Artificial intelligence (AI) all in one presentation consists of almost all concepts of Artificial intelligence. i.e.
Artificial intelligence, Robotics, ANN, NLP, NLU, History of AI, History, Pakistan, basharat jehan, agriculture university peshawar, Forward and Backward Chaining, Grammers in AI, Morphology, Examples of Expert system, Laws of Robotics,Expert system languages, Syntactic Analysis in NLP, Lecture Notes
5. INTELLIGENCE
Intelligence is the computational part of the
ability to achieve goals in the world. Varying
kinds and degrees of intelligence occur in
people, many animals and some machines.
6. Artificial intelligence (AI)
Artificial intelligence (AI) is the intelligence
exhibited by machines or software.
Major AI researchers and textbooks define this
field as "the study and design of intelligent
agents", where an intelligent agent is a system
that perceives its environment and takes
actions that maximize its chances of success.
7. John McCarthy, who coined the term in 1955,
defines it as "the science and engineering of
making intelligent machines".
8. Main area’s of Research in
AI
reasoning,
knowledge,
planning,
learning,
natural language processing
(communication),
perception and the ability to move and
manipulate objects
9. Scope of AI
Automated Reasoning
Data Mining
Intelligent Agents
Robotics
Machine Learning
Natural Language Processing
Pattern Recognition
Semantic Web
10.
11.
12. 12
Artificial Neural Network
Artificial neural networks
A computer representation of knowledge that
attempts to mimic the neural networks of the
human body.
13.
14. WEEK 2
Application areas of AI
Expert systems
Natural Language Processing (NLP)
Computer vision
Speech recognition and generation
Robotics
Neural network
Virtual reality
15.
16. Intelligent Systems in Your
Everyday Life Post Office
automatic address recognition and sorting of mail
Banks
automatic check readers, signature verification systems
automated loan application classification
Customer Service
automatic voice recognition
The Web
Identifying your age, gender, location, from your Web surfing
Automated fraud detection
Digital Cameras
Automated face detection and focusing
Computer Games
Intelligent characters/agents
17. FINANCE
Banks use artificial intelligence systems to:
organize operations
invest in stocks
and manage properties.
In August 2001, robots beat humans in a
simulated financial trading competition.
Financial institutions have long used artificial
neural network systems to detect charges or
claims outside of the norm, flagging these for
human investigation.
18. HOSPITALS AND MEDICINE
A medical clinic can use artificial intelligence
systems to organize
bed schedules
make a staff rotation
provide medical information and other
important tasks.
19. Artificial neural networks are used as clinical
decision support systems for medical
diagnosis, such as in Concept
Processing technology in EMR software.
20. Computer-aided interpretation of medical
images
Such systems help scan digital
images, e.g. from computed tomography,
for typical appearances and to highlight
conspicuous sections, such as possible
diseases. A typical application is the
detection of a tumor.
Heart sound analysis
21. HEAVY INDUSTRY
Robots have proven effective in jobs that are
very repetitive which may lead to mistakes or
accidents due to a lapse in concentration and
other jobs which humans may find degrading.
Japan is the leader in using and producing
robots in the world.
In 1999, 1,700,000 robots were in use
worldwide.
22. TRANSPORTATION
Fuzzy logic controllers have been developed
for automatic gearboxes in automobiles.
For example, the 2006 Audi TT, VW Toureg
VW Caravell feature the DSP transmission
which utilizes Fuzzy Logic. A number of Škoda
variants also currently include a Fuzzy Logic
based controller.
23. TELECOMMUNICATIONS
MAINTENANCE
Many telecommunications companies make
use of heuristic search in the management of
their workforces
for example BT Group has deployed heuristic
search in a scheduling application that
provides the work schedules of 20,000
engineers.
24. Toys and games
The 1990s saw some of the first attempts to mass-
produce domestically aimed types of basic Artificial
Intelligence for education, or leisure.
Digital Revolution
Tamagotchis and Giga Pets, iPod Touch,
The Internet
The first widely released robot, Furby
Aibo, a robotic dog with intelligent features
and autonomy.
AI has also been applied to video games, for
example video game bots, which are designed to
stand in as opponents where humans aren't available
or desired
25. AVIATION
The Air Operations Division (AOD) uses AI for
the rule based expert systems.
The AOD has use for artificial intelligence for
surrogate operators for combat and training
simulators
1. Mission management aids
2. Support systems for tactical decision making
3. Post processing of the simulator data into
symbolic summaries.
26. Airplane simulators are using artificial
intelligence in order to process the data taken
from simulated flights.
The computers are able to come up with the
best success scenarios in these situations.
The computers can also create strategies
based on the placement, size, speed and
strength of the forces and counter forces.
Pilots may be given assistance in the air
during combat by computers.
27. SPEECH RECOGNITION
In the 1990s, computer speech recognition
reached a practical level for limited purposes.
United Airlines has replaced its keyboard tree
for flight information by a system using speech
recognition of flight numbers and city names.
29. is a field of computer science, artificial
intelligence, and linguistics concerned with the
interactions between computers and human
(natural) languages.
As such, NLP is related to the area of human–
computer interaction.
Many challenges in NLP involve natural language
understanding, that is, enabling computers to
derive meaning from human or natural language
input, and others involve natural language
generation.
Natural language
processing (NLP)
30. Major tasks in NLP
Conference resolution
Machine translation
Named entity recognition
Natural language
understanding
Speech recognition
Topic segmentation
Information retrieval
Speech processing
33. History
The first expert systems were created in the
1970s and then proliferated in the
1980s. Expert systems were among the first
truly successful forms of AI software
34. Expert systems
In artificial intelligence, an expert system is a
computer system that emulates the decision-
making ability of a human expert
Expert systems are designed to solve
complex problems by reasoning about
knowledge, represented primarily as if–then
rules rather than through
conventional procedural code
35.
36. Computer vision
Computer vision is a field that includes
methods for acquiring, processing, analyzing,
and understanding images and, high-
dimensional data from the real world in order
to produce numerical or symbolic
information, in the forms of decisions.
37.
38. Applications for computer vision
Applications range from tasks such as
industrial machine vision systems
Computer vision covers the core technology of
automated image analysis which is used in
many fields.
