know that who is hungry, either Kiran or Sunita.
How to implement NLP
There are the following methods to implement NLP -
Natural Language Processing APIs allow developers to integrate human-to-machine communications and complete several useful tasks such as speech recognition, chatbots, spelling correction, sentiment analysis, etc.
A list of NLP APIs is given below:
Scikit-learn: It provides a wide range of algorithms for building machine learning models in Python.
Natural language Toolkit (NLTK): NLTK is a complete toolkit for all NLP techniques.
Pattern: It is a web mining module for NLP and machine learning.
TextBlob: It provides an easy interface to learn basic NLP tasks like sentiment analysis, noun phrase extraction, or pos-tagging.
Quepy: Quepy is used to transform natural language questions into queries in a database query language.
SpaCy: SpaCy is an open-source NLP library which is used for Data Extraction, Data Analysis, Sentiment Analysis, and Text Summarization.
Gensim: Gensim works with large datasets and processes data streams.
Difference between Natural language and Computer Language
Before learning NLP, you must have the basic knowledge of Python.
Our NLP tutorial is designed to help beginners.
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