Introduction to NLP
NLP stands for Natural Language Processing. It is a subfield or branch of Artificial intelligence (AI) that enables computers to understand human languages and process them in a manner that is valuable. It concerns with the interactions between human spoken (natural) languages like English and computers.
What is NLP?
Natural Language Processing helps computers to observe, analyze, understand and derive valuable meaning from natural or human spoken languages. Tasks like translation, automatic summarization, and relationship extraction, speech recognition, named entity recognition, topic segmentation, and sentiment analysis can be performed by developers using Natural language processing (NLP). However, NLP applications are hard or challenging as computers require humans to interact with them using programming languages like Java, Python, etc., which are structured and unambiguous. But human spoken languages are ambiguous and changes with regional or social change; hence it becomes difficult to train computers to understand natural languages.
Understanding
There are many difficulties faced in natural language processing; hence it is characterized as hard in computer science. One major problem that NLP faces is that many words have multiple meanings. These can be distinguished easily by humans but are difficult for computers. For example, the word ‘board’ can mean a chopping board, blackboard or board of directors. The difference is understood by the sentence preceding it or words that surrounds ‘board’.
- He is the head of the board.
- She solved the problem on the board.
Humans would understand it easily that ‘board’ in the first sentence refers to the board of directors, and in the second one, it refers to a blackboard, but it is not the case with computers.
Despite these difficulties, computers are enhancing their ability to understanding human languages. Knowledge of various linguistic fields helps to speed up this process:
- Morphology: It is the study of the formation of words from basic or primitive units.
- Morpheme: It refers to a basic unit of meaning in a language.
- Phonology: It is the study of systems of sounds.
- Syntax: It refers to the way words and phrases are arranged to form a sentence.
- Semantics: It refers to the meaning of the words and the sentences.
- Pragmatics: It refers to understanding, interpreting and using sentences in different situations.
- Discourse: It refers to how the preceding sentence affects the meaning or interpretation of the next sentence.
- World Knowledge: It refers to the general knowledge about the world.
Working with NLP
The process of Natural Language Processing can be done in three steps:
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- First, speech–to–text process.
- Part-of-speech tagging
- Text to speech conversion.
Let us understand each step one by one:
1. Speech-to-text process:
It refers to reading the written text in human or natural language. It is a process of analyzing and understanding the natural language received by the computer as input. The computer uses various algorithms or built-in statistical models that perform speech recognition, which helps convert the received natural language into a programming language. This is done by performing Lexical Analysis, which divides the whole received data into small sentences or words.
2. Part-of-speech tagging or Word Category disambiguation:
In this process, grammatical forms such as nouns, adjectives, verbs, tenses, etc., are identified using Lexicon rules called syntactic analysis. It also draws the exact or dictionary meaning of the text called semantic analysis. Finally, it also performs discourse Integration which interprets the meaning of a sentence according to the preceding sentence.
3. Text-to-speech conversion:
In this step, the text in a programming language is converted into a textual or audible format valuable to the user.
Advantages of NLP
- Automatic summarization
- Coreference resolution
- Discourse Analysis
- Relieves the burden of syntax learning
- No training required
- Allows non-programmers to interact with computing systems
- Common synonyms of input can be interpreted
- Better and efficient results
- Search processing tells what users exactly mean
- More data mining means more data for growth
- Can perform complex search
- Contextual understanding
Required Skills
- Programming skills
- Knowledge about machine learning
- Common NLP problems
- NLP tools
- Ability to understand research papers
- Building prototypes
- Capability to learn new stuff
- Software engineering skills
- Probability and statistics
- Linguistic knowledge
- Recursive neural networking
Why do we use NLP?
- For machine translation
- For automatic summarization
- For sentiment analysis
- For text classification
- For question answering
- For language modeling
- For speech recognition
- For caption generation
- For Named Entity Recognition (NER)
- For Part- Of- Speech Tagging
- For Semantic Parsing
- For Paraphrase Detection
- For character recognition
- For spell checking
- For Chatbots
- For customer service
- For market intelligence
How will this technology help you in career growth?
The growing applications and interest in the field of natural language processing have created various opportunities for professionals specializing in Machine Learning, Computational Linguistics, and Data Science. Big companies like Google, Facebook, Forte Group, Sony Ericsson, Merrill Lynch, British Airways, Sainsbury, J.P. Morgan, Celtic, American Express, Ernst & Young, and many more hire natural language processing analysts and experts.
Various job roles related to Natural language processing are NLP scientists, NLP engineers, NLP architects, NLP applied research scientists, cognitive data scientists, Voice over artists and many more. The average salary for jobs related to natural language processing ranges from $76,343 per year to $142,912 per year.
Conclusion
If you are interested in interacting with computing systems and have programming and linguistic knowledge, learning, natural language processing is valuable. Moreover, due to an increase in data and the need to interact with computers, the need for natural language processing is increasing day by day, and various job opportunities are coming into the market with handsome packages. Therefore there is a great scope of NLP in the future.
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