Speech recognition, Natural language understanding, Natural language generation,Chatbots, Machine Translation.

1. NATURAL LANGUAGE
PROCESSING
UNIT 2
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2. SYLLABUS
 Speech recognition
 Natural language understanding
 Natural language generation
 Chatbots
 Machine Translation
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3. Speech recognition
 Speech recognition is an interdisciplinary
subfield of computer science and
computational linguistics that develops
methodologies and technologies that enable
the recognition and translation of spoken
language into text by computers.
 It is also known as automatic speech
recognition (ASR), computer speech
recognition or speech to text (STT).
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4. Cont….
 Some speech recognition systems require
"training" (also called "enrollment") where an
individual speaker reads text or
isolated vocabulary into the system.
 The system analyzes the person's specific voice
and uses it to fine-tune the recognition of that
person's speech, resulting in increased accuracy.
 Systems that do not use training are called
"speaker independent”systems.
 Systems that use training are called "speaker
dependent".
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5. Natural language understanding
 Natural-language understanding (NLU)
or natural-language interpretation (NLI) is a
subtopic of natural-language
processing in artificial intelligence that deals with
machine reading comprehension.
 Natural-language understanding is considered
an AI-hard problem.
 There is considerable commercial interest in the
field because of its application to automated
reasoning, machine translation, question
answering, news-gathering, text
categorization, voice-activation, archiving, and
large-scale content analysis.
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6. Cont….
 The program STUDENT, written in 1964
by Daniel Bobrow for his PhD dissertation
at MIT is one of the earliest known attempts at
natural-language understanding by a
computer.
 Eight years after John McCarthy coined the
term artificial intelligence, Bobrow's
dissertation (titled Natural Language Input for
a Computer Problem Solving System) showed
how a computer could understand simple
natural language input to solve algebra word
problems.
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7. Natural language generation
 Natural-language generation (NLG) is a
software process that transforms structured
data into natural language.
 It can be used to produce long form content
for organizations to automate custom reports,
as well as produce custom content for a web
or mobile application.
 It can also be used to generate short blurbs of
text in interactive conversations (a chatbot)
which might even be read out by a text-to-
speech system.
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8. Cont….
 Automated NLG can be compared to the process humans
use when they turn ideas into writing or speech.
 Psycholinguists prefer the term language production for this
process, which can also be described in mathematical terms,
or modeled in a computer for psychological research.
 NLG systems can also be compared to translators of artificial
computer languages, such as decompilers or transpilers,
which also produce human-readable code generated from
an intermediate representation.
 Human languages tend to be considerably more complex and
allow for much more ambiguity and variety of expression than
programming languages, which makes NLG more
challenging.
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9. Cont….
 NLG has existed since ELIZA was developed
in the mid 1960s, but commercial NLG
technology has only recently become widely
available.
 NLG techniques range from simple template-
based systems like a mail merge that
generates form letters, to systems that have a
complex understanding of human grammar.
 NLG can also be accomplished by training a
statistical model using machine learning,
typically on a large corpus of human-written
texts.
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10. Chatbots
 A chatbot is a software application used to
conduct an on-line chat conversation via text or
text-to-speech, in lieu of providing direct contact
with a live human agent.
 Designed to convincingly simulate the way a
human would behave as a conversational partner,
chatbot systems typically require continuous
tuning and testing, and many in production remain
unable to adequately converse or pass the
industry standard Turing test.
 The term "ChatterBot" was originally coined
by Michael Mauldin (creator of the first Verbot) in
1994 to describe these conversational programs.
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11. Cont….
 Chatbots are used in dialog systems for various purposes
including customer service, request routing, or for information
gathering.
 While some chatbot applications use extensive word-
classification processes, natural language processors, and
sophisticated AI, others simply scan for general keywords and
generate responses using common phrases obtained from an
associated library or database.
 Most chatbots are accessed on-line via website popups or
through virtual assistants.
 They can be classified into usage categories that
include: commerce (e-commerce via
chat), education, entertainment, finance, health, news,
and productivity.
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12. Machine Translation
 To modern machine translation systems, the
“company” a word keeps is its “context,” or the
words surrounding it.
 For example, the English word “grape” occurs in
contexts such as “grape juice” and “grape vine”.
 Machine translation, sometimes referred to by the
abbreviation MT (not to be confused with
computer-aided translation, machine-aided human
translation or interactive translation), is a sub-field
of computational linguistics that investigates the
use of software to translate text or speech from
one language to another.
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13. Cont…
 On a basic level, MT performs mechanical
substitution of words in one language for
words in another, but that alone rarely
produces a good translation because
recognition of whole phrases and their closest
counterparts in the target language is needed.
 Not all words in one language have equivalent
words in another language, and many words
have more than one meaning.
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14. Cont…
 Current machine translation software often
allows for customization by domain
or profession (such as weather reports),
improving output by limiting the scope of
allowable substitutions.
 This technique is particularly effective in
domains where formal or formulaic language is
used.
 It follows that machine translation of
government and legal documents more readily
produces usable output than conversation or
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15. Thank you 
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