39. APPLICATIONS OF COMPUTER VISION
INCLUDE
Controlling processes: an industrial robot
Navigation: by an autonomous vehicle or mobile
robot;
Detecting events: for visual surveillance or people
counting
Organizing information: for indexing databases of
images and image sequences;
Modeling objects or environments: medical image
analysis or topographical modeling;
Interaction: as the input to a device for computer-
human interaction
Automatic inspection: in manufacturing applications.
42. Speech recognition and generation
Speech Technology
The speech capabilities that can be added to an application
are text-to-speech synthesis (TTS) and speech recognition
(SR).
Text-To-Speech Synthesis (TTS)
This involves turning a string into spoken language that is
played through the computer speakers. The complexities of
turning words into phonemes, adding appropriate emphasis
and translating the result into digital audio are beyond the
scope of this paper and are catered for by a TTS engine
installed on your machine.
The end result is that the computer talks to the user to save
the user having to read some text on the screen.
43. SPEECH RECOGNITION (SR)
computer takes the user's speech and
interprets what has been said. This allows the
user to control the computer by voice, rather
than having to use the mouse and keyboard,
or alternatively just dictating the contents of a
document.
44. The complex nature of translating the raw
audio into phonemes involves a lot of signal
processing.
These details are taken care of by an SR
engine that will be installed on machine.
SR engines are called recognisers and these
days typically implement continuous speech
recognition
45.
46. Robotics
Robotics is the branch of mechanical
engineering, electrical engineering and
computer science that deals with the design,
construction, operation, and application of
robots, as well as computer systems for their
control, sensory feedback, and information
processing. The word robotics was derived
from the word robot, which was introduced to
the public by Czech writer Karel Čapek in his
play R.U.R. (Rossum's Universal Robots),
which was published in 1920
47. Aspects of robotics
Robots all have some kind of mechanical
construction, a frame, form or shape designed
to achieve a particular task.
Robots have electrical components which
power and control the machinery.
All robots contain some level of computer
programming code.
48.
49. ARTIFICIAL NEURAL
NETWORKS
In machine learning, artificial neural
networks (ANNs) are a family of statistical
learning algorithms inspired by biological
neural networks and are used to estimate
or approximate functions that can depend on a
large number of inputs and are generally
unknown.
50.
51.
52. • Virtual reality, is a computer-
simulated environment that can simulate
physical presence in places in the real
world or imagined worlds.
• Virtual reality can recreate sensory
experiences, which include virtual
taste, sight, smell, sound, touch, etc.
• It is also known as immersive multimedia
53. GAMES
The use of graphics, sound and input
technology in video games can be
incorporated into VR
the Virtual Boy developed by Nintendo, the
iGlasses developed by Virtual I-O, the
Cybermaxx developed by Victormaxx and the
VFX-1 developed by Forte Technologies
54. There is also a new high field of view VR
headset system in development designed
specifically for gaming called the Oculus Rift
55.
56. TRAINING
The usage of VR in a training perspective is to
allow professionals to conduct training in a
virtual environment where they can improve
upon their skills without the consequence of
failing the operation.
VR plays an important role in combat training
for the military
57.
58. WEEK3
Expert system
Evolution of expert system
Structure of expert system
Types of expert system
Main application areas of expert system
59. Expert system
In artificial intelligence, an expert system is a
computer system that emulates the decision-
making ability of a human expert.
60. Evaluation of Expert System
Expert systems were introduced by the
Stanford Heuristic Programming Project led by
Feigenbaum, who is sometimes referred to as
the "father of expert systems".
61. The Stanford researchers tried to identify
domains where expertise was highly valued
and complex, such as diagnosing infectious
diseases (Mycin) and identifying unknown
organic molecules (Dendral).
67. Types of Expert Systems
Expert systems are divided in two types based
on inference engine.
Forward Chaining inference Engine
Backward Chaining Inference Engine
68. Forward Chaining
In artificial intelligence (AI) systems, forward
chaining refers to a scenario where the AI has
been provided with a specific problem must
"work forwards" to figure out how to solve the
set problem. To do this, the AI would look back
through the rule-based system to find the "if"
rules and determine which rules to use.
69. CPSC 433 Artificial Intelligence
Forward Chaining Example
If [X croaks and eats flies]
Then [X is a frog]
[Fritz croaks and eats flies]
[Fritz is a frog]
If [X is a frog]
Then [X is colored green]
[Fritz is colored green] [Fritz is colored Y] ?
Knowledge Base
If [X croaks and eats flies]
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[Fritz croaks and eats flies]
[Fritz is a frog]
[Fritz is colored green]
Goal
[Fritz is colored Y]?Y = green
70. Backward chaining
Backward chaining (or backward
reasoning) is an inference method that can be
described (in lay terms) as working backward
from the goal(s).
71. CPSC 433 Artificial Intelligence
Backward Chaining Example
Knowledge Base
If [X croaks and eats flies]
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
Then [X is colored green]
If [X is a canary]
Then [X is colored yellow]
[Fritz croaks and eats flies]
Goals
[Fritz is colored Y]?
[X is a frog]
[X is a canary]
[X croaks and eats flies]
[Fritz is colored Y]
If [X is a frog]
Then [X is colored green]
[X is a frog]
If [X is a canary]
Then [X is colored yellow]
[X is a canary]
If [X croaks and eats flies]
Then [X is a frog]
[X croaks and eats flies] [Fritz croaks and eats flies]
X = Fritz, Y = green
74. Week 4
Features of expert system
Overview of expert system’s programming
tools
Benefits and limitations of Experts systems
75. Features of expert system
• Goal driven reasoning or backward chaining - an inference
technique which uses IF THEN rules to repetitively break a
goal into smaller sub-goals which are easier to prove;
• Coping with uncertainty - the ability of the system to reason
with rules and data which are not precisely known;
• Data driven reasoning or forward chaining - an inference
technique which uses IF THEN rules to deduce a problem
solution from initial data;
• Data representation - the way in which the problem specific
data in the system is stored and accessed;
• User interface - that portion of the code which creates an
easy to use system;
• Explanations - the ability of the system to explain the
reasoning process that it used to reach a recommendation.
76.
77. Jess is a rule engine for the Java platform that
was developed by Ernest Friedman-Hill of
Sandia National Labs.
