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Artifitial intelligence (ai) all in one

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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

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

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Artifitial intelligence (ai) all in one

  1. 1.  Prepared By:  Engr: Basharat Jehan Qualification: MS Software Engineering Lecturer Agriculture University Peshawar Amir Muhammad Khan Campus Mardan KP, Pakistan  Email id: Basharatjehan1987@gmail.com
  2. 2. Week 1  Introduction to A.I.  Scope  Natural intelligence vs. artificial intelligence  AI computing vs. traditional computing
  3. 3. Father of Artificial Intelligence
  4. 4. 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.
  5. 5. 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.
  6. 6.  John McCarthy, who coined the term in 1955, defines it as "the science and engineering of making intelligent machines".
  7. 7. Main area’s of Research in AI  reasoning,  knowledge,  planning,  learning,  natural language processing (communication),  perception and the ability to move and manipulate objects
  8. 8. Scope of AI  Automated Reasoning  Data Mining  Intelligent Agents  Robotics  Machine Learning  Natural Language Processing  Pattern Recognition  Semantic Web
  9. 9. 12 Artificial Neural Network Artificial neural networks A computer representation of knowledge that attempts to mimic the neural networks of the human body.
  10. 10. WEEK 2 Application areas of AI  Expert systems  Natural Language Processing (NLP)  Computer vision  Speech recognition and generation  Robotics  Neural network  Virtual reality
  11. 11. 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
  12. 12. 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.
  13. 13. 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.
  14. 14.  Artificial neural networks are used as clinical decision support systems for medical diagnosis, such as in Concept Processing technology in EMR software.
  15. 15.  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
  16. 16. 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.
  17. 17. 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.
  18. 18. 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.
  19. 19. 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
  20. 20. 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.
  21. 21.  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.
  22. 22. 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.
  23. 23. Natural Language Processing (NLP)
  24. 24.  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)
  25. 25. Major tasks in NLP  Conference resolution  Machine translation  Named entity recognition  Natural language  understanding  Speech recognition  Topic segmentation  Information retrieval  Speech processing
  26. 26.  Automatic summarization  Discourse analysis  Morphological segmentation  Natural language generation  Optical character recognition  Parsing  Relationship extraction  Sentiment analysis
  27. 27. 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
  28. 28. 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
  29. 29. 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.
  30. 30. 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.
  31. 31. 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.
  32. 32. Overview of relations between computer vision and other fields
  33. 33. 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.
  34. 34. 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.
  35. 35.  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
  36. 36. 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
  37. 37. 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.
  38. 38. 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.
  39. 39. • 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
  40. 40. 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
  41. 41.  There is also a new high field of view VR headset system in development designed specifically for gaming called the Oculus Rift
  42. 42. 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
  43. 43. WEEK3  Expert system  Evolution of expert system  Structure of expert system  Types of expert system  Main application areas of expert system
  44. 44. Expert system  In artificial intelligence, an expert system is a computer system that emulates the decision- making ability of a human expert.
  45. 45. 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".
  46. 46.  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).
  47. 47. Structure of Expert System
  48. 48. Knowledge Acquisition
  49. 49. Knowledge base  Knowledge base – collection of information obtained from books, magazines, knowledgeable persons, etc.
  50. 50. Inference Engine
  51. 51. User Interface
  52. 52. Types of Expert Systems  Expert systems are divided in two types based on inference engine.  Forward Chaining inference Engine  Backward Chaining Inference Engine
  53. 53. 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.
  54. 54. 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
  55. 55. 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).
  56. 56. 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
  57. 57. Application area’s of ES
  58. 58. Week 4  Features of expert system  Overview of expert system’s programming tools  Benefits and limitations of Experts systems
  59. 59. 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.
  60. 60.  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."
  61. 61. 79 Advantages of Expert Systems  Increased availability  Reduced cost  Reduced danger  Performance  Multiple expertise  Increased reliability
  62. 62. 80 Advantages Continued  Explanation  Fast response  Steady, unemotional, and complete responses at all times  Intelligent tutor  Intelligent database
  63. 63. 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
  64. 64. 2. MACSYMA • Developed at MIT since 1968 onwards • Domain: high-performance symbolic math (algebra, calculus, differential equations,...) • Task: carry out complex mathematical derivations
  65. 65. 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
