Ch94 NLP

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Information about Ch94 NLP
Travel-Nature

Published on March 12, 2008

Author: Marcell

Source: authorstream.com

Natural Language Processing:  Natural Language Processing R & N 22 Turing’s Prediction:  Turing’s Prediction 1950 Turing predicted that in about fifty years "an average interrogator will not have more than a 70 percent chance of making the right identification after five minutes of questioning". Was Turing Right?:  Was Turing Right? >What's the best place to eat Chinese food? ALICE: My favorite places are California and Maine. Holland is cool too. >What do you like about Maine? ALICE: The special effects are the best. Another Application - MT:  Another Application - MT Austin Police are trying to find the person responsible for robbing a bank in Downtown Austin. El policía de Austin está intentando encontrar a la persona responsable de robar un banco en Austin céntrica. The police of Austin is trying to find the responsible person to rob a bank in centric Austin. MT - The Limits:  MT - The Limits A Florida teen charged with hiring an undercover policeman to shoot and kill his mother instructed the purported hitman not to damage the family television during the attack, police said on Thursday. Un adolescente de la Florida cargado con emplear a un policía de la cubierta interior para tirar y para matar a su madre mandó a hitman pretendida para no dañar la televisión de la familia durante el ataque, limpia dicho el jueves. An adolescent of Florida loaded with using a police of the inner cover to throw and to kill his mother commanded to hitman tried not to damage the television of the family during the attack, clean said Thursday. MT - The Limits:  MT - The Limits http://www.shtick.org/Translation/translation47.htm Going Both Ways:  Going Both Ways Notice that both of these applications require that we process language in two directions: Understanding Generation But also notice that it is possible to do a somewhat passable job without going through any meaning representation. When Meaning is Critical:  When Meaning is Critical English: Put the kid’s cereal on the bottom shelves. Java:  Java import java.util.ArrayList; public class GroceryStore { private int[][][] shelves; private ArrayList products; public void placeProducts(String productFile) { FileReader r = new FileReader(productFile); GroceryItemFactory factory = new GroceryItemFactory(); while(r.hasNext()) products.add( factory.createItem(r.readNext())); ThreeDLoc startLoc; GroceryItem temp; for(itemNum = 0; itemNum < products.size(); itemNum++) { temp = (GroceryItem)(products.get(itemNum)) startLoc = temp.getPlacement(this); shelves[startLoc.getX()][startLoc.getY()][startLoc.getY()]= tempgetIDNum(); } } } Java, Continued:  Java, Continued public class ChildrensCereal extends GroceryItem { private static final int PREFERRED_X = -1; private static final int PREFERRED_Y = 0; private static final int PREFERRED_Z = 0; public ThreeDLoc getPlacement(GroceryStore store) { ThreeDLoc result = new ThreeDLoc(); result.setX(store.find(this)); result.setY(PREFERRED_Y); result.setZ(PREFERRED_Z); return result; } } It’s All about Mapping:  It’s All about Mapping What Are We Going to Map to?:  What Are We Going to Map to? English: Do you know how much it rains in Austin? The database: English: What is the average rainfall, in Austin, in months with 30 days?:  English: What is the average rainfall, in Austin, in months with 30 days? SQL: SELECT Avg(RainfallByStation.rainfall) AS AvgOfrainfall FROM Stations INNER JOIN (Months INNER JOIN RainfallByStation ON Months.Month = RainfallByStation.month) ON Stations.station = RainfallByStation.station HAVING (((Stations.City)="Austin") AND ((Months.Days)=30)); Designing a Mapping Function for NL Understanding:  Designing a Mapping Function for NL Understanding Morphological Analysis and POS tagging