CLIPS is a public domain software tool for
building expert systems. The name is an
acronym for "C Language Integrated
Production System."
81. 1. DENDRAL
• First expert system
• Project began at Stanford in mid 1960's, and is still being used.
• Domain: Organic chemistry - mass spectrometry
• Task: identify molecular structure of unknown compounds
from mass spectra data
82. 2. MACSYMA
• Developed at MIT since 1968 onwards
• Domain: high-performance symbolic math (algebra, calculus,
differential equations,...)
• Task: carry out complex mathematical derivations
83. 3. Hearsay I and II
• Developed at Carnegie-Mellon in late 1960's
• Domain: speech understanding for simple database query
• Task: Using specific vocabulary and grammar criteria, generate
correct speech recognition
84. 4. INTERNIST/CADUCEUS
• Developed at U of Pittsburgh in early 1970's thru mid 80’s
• Domain: diagnostic aid for all of internal medicine
• Task: medical diagnosis given interactive input
85. 5. MYCIN
• Stanford U in mid 70's
• Domain: Medical diagnosis for bacterial and meningitis infections
• Task: interview physician, make diagnosis and therapy recommendations
86. 6. Prospector
• Developed at SRI international in late 1970's
• Domain: exploratory geology
• Task: evaluate geological sites
.
87. 7. PUFF
• Developed at Stanford in 1979
• Domain: Diagnosis of obstructive airway diseases using MYCIN's
inference engine and a new knowledge base
• Task: Take data from instruments and dialog, and diagnose type and
severity of disease
•
88. 8. XCON
• Originally called R1, developed at Carnegie Mellon and DEC in late 70's
• Domain: configure computer hardware
89. Some other famous systems
• DELTA/CATS:
- diagnose and repair diesel locomotives
- developed in LISP, but ported to FORTRAN (a common phenomenon)
• DRILLING ADVISOR:
- diagnose oil drilling problems
- rule-based, exhaustive backward chaining with uncertainty, frames
• GENESIS:
- designs molecular genetics experiments and procedures
- was used by over 500 research scientists
• GATES:
- airline gate assignment and tracking system
- used by TWA at JFK airport
- implemented in Prolog on microcomputers
- access database for 100 daily flights, and creates gate assignment in 30 seconds
(experts took between 10 and 15 hours, with 1 hour per modification)
( possible extension: lost luggage!)
90. Week 5
Robotics:
Reasons to use a robot
Main application areas
Laws of robotics
91. Robotics
Robotics is the branch of mechanical
engineering, electrical engineering and
computer science that deals with the design,
construction, operation, and application of
robots, as well as computer systems for their
control, sensory feedback, and information
processing.
92. Reasons to use a robot
Robots are powerful machines that give us
access to places that are otherwise
inaccessible to the human population. They
protect us from danger by performing tasks
that are harmful to our health.
Some of the first robots were used in the
1940s to handle radioactive materials.
93. Since then robots have become permanent
members of the industrial workforce, including
parts handling, welding, and painting.
Robots simulate many human functions. They
can move, sense their surroundings, and
respond to changes in the environment. Many
robots are mechanical arms attached to a
base. Robotic arms use flexible joints to
perform tasks that require very precise
movements.
94. Medical robots are now so advanced that they
are being employed in brain, heart and eye
surgeries, allowing doctors to treat conditions
that were previously only possible through
treatments nearly as dangerous as the
offending condition.
95. Robotics Applications
Outer Space - Manipulative arms that are controlled by a human are used to unload
the docking bay of space shuttles to launch satellites or to construct a space station
The Intelligent Home - Automated systems can now monitor home security,
environmental conditions and energy usage. Door and windows can be opened
automatically and appliances such as lighting and air conditioning can be pre
programmed to activate. This assists occupants irrespective of their state of mobility.
Exploration - Robots can visit environments that are harmful to humans. An
example is monitoring the environment inside a volcano or exploring our deepest
oceans. NASA has used robotic probes for planetary exploration since the early
sixties.
Military Robots - Airborne robot drones are used for surveillance in today's modern
army. In the future automated aircraft and vehicles could be used to carry fuel and
ammunition or clear minefields
Farms - Automated harvesters can cut and gather crops. Robotic dairies are
available allowing operators to feed and milk their cows remotely.
96. The Car Industry - Robotic arms that are able to perform
multiple tasks are used in the car manufacturing process.
They perform tasks such as welding, cutting, lifting, sorting
and bending. Similar applications but on a smaller scale are
now being planned for the food processing industry in
particular the trimming, cutting and processing of various
meats such as fish, lamb, beef.
Hospitals - Under development is a robotic suit that will
enable nurses to lift patients without damaging their backs.
Scientists in Japan have developed a power-assisted suit
which will give nurses the extra muscle they need to lift their
patients - and avoid back injuries.
97. Disaster Areas - Surveillance robots fitted
with advanced sensing and imaging
equipment can operate in hazardous
environments such as urban setting damaged
by earthquakes by scanning walls, floors and
ceilings for structural integrity.
Entertainment - Interactive robots that exhibit
behaviors and learning ability. SONY has one
such robot which moves freely, plays with a
ball and can respond to verbal instructions.
98. Laws of Robotics
A robot may not injure a human being or, through
inaction, allow a human being to come to harm.
A robot must obey orders given it by human
beings except where such orders would conflict
with the First Law.
A robot must protect its own existence as long as
such protection does not conflict with the First or
Second Law.
By Isaac Asimov's was an American author and
professor of biochemistry at Boston University,
103. Main Components of robot
CONTROLLER :
Every robot is connected to a computer, which
keeps the pieces of the arm working together.
This computer is known as the controller. The
controller functions as the "brain" of the robot.
The controller also allows the robot to be
networked to other systems, so that it may
work together with other machines, processes,
or robots.
104. ARM :
Robot arms come in all shapes and sizes. The
arm is the part of the robot that positions the
end-affector and sensors to do their pre-
programmed business.
105. DRIVE :
The drive is the "engine" that drives the links
(the sections between the joints into their
desired position. Without a drive, a robot
would just sit there, which is not often helpful.
Most drives are powered by air, water
pressure, or electricity.