  66. 66. 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
  67. 67. 5. MYCIN • Stanford U in mid 70's • Domain: Medical diagnosis for bacterial and meningitis infections • Task: interview physician, make diagnosis and therapy recommendations
  68. 68. 6. Prospector • Developed at SRI international in late 1970's • Domain: exploratory geology • Task: evaluate geological sites .
  69. 69. 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 •
  70. 70. 8. XCON • Originally called R1, developed at Carnegie Mellon and DEC in late 70's • Domain: configure computer hardware
  71. 71. 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!)
  72. 72. Week 5  Robotics:  Reasons to use a robot  Main application areas  Laws of robotics
  73. 73. 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.
  74. 74. 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.
  75. 75.  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.
  76. 76.  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.
  77. 77. 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.
  78. 78.  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.
  79. 79.  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.
  80. 80. 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,
  81. 81.  Final Term Course
  82. 82. Week 6  Types of robots  Components of a typical robot  Characteristics of robotics  Robot sensors  Robots programming tools
  83. 83. Types of Robotes
  84. 84. 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.
  85. 85.  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.
  86. 86.  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.
  87. 87.  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.
  88. 88.  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.
  89. 89. Characteristics of Robots
  90. 90. 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.
  91. 91. Robots Sensors
  92. 92.  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
  93. 93. 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.
  94. 94. Forms of Natural Language  The input/output of a NLP system can be:  written text  speech
  95. 95. 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.
  96. 96. 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 … …
  97. 97. 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
  98. 98. 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
  99. 99. 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. 
  100. 100.  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.
  101. 101. NLP - Prof. Carolina Ruiz Syntactic Analysis - Parsing sentence noun_phrase verb_phrase proper_noun verb noun_phrase determiner noun “Mary” “ate” “the” “apple”
  102. 102.  3) Semantic Analysis: The structures created by the syntactic analyzer are assigned meanings.
  103. 103. 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?
  104. 104. 5) Phonology  Phonology is a branch of linguistics concerned with the systematic organization of sounds in languages.
  105. 105. 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.
  106. 106. 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
  107. 107. NLG Stages
  108. 108. Programming tools for NLP  See link  http://www.phontron.com/nlptools.php
  109. 109. Week-8  Problems in Natural Languages  Ambiguity:  Lexical  Syntactic  Semantic  Anaphoric  Pragmatics  Imprecision  Inaccuracy  Incompleteness  Solution of the NL problems
  110. 110. Ambiguity  Ambiguity can be referred as the ability of having more than one meaning or being understood in more than one way.
  111. 111. 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.
  112. 112.  2) Syntactic Ambiguity: The structural ambiguities were syntactic ambiguities.  Structural ambiguity is of two kinds: Scope Ambiguity and Attachment Ambiguity.
  113. 113. 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.
  114. 114.  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.
  115. 115.  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.
  116. 116.  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.
  117. 117. 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 Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 144 6 Septemb er 2015
  118. 118. Machine Translation Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 145 6 Septemb er 2015 http://iselab.cvc.uab.es/tutorials_ise/PPTs/survey/
  119. 119. 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) Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 146 6 Septemb er 2015
  120. 120. Why Machine Translation or Goals of MT ???  Cheap, universal access to world’s online information regardless of original language. Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 147 6 Septemb er 2015
  121. 121. 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) Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 148 6 Septemb er 2015
  122. 122. 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) Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 149 6 Septemb er 2015
  123. 123. 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) Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 150 6 Septemb er 2015
  124. 124. 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. Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 151 6 Septemb er 2015
  125. 125. 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.
  126. 126. 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.
  127. 127. 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.
  128. 128. Approaches of Machine Translation  Rule-based MT  Example-based MT  Statistical Based MT  Hybrid MT Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 155 6 Septemb er 2015
  129. 129. 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) Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 156 6 Septemb er 2015
  130. 130. 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.
  131. 131.  A girl eats an apple. => Ein Mädchen isst einen Apfel.
  132. 132. 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) Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 159 6 Septemb er 2015
  133. 133. 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
  134. 134. 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
  135. 135. 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)
