Natural Language Processing (NLP) Natural language processing strives to build machines
that understand and respond to text or voice data—and respond with text or speech of their
own—in much the same way humans do. What is natural language processing? Natural
language processing (NLP) refers to the branch of computer science—and more specifically,
the branch of artificial intelligence or AI—concerned with giving computers the ability to
understand text and spoken words in much the same way human beings can.
NLP combines computational linguistics—rule-based modelling of human language—with
statistical, machine learning, and deep learning models. Together, these technologies enable
computers to process human language in the form of text or voice data and to ‘understand’ its
full meaning, complete with the speaker or writer’s intent and sentiment. NLP drives
computer programs that translate text from one language to another, respond to spoken
commands, and summarize large volumes of text rapidly—even in real time. There’s a good
chance you’ve interacted with NLP in the form of voice-operated GPS systems, digital
assistants, speech-to-text dictation software, customer service chatbots, and other consumer
conveniences. But NLP also plays a growing role in enterprise solutions that help streamline
business operations, increase employee productivity, and simplify mission-critical business
processes.
NLP tasks Human language is filled with ambiguities that make it incredibly difficult to write
software that accurately determines the intended meaning of text or voice data. Homonyms,
homophones, sarcasm, idioms, metaphors, grammar and usage exceptions, variations in
sentence structure— these just a few of the irregularities of human language that take humans
years to learn, but that programmers must teach natural language-driven applications to
recognize and understand accurately from the start, if those applications are going to be
useful. Several NLP tasks break down human text and voice data in ways that help the
computer make sense of what it's ingesting. Some of these tasks include the following:
• Speech recognition, also called speech-to-text, is the task of reliably converting voice data
into text data. Speech recognition is required for any application that follows voice
commands or answers spoken questions. What makes speech recognition especially
challenging is the way people talk—quickly, slurring words together, with varying emphasis
and intonation, in different accents, and often using incorrect grammar.
• Part of speech tagging, also called grammatical tagging, is the process of determining the
part of speech of a particular word or piece of text based on its use and context. Part of
speech identifies ‘make’ as a verb in ‘I can make a paper plane,’ and as a noun in ‘What make
of car do you own?’
• Word sense disambiguation is the selection of the meaning of a word with multiple
meanings through a process of semantic analysis that determine the word that makes the most
sense in the given context. For example, word sense disambiguation helps distinguish the
meaning of the verb 'make' in ‘make the grade’ (achieve) vs. ‘make a bet’ (place).
• Named entity recognition, or NEM, identifies words or phrases as useful entities. NEM
identifies ‘Kentucky’ as a location or ‘Fred’ as a man's name.
• Co-reference resolution is the task of identifying if and when two words refer to the same
entity. The most common example is determining the person or object to which a certain
that understand and respond to text or voice data—and respond with text or speech of their
own—in much the same way humans do. What is natural language processing? Natural
language processing (NLP) refers to the branch of computer science—and more specifically,
the branch of artificial intelligence or AI—concerned with giving computers the ability to
understand text and spoken words in much the same way human beings can.
NLP combines computational linguistics—rule-based modelling of human language—with
statistical, machine learning, and deep learning models. Together, these technologies enable
computers to process human language in the form of text or voice data and to ‘understand’ its
full meaning, complete with the speaker or writer’s intent and sentiment. NLP drives
computer programs that translate text from one language to another, respond to spoken
commands, and summarize large volumes of text rapidly—even in real time. There’s a good
chance you’ve interacted with NLP in the form of voice-operated GPS systems, digital
assistants, speech-to-text dictation software, customer service chatbots, and other consumer
conveniences. But NLP also plays a growing role in enterprise solutions that help streamline
business operations, increase employee productivity, and simplify mission-critical business
processes.
NLP tasks Human language is filled with ambiguities that make it incredibly difficult to write
software that accurately determines the intended meaning of text or voice data. Homonyms,
homophones, sarcasm, idioms, metaphors, grammar and usage exceptions, variations in
sentence structure— these just a few of the irregularities of human language that take humans
years to learn, but that programmers must teach natural language-driven applications to
recognize and understand accurately from the start, if those applications are going to be
useful. Several NLP tasks break down human text and voice data in ways that help the
computer make sense of what it's ingesting. Some of these tasks include the following:
• Speech recognition, also called speech-to-text, is the task of reliably converting voice data
into text data. Speech recognition is required for any application that follows voice
commands or answers spoken questions. What makes speech recognition especially
challenging is the way people talk—quickly, slurring words together, with varying emphasis
and intonation, in different accents, and often using incorrect grammar.
• Part of speech tagging, also called grammatical tagging, is the process of determining the
part of speech of a particular word or piece of text based on its use and context. Part of
speech identifies ‘make’ as a verb in ‘I can make a paper plane,’ and as a noun in ‘What make
of car do you own?’
• Word sense disambiguation is the selection of the meaning of a word with multiple
meanings through a process of semantic analysis that determine the word that makes the most
sense in the given context. For example, word sense disambiguation helps distinguish the
meaning of the verb 'make' in ‘make the grade’ (achieve) vs. ‘make a bet’ (place).
• Named entity recognition, or NEM, identifies words or phrases as useful entities. NEM
identifies ‘Kentucky’ as a location or ‘Fred’ as a man's name.
• Co-reference resolution is the task of identifying if and when two words refer to the same
entity. The most common example is determining the person or object to which a certain