106. END- EFFECTOR :
The end-effector is the "hand" connected to
the robot's arm. It is often different from a
human hand - it could be a tool such as a
gripper, a vacuum pump, tweezers, scalpel,
blowtorch - just about anything that helps it do
its job. Some robots can change end-effectors,
and be reprogrammed for a different set of
tasks.
107. SENSOR :
Most robots of today are nearly deaf and blind.
Sensors can provide some limited feedback to
the robot so it can do its job. Compared to the
senses and abilities of even the simplest living
things, robots have a very long way to go.
109. Robotic sensing
Robotic sensing is a branch of robotics
science intended to give robots sensing
capabilities, so that robots are more human-
like. Robotic sensing mainly gives robots the
ability to see.
117. Week-7
Natural Language Processing(N LP)
Natural languages vs. computer languages
Natural language understanding (NLU)
Natural language generation (NLG)
Domain areas of NLP
Programming tools for NLP
118. BİL711 Natural Language Processing
118
What is Natural Language
Processing (NLP)
The process of computer analysis of input
provided in a human language (natural
language), and conversion of this input into
a useful form of representation.
The field of NLP is primarily concerned with
getting computers to perform useful and
interesting tasks with human languages.
The field of NLP is secondarily concerned with
helping us come to a better understanding of
human language.
119. Forms of Natural Language
The input/output of a NLP system can be:
written text
speech
120. Components of NLP
Natural language generation systems convert
information from computer databases into readable
human language.
Natural language understanding systems convert
human language into representations that are easier
for computer programs to manipulate.
121. Where does it fit in the CS
taxonomy?
Computers
Artificial Intelligence AlgorithmsDatabases Networking
Robotics SearchNatural Language Processing
Information
Retrieval
Machine
Translation
Language
Analysis
Semantics Parsing
…
…
122. Applications of Nat. Lang. Processing
Machine Translation
is a sub-field of computational linguistics that investigates the
use of software to translate text or speech from one natural
language to another.
Database Access
Information Retrieval
Selecting from a set of documents the ones that are relevant to a
query
Text Categorization
Sorting text into fixed topic categories
Extracting data from text
Converting unstructured text into structure data
Spoken language control systems
Spelling and grammar checkers
123. NLP - Prof. Carolina Ruiz
Input/Output data Processing stage Other data used
Frequency spectrogram freq. of diff.
speech recognition sounds
Word sequence grammar of
“He loves Mary” syntactic analysis language
Sentence structure meanings of
semantic analysis words
He loves Mary
Partial Meaning context of
x loves(x,mary) pragmatics utterance
Sentence meaning
loves(john,mary)
Natural Language Understanding
124. Natural language understanding
Phases
1) Morphological Analysis: Individual words are analyzed into their components and
non word tokens, such as punctuation are separated from the words. Consider the
sentence:
Example
The man looked at the horses.
The plural ending –s in horses is dependent on the noun horse to receive meaning
and can therefore not be a word. Horses however, is a word, as it can occur in other
positions in the sentence or stand on its own:
The horses looked at the man.
- What is the man looking at? - Horses.
Words are thus both independent since they can be separated from other words and
move around in sentences, and the smallest units of language since they are the
only units of language for which this is possible.
125.
126.
127. 2) Syntactic Analysis: Linear sequences of
words are transformed into structures that
show how the words relate each other. Some
word sequences may be rejected if they
violate the languages rules for how words may
be combined.
129. 3) Semantic Analysis: The structures created
by the syntactic analyzer are assigned
meanings.
130. NLP - Prof. Carolina Ruiz
4) Pragmatics
Uses context of utterance
Where, by who, to whom, why, when it was said
Intentions: inform, request, promise, criticize, …
Handling Pronouns
“Mary eats apples. She likes them.”
She=“Mary”, them=“apples”.
Handling ambiguity
Pragmatic ambiguity: “you’re late”: What’s the
speaker’s intention: informing or criticizing?
131. 5) Phonology
Phonology is a branch of linguistics
concerned with the systematic organization of
sounds in languages.
132. Natural Language Generation
(NLG)
Natural Language Generation (NLG)
Systems which take information from
some database and figure out how to
present it to a human. Very little
linguistics involved.
133. NLP - Prof. Carolina Ruiz
Natural Language Generation
Talking back!
What to say or text planning
flight(AA,london,boston,$560,2pm),
flight(BA,london,boston,$640,10am),
How to say it
“There are two flights from London to Boston. The first one is
with American Airlines, leaves at 2 pm, and costs $560 …”
Speech synthesis
Simple: Human recordings of basic templates
More complex: string together phonemes in phonetic spelling
of each word
Difficult due to stress, intonation, timing, liaisons between words
136. Week-8
Problems in Natural Languages
Ambiguity:
Lexical
Syntactic
Semantic
Anaphoric
Pragmatics
Imprecision
Inaccuracy
Incompleteness
Solution of the NL problems
137. Ambiguity
Ambiguity can be referred as the ability of
having more than one meaning or being
understood in more than one way.
138. Ambiguities in Natural Language
Processing
1) Lexical Ambiguity: is the ambiguity of a single word. A
word can be ambiguous with respect to its syntactic
class.Eg: book, study.
For eg: The word silver can be used as a noun, an adjective,
or a verb.
She bagged two silver medals.
She made a silver speech.
His worries had silvered his hair.
Lexical ambiguity can be resolved by Lexical category
disambiguation i.e, parts-of-speech tagging. As many words
may belong to more than one lexical category part-of-speech
tagging is the process of assigning a part-of-speech or
lexical category such as a noun, verb, pronoun, preposition,
adverb, adjective etc. to each word in a sentence.
139. 2) Syntactic Ambiguity: The structural
ambiguities were syntactic ambiguities.
Structural ambiguity is of two kinds: Scope
Ambiguity and Attachment Ambiguity.
140. 2.1 Scope ambiguity involves operators and quantifiers.
Consider the example:
Old men and women were taken to safe locations.
The scope of the adjective (i.e., the amount of text it qualifies)
is ambiguous. That is, whether the structure (old men and
women) or ((old men) and women)?
The scope of quantifiers is often not clear and creates
ambiguity.
Every man loves a woman.[7]
The interpretations can be, For every man there is a woman
and also it can be there is one particular woman who is
loved by every man.