  136. 136. 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.
  137. 137. 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
  138. 138. 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
  139. 139. 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
  140. 140. Corpus  Corpus:  corpus, plural corpora A collection of linguistic data, either compiled as written texts or as a transcription of recorded speech. Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 167 6 Septemb er 2015
  141. 141. 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. Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 168 6 Septemb er 2015
  142. 142. 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. Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 169 6 Septemb er 2015
  143. 143. LinguaSys Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 170 6 Septemb er 2015
  144. 144. Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 171 6 Septemb er 2015
  145. 145. 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] Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 172 6 Septemb er 2015
  146. 146. 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. Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 173 6 Septemb er 2015
  147. 147. Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 174 6 Septemb er 2015
  148. 148. Recent Research  Presently a large amount of research is done into example-based machine translation and statistical machine translation Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 175 6 Septemb er 2015
  149. 149. 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] Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 176 6 Septemb er 2015
  150. 150. 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] Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 177 6 Septemb er 2015
  151. 151. Types of Machine Translation  Monolingual  Bilingual  Multilingual Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 178 6 Septemb er 2015
  152. 152. 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) Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 179 6 Septemb er 2015
  153. 153. 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) Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 180 6 Septemb er 2015
  154. 154. 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) Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 181 6 Septemb er 2015
  155. 155. Translation Unit 182 6 Septemb er 2015  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
  156. 156. 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. Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 183 6 Septemb er 2015
  157. 157. 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] Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 184 6 Septemb er 2015
  158. 158. 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 Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 185 6 Septemb er 2015
  159. 159. 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. Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 186 6 Septemb er 2015
  160. 160. Sentence-by-sentence Translation (Cont..)  What does sentence translation ignore?  Discourse properties/structure  Inter-sentence co-reference. Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 187 6 Septemb er 2015
  161. 161. 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: QACC, UOP
  162. 162. 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
  163. 163. 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.
  164. 164. 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
  165. 165. 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
  166. 166.  Referring Expression: any expressions used to refer to somebody or someone with the particular picture in mind (Heasley 1983)
  167. 167. Types of References We can summaries reference with a diagram to make it easier to grasp: 194
  168. 168. 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
  169. 169.  "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.
  170. 170. 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
  171. 171. 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
  172. 172. 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
  173. 173. 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)
  174. 174. 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
  175. 175. 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
  176. 176. 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
  177. 177. 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
  178. 178. 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
  179. 179. Agenda  Natural Language Understanding (NLU)  Ellipsis Definition  Examples of Ellipsis  Origin of the Word Ellipsis Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 206 6 Septemb er 2015
  180. 180. 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. Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 207 6 Septemb er 2015
  181. 181. 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. Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 208 6 Septemb er 2015 Chocolates Been to Karachi
  182. 182. Examples of Ellipsis Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 209 6 Septemb er 2015 (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]
  183. 183. Examples of Ellipsis -Ǿ ‫ما‬‫کله‬‫ې‬ ‫پس‬‫ر‬‫و‬‫به‬‫ډوډۍ‬‫ه‬‫ړ‬‫يو‬‫ينې‬‫ر‬‫نو‬‫به‬ ‫کله‬ ] Mirza Jahanzeb Yar, ”Gulmeena", Page-45] ‫ما‬‫کله‬‫ې‬ ‫پس‬‫ر‬‫و‬‫به‬‫ډوډۍ‬‫ه‬‫ړ‬‫يو‬‫ينې‬‫ر‬‫نو‬‫به‬ ‫کله‬‫ه‬‫ړ‬‫يو‬- . Natural Language Processing (NLP) by Rahman Ali, Lect: QACC, UOP 210 6 Septemb er 2015 Denotes the missing verb phrased [‫یوړه‬].
  184. 184. 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.
  185. 185.  There are five methods of learning . They are,  1. Rote learning  2. Direct instruction (by being told)  3. Analogy  4. Induction  5. Deduction
  186. 186. 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.
  187. 187. 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.
  188. 188. 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.
  189. 189. 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.
  190. 190. 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

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