141. 2.2) Attachment Ambiguity
A sentence has attachment ambiguity if a constituent fits more than
one position in a parse tree. Attachment ambiguity arises from
uncertainty of attaching a phrase or clause to a part of a sentence.
Consider the example:
The man saw the girl with the telescope.[2]
It is ambiguous whether the man saw a girl carrying a telescope, or
he saw her through his telescope.
The meaning is dependent on whether the preposition ‘with’ is
attached to the girl or the man.
Consider the example:
Buy books for children
Preposition Phrase ‘for children’ can be either adverbial and attach
to the verb buy or adjectival and attach to the object noun books.
142. 3) Semantic Ambiguity: This occurs when the meaning of the words themselves
can be misinterpreted. Even after the syntax and the meanings of the individual words have
been resolved, there are two ways of reading the sentence.
Consider the example,
Seema loves her mother and Sriya does too.
The interpretations can be Sriya loves Seema’s mother or Sriya likes her own mother.
Semantic ambiguities born from the fact that generally a computer is not in a position to
distinguishing what is logical from what is not.
Consider the example:
The car hit the pole while it was moving.
The interpretations can be The car, while moving, hit the pole and The car hit the pole while the
pole was moving. The first interpretation is preferred to the second one because we have a model
of the world that helps us to distinguish what
is logical (or possible) from what is not. To supply to a computer a model of the world is not so
easy.[4]
Consider the example:
We saw his duck
Duck can refer to the person’s bird or to a motion he made.
Semantic ambiguity happens when a sentence contains an ambiguous word or phrase.
143. 4) Anaphoric Ambiguity: Anaphoras are the
entities that have been previously introduced
into the discourse.
Consider the example,
The horse ran up the hill. It was very steep (ڈھلوان
,سے تیزی ).
The anaphoric reference of ‘it’ in the two situations
cause ambiguity.
Steep applies to surface hence ‘it’ can be hill.
Tired applies to animate object hence ‘it’ can be
horse.
144. Agenda
Machine Translation
Why Machine Translation
History of MT
Approaches to MT
MT Application
Recent Research
Strategies of MT
Types of MT
Next Week Plan
The End
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145. Machine Translation
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http://iselab.cvc.uab.es/tutorials_ise/PPTs/survey/
146. Machine Translation
Machine Translation (MT) is the task of
automatically converting one natural language
into another, preserving the meaning of the input
text, and producing fluent text in the output
language.
http://nlp.stanford.edu/projects/mt.shtml, Retrieval date: 28 Nov, 2010)
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147. Why Machine Translation or Goals
of MT ???
Cheap, universal access to world’s online
information regardless of original language.
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148. History of Machine Translation
The history of machine translation started way back in
the 1950s.
The first work of translation was published in 1954 in the
Georgetown experiment involving fully automatic
translation of more than 60 Russian sentences into
English.
The experiment was a great success and the authors
claimed that machine translation would be used in
translations within three or five years.
However, the real progress was very slow.
(http://www.thelanguagetranslation.com/machine-translation.html)
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149. History of Machine Translation
(Cont..)
The ALPAC (Automatic Language Processing
Advisory Committee) report in 1966 further
reduced the investment in Machine translation
because the report evaluated the progress in
computational linguistics in general and
machine translation in particular and was very
skeptical (disbelieving) to research done in
machine translation so far and gave more
emphasis to the need for basic research in
computational linguistics.
(http://www.thelanguagetranslation.com/machine-translation.html)
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150. History of Machine Translation
(Cont..)
However, starting in the late 1970s and beginning 1980s, with
the impact of personal computer revolution, with the increase
in computational power, more interest began to be shown in
statistical models for machine translation.
There was growth in the use of machine translation as a
result of the beginning of less expensive and more powerful
computers.
With the 1990s, the importance of machine translation further
increased (for better or worse) and the use of "translation
engines" on the Internet to allow for translation of websites
and email languages.
(http://www.thelanguagetranslation.com/machine-translation.html)
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151. History of Machine Translation
(Cont..)
Today there are many software programs
several of them online for translating source
language.
Such software includes the SYSTRAN system
which powers both Google translate,
AltaVista's Babelfish, StarDict etc.
These tools produce a rough translation that
gives the summary of the source text.
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152. Translation process
The human translation process may be
described as:
Decoding the meaning of the source text; and
Re-encoding this meaning in the target
language.
153. Bilingual MT
A bilingual dictionary or translation dictionary
is a specialized dictionary used to translate words
or phrases from one language to another.
Bilingual dictionaries can be unidirectional,
meaning that they list the meanings of words of
one language in another, or can be bidirectional,
allowing translation to and from both languages.
Bidirectional bilingual dictionaries usually consist
of two sections, each listing words and phrases of
one language alphabetically along with their
translation.
154. Multilingual MT
A Multilingual MT or translation dictionary is a
specialized dictionary used to translate words or
phrases from one language to several other
languages. Multilingual MT can be unidirectional,
meaning that they list the meanings of words of
one language in another, or can be bidirectional,
allowing translation to and from both languages.
Multilingual MT usually consist of two sections,
each listing words and phrases of one language
alphabetically along with their translation.
155. Approaches of Machine Translation
Rule-based MT
Example-based MT
Statistical Based MT
Hybrid MT
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156. Rule-based Machine Translation
(RBMT
Also known as “Knowledge-based Machine Translation”;
“Classical Approach” of MT).
Machine translation systems that are based on linguistic
information about source and target languages basically
retrieved from (bilingual) dictionaries and grammars
covering the main semantic, morphological, and syntactic
regularities of each language respectively.
This approach of MT make use of morphological, syntactic,
and semantic analysis of both the source and the target
languages involved in a concrete translation task.
(http://en.wikipedia.org/wiki/Rule-based_machine_translation)
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157. Basic principles
A girl eats an apple. Source Language =
English; Demanded Target Language =
German Minimally, to get a German translation
of this English sentence one needs:
A dictionary that will map each English word to
an appropriate German word.
Rules representing regular English sentence
structure.
Rules representing regular German sentence
structure.
158. A girl eats an apple. => Ein Mädchen isst
einen Apfel.
159. Example-based Machine Translation (EBMT)
EBMT approach to machine translation is often characterized
by its use of a bilingual corpus with parallel texts as its main
knowledge base, at run-time.
It is essentially a translation by analogy and can be viewed as
an implementation of case-based reasoning approach of
machine learning.
(EBMT) approach was proposed by Makoto Nagao in
1984.[3][4]
(http://en.wikipedia.org/wiki/Example-based_machine_translation)
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160. 160/23
Example-based MT
Long-established approach to empirical MT
First developed in contrast with rule-based MT
Idea of translation by analogy (Nagao 1984)
Translate by adapting previously seen examples
rather than by linguistic rule
“Existing translations contain more solutions to
more translation problems than any other available
resource.” (P. Isabelle et al., TMI, Kyoto, 1993)
In computational terms, belongs in family of Case-
based reasoning approaches
161. 161/23
EBMT basic idea
database of translation pairs
match input against example database
(like Translation Memory)
identify corresponding translation
fragments (align)
recombine fragment into target text
162. 162/23
He buys a book on international politics
Input
Matches
He buys a notebook.
Kare wa nōto o kau.
I read a book on international politics.
Watashi wa kokusai seiji nitsuite kakareta hon o yomu.
Result
Kare wa o kau.kokusai seiji nitsuite kakareta hon
Example (Sato & Nagao 1990)
163. Statistical machine translation
(SMT)
Statistical machine translation (SMT) is a
machine translation paradigm where
translations are generated on the basis of
statistical models whose parameters are
derived from the analysis of bilingual text
corpora.
164. 164
How to Build an SMT System
Start with a large parallel corpus
Consists of document pairs (document and its translation)
Sentence alignment: in each document pair automatically find
those sentences which are translations of one another
Results in sentence pairs (sentence and its translation)
Word alignment: in each sentence pair automatically annotate
those words which are translations of one another
Results in word-aligned sentence pairs
Automatically estimate a statistical model from the word-
aligned sentence pairs
Results in model parameters
Given new text to translate, apply model to get most probable
translation
165. 165
Sentence alignment
If document De is translation of document Df
how do we find the translation for each
sentence?
The n-th sentence in De is not necessarily the
translation of the n-th sentence in document Df
In addition to 1:1 alignments, there are also 1:0,
0:1, 1:n, and n:1 alignments
In European Parliament proceedings,
approximately 90% of the sentence alignments
are 1:1
Modified from Dorr, Monz
166. 166
Sentence alignment
There are several sentence alignment algorithms:
Align (Gale & Church): Aligns sentences based on their
character length (shorter sentences tend to have shorter
translations then longer sentences). Works well
Char-align: (Church): Aligns based on shared character
sequences. Works fine for similar languages or technical
domains
K-Vec (Fung & Church): Induces a translation lexicon from
the parallel texts based on the distribution of foreign-
English word pairs
Cognates (Melamed): Use positions of cognates (including
punctuation)
Length + Lexicon (Moore): Two passes, high accuracy,
freely available
Modified from Dorr, Monz
167. Corpus
Corpus:
corpus, plural corpora A collection of linguistic data,
either compiled as written texts or as a transcription of
recorded speech.
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168. Hybrid Machine Translation (HMT)
Hybrid machine translation (HMT) leverages
the strengths of statistical and rule-based
translation methodologies.[5]
Several MT companies (Asia Online,
LinguaSys, and Systran) are claiming to have
a hybrid approach using both rules and
statistics.
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169. Machine Translation Applications
LinguaSys (http://www.linguasys.net/)
provides highly customized hybrid machine translation that can go from any language
to any language. [Video: http://www.youtube.com/watch?v=lcSYwNP4CQ4]
Asia Online [http://www.asiaonline.net/translation.aspx]
provides a custom machine translation engine building capability that they claim gives
near-human quality compared to the "gist" based quality of free online engines. Asia
Online also provides tools to edit and create custom machine translation engines with
their Language Studio suite of products.
Hindi to Punjabi Machine Translation System[3],
provides machine translation using a direct approach. It translates Hindi into Punjabi. It
also features writing e-mail in the Hindi language and sending the same in Punjabi to
the recipient.
IdiomaX,
which powers online translation services at idiomax.com
Toggletext
uses a transfer-based system (known as Kataku) to translate between English and Indonesian.
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172. Machine Translation Applications
(Cont..)
Arabic machine translation
in multilingual framework.
Worldlingo
provides machine translation using both statistical based TE's and rule based TE's.
Most recognizable as the MT partner in Microsoft Windows and Microsoft Mac Office.
Power Translator
SDL ETS and Language Weaver
which power FreeTranslation.com (website)
SYSTRAN,
which powers Yahoo! Babel Fish
Promt,
which powers online translation services at Voila.fr and Orange.fr
AppTek,
which released a hybrid MT system in 2009.[4]
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173. Machine Translation Applications
(Cont..)
Anusaaraka
A free open source machine translation from English to Hindi based on Panini
grammar and uses state of the art NLP tools. Can be used online and
downloaded from
Apertium,
a free and open source machine translation platform (WinXLator gives this a
Windows GUI, but it is likely to be in violation of the Apertium GPL license)
Google Translator
A free online translator from Google. [URL: translate.google.com]
Other translation software, most of them running under Microsoft
Windows, includes:
Translation memory tools, such as SDL Trados, Wordfast, Deja Vu, Swordfish,
and
localization tools, such and Alchemy CATALYST and Multilizer.
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175. Recent Research
Presently a large amount of research is done
into example-based machine translation and
statistical machine translation
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176. Advantages of Machine
Translations
Machine translations work at a faster rate than human translations.
Another advantage of machine translation is that it is comparatively cheaper. It is one time
cost -the cost of the tool and its installation.
When time is a crucial factor, machine translation can save the day. You don't have to
spend hours poring over dictionaries to translate the words. Instead, the software can
translate the content quickly and provide a quality output to the user in no time at all.
The next benefit of machine translation is that it is comparatively cheap. Initially, it might
look like a unnecessary investment but in the long run it is a very small cost considering
the return it provides. This is because if you use the expertise of a professional translator,
he will charge you on a per page basis which is going to be extremely costly while this will
be cheap.
Confidentiality is another matter which makes machine translation favorable. Giving
sensitive data to a translator might be risky while with machine translation your information
is protected.
A machine translator usually translates text which is in any language so there is no such
major concern while a professional translator specializes in one particular field.
[http://www.thelanguagetranslation.com/machine-translation.html]
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177. Drawback of MT
Accuracy is not offered by the machine translation on a
consistent basis. You can get the gist of the draft or
documents but machine translation only does word to word
translation without comprehending the information which
might have to be corrected manually later on.
Systematic and formal rules are followed by machine
translation so it cannot concentrate on a context and solve
ambiguity and neither makes use of experience or mental
outlook like a human translator can.
[http://www.thelanguagetranslation.com/machine-translation.html]
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178. Types of Machine Translation
Monolingual
Bilingual
Multilingual
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179. Monolingual Machine Translation
The translation of natural language text of the
source language to the target text in the same
language is called Monolingual Machine
Translation.
Source Text (English)……..Computer…………> Target Text
(English)
Source Text (Urdu)……..Computer…………> Target Text (Urdu)
Source Text (Pashto)……..Computer…………> Target Text (Pashto)
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180. Bilingual Machine Translation
The translation of natural language text written
in one natural language to the target text in the
other language is called bilingual Machine
Translation.
Source Text (English)……..Computer…………> Target Text
(Pashto)
Source Text (Urdu)……..Computer…………> Target Text (Chines)
Source Text (Pashto)……..Computer…………> Target Text (Hindko)
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181. Multilingual Machine Translation
The translation of natural language text written
in one language to the target text in more than
two languages is called Multilingual Machine
Translation.
Source Text (English)……..Computer…………> Target Text (Urdu, Pashto)
Source Text (Urdu)……..Computer…………> Target Text (Hindko,
Japanies, Turkish)
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182. Translation Unit
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In the field of translation, a translation unit is
a segment of a text which the translator treats
as a single cognitive unit for the purposes of
establishing an equivalence.
The translation unit may be a
Single word, or it may be
Sentence
Discourse
183. Translation Unit (Cont..)
When a translator segments a text into translation units,
the larger these units are, better chance there is of
obtaining an idiomatic translation.
This is true not only of human translation, but also in
cases where human translators use computer-assisted
translation, and also when translations are performed by
machine translation systems.
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184. Word-For-Word Translation
Transferring the meaning of each individual
word in a text to another, equivalent word in the
target language.
Sometimes called 'Literal Translation'.
While this is clearly appropriate for dictionaries,
it can produce very complex passages of text.
[Translation Theory, http://www.translatum.gr/etexts/translation-
theory.htm#UnitOfTranslation, Retrieved date: 09-Jan,2011]
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185. Word-For-Word Translation
(Cont..)
Problems/limitations
Words order in source and target language
SOV vs SVO
I ate the meal [English]
Ma dody wakhwara [Pashto]
Sometimes, no matching word in target language
No (1-1) correspondence between the words of
source language and target language
Poly-semantic words
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186. Sentence-by-sentence Translation
A sentence in the source language is taken as a
unit of translation and translated to the
corresponding target language.
Most MT work focuses on sentence translation.
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187. Sentence-by-sentence Translation
(Cont..)
What does sentence translation ignore?
Discourse properties/structure
Inter-sentence co-reference.
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188. 188
William Shakespeare was an English poet and
playwright widely regarded as the greatest writer of
the English language, as well as one of the greatest
in Western literature, and the world's pre-eminent
dramatist.
He wrote about thirty-eight plays and 154 sonnets
(poems), as well as a variety of other poems.
<doc>
</doc>
. . .
<sentence>
<sentence>
<sentence>
Problems in Sentence-based MT
What is the referent of “He”?
Natural Language Processing
(NLP) by Rahman Ali, Lect:
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189. Contents
Referring Expression
Referring expression
Referent
Types of References
Exophora and
Endophora
Types of Endophora
Anaphora and
Cataphora
Types or Categories of Anaphora
Anaphoric/Cataphoric Devices
Anaphora Resolution
Anaphora and Ambiguity
Reading/References
Next Week Plan
189
190. Reference
Reference is a relation between objects in
which one object designates, or acts as a
means by which to connect to or link to,
another object. The first object in this relation
is said to refer to the second object.
191. Referring Expression
A natural language expression used to perform
reference is called a referring expression and the entity
that is referred to is called the referent.
Referring expressions are words or phrases, the semantic
interpretation of which is a discourse entity (also called referent)
Example:
A pretty woman entered the restaurant. She sat at the table next
to mine and only then I recognized her. This was Amy Garcia, my
next door neighbor from 10 years ago. The woman has totally
changed! Amy was at the time shy…
191
192. Referring Expression ( Cont’)
A pretty woman entered the restaurant. She sat
at the table next to mine and only then I
recognized her. This was Amy Garcia, my next
door neighbor from 10 years ago. The woman
has totally changed! Amy was at the time shy…
192
Referent
Referring Expression
193. Referring Expression: any expressions used to refer
to somebody or someone with the particular picture
in mind (Heasley 1983)
194. Types of References
We can summaries reference with a diagram
to make it easier to grasp:
194
195. Exophoric Reference
Exophoric reference, depends on the context outside
the text for its meaning.
In linguistics, Exophora is reference to something
extra-linguistic.
For example
"What is this?",
here "this" is exophoric rather than endophoric, because it refers to
something extra-linguistic, i.e. there is not enough information in
the utterance itself to determine what "this" refers to, but we must
instead observe the non-linguistic context of the utterance (e.g. the
speaker might be holding an unknown object in their hand as they
ask that question.)
195
196. "Did the gardener water those plants?", it is
quite possible that "those" refers back to the
preceding text, to some earlier mention of
those particular plants in the discussion.
197. Endophoric Reference
The pronouns refer to items within the same text; it is
endophoric reference.
Endophora is a linguistic reference to something intra-
linguistic.
For Example:
"I saw Sally yesterday. She was lying on the beach".
Here, "she" is intra-linguistic, and hence endophoric, because it refers to something
(Sally, in this case) already mentioned in the text.
(From Wikipedia, the free encyclopedia)
197
198. Endophora (Another Definition)
Words or phrases like pronouns are endophora
when they point backwards or forwards to
something in the text:
For example:
As [he]1 was late, [Harry] 1 wanted to phone [his] 1
[boss] 2 and tell [her] 2 what had happened.
(From Wikipedia, the free encyclopedia)
198
199. Types of Endophora
1) Cataphora:
The type of endophora in which the referring expression occur
before the referent are termed as cataphora.
OR The type of endophora in which the pronouns link forward to a
referent (nouns) in the text that follows.
For example:
When [she] 1 saw the snake, [Harry] 1 cried.
The elevator opened for [him] 1 on the 14th flour, and [Ali] 1 stepped out
quickly.
2) Anaphora:
The type of endophora in which the referent occurs before the
referring expression are termed as anaphora.
OR The type of endophora in which the pronouns link backward to
a referent (nouns) in the text.
For example:
199
200. Anaphora(Another Definition)
Anaphora is a phenomenon in which certain
textual elements refer to earlier text elements
(called correlates) and share the meaning of the
correlates.
For Example:
1)John helped Mary.
2) He was kind.
200
Correlate/
Referent/
AntecedentReferring Element/
Anaphor/
Anaphoric Device (AD)
201. Anaphoric and Cataphoric Devices
The referring elements (pronouns) in anaphoric text that refer to
their corresponding referent ( nouns) backward are called
anaphoric devices. Also, called anaphor.
For example:
Bell is a powerful player but unfortunately he will not take part in the trophy
due to injury.
The referring elements (pronouns) that refer to their corresponding
referent (nouns) forward in cataphoric text are called cataphoric
devices. Also, called cataphor.
For example:
As her father went abroad, Nighat took control of the organization by herself.
201
Anaphoric Device
Cataphoric Device
202. Types of Anaphora (On the basis of
position of anaphor and its antecedent)
Intra-sentential/Sentence internal anaphora:
The anaphora in which the AD and its antecedent both occurs
in the same sentence is called sentence internal.
Reflexive pronouns
(himself, herself, itself, themselves) are typical examples of intra-sentential
anaphora.
Possessive pronouns
(his, her, hers, its, their, theirs) can often be used as intra-sentential
anaphors too, and often be in the same clause as the anaphor.
For example:
[John] 1 took [his] 1 [hat] 2 off and hung [it] 2 on a peg.
202
203. Types of Anaphora (Cont..)
Inter-sentential/Sentence external anaphora:
The anaphora in which the AD and its antecedent
doesn’t occur in the same sentence is called sentence
external or inter-sentential anaphora.
For example:
[Jehansher] 1 Khan was senior player of Sqash. [He] 1 has won
several trophies.
[John] 1 took his hat off and hung it on a peg. [He] 1 was very
tied therefore went to slept..
203
204. Anaphora and Ambiguity
Many anaphors are ambiguous. Like:
A)
Jane told Marry she was in love (ambiguous)
Jane informed Marry she was in love. (Here Jane is in
love)
B)
Jane told marry she was in danger (ambiguous)
Jane warned Marry she was in danger.
204
205. Anaphora Resolution
Anaphora Resolution == the problem of resolving what a
pronoun, or a noun phrase refers to.
Consider the following Discourse:
1) John helped Mary.
2) He was kind.
After anaphora resolution:
1) John helped Mary.
2) John was kind.
205
206. Agenda
Natural Language Understanding (NLU)
Ellipsis Definition
Examples of Ellipsis
Origin of the Word Ellipsis
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207. Ellipsis
Definition:
The omission of a portion, of a phrase or a
sentence is called Ellipsis (Rav, L., F.).
Example:
He is rich, but his brother is not ᶲ.
Bob ᶲ and Tom ate cheese.
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208. Examples of Ellipsis
George bought a huge box of chocolates but few Ǿ
were left by the end of the day.
I have never been to Karachi but my father has Ǿ ,
and he says it was wonderful.
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Chocolates
Been to Karachi
209. Examples of Ellipsis
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(1)
لژووپکښے څويو اولشوټپيانخلکډير السهلهبريدونو ځانمرګې د
لشو-
څويو اولشوټپيانخلکډيرالسهلهبريدونو ځانمرګېدخلکلژووپکښے
لشو-
Shows that here a noun is missing which is the possible
referring expression for the antecedent [People]
210. Examples of Ellipsis
-Ǿ ماکلهې پسروبهډوډۍهړيوينېرنوبه کله
] Mirza Jahanzeb Yar, ”Gulmeena", Page-45]
ماکلهې پسروبهډوډۍهړيوينېرنوبه کلههړيو-
.
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Denotes the missing verb phrased
[یوړه].
211. LEARNING
Learning is the improvement of performance
with experience over time.
Learning element is the portion of a learning AI
system that decides how to modify the
performance element and implements those
modifications.
212. There are five methods of learning . They are,
1. Rote learning
2. Direct instruction (by being told)
3. Analogy
4. Induction
5. Deduction
213. Rote Learning
Rote learning is the memorization of
information based on repetition. The two
biggest examples of rote learning are the
alphabet and numbers.
Example:- Memorizing multiplication tables,
formulate , etc.
214. Learning by Instruction
This type of learning occur when a person is
instructed about a problem solution or for new
knowledge learning by an instructor.
For example
Learning by instruction occurs when one male
imitates the song of another.
215. Learn by Analogy
Analogical learning is the process of learning a
new concept or solution through the use of
similar known concepts or solutions. We use
this type of learning when solving problems on
an exam where previously learned examples
serve as a guide or when make frequent use
of analogical learning.
216. Inductive Learning
Inductive Learning is the process of making
generalized decisions after observing, or
witnessing, repeated specific instances of
something. For example
This cat is black. That cat is black A third cat is
black. Therefore all cats are black.
This marble from the bag is black. That marble
from the bag is black. A third marble from the
bag is black. Therefore all the marbles in the
bag black.
217. Learning by Deduction
In the process of deduction, you begin with
some statements, called 'premises', that are
assumed to be true, you then determine what
else would have to be true if the premises are
true.
All men are mortal. Joe is a man. Therefore
Joe is mortal. If the first two statements are
true, then the conclusion must be true. 2
Bachelor's are unmarried men. Bill is
unmarried. Therefore, Bill is a bachelor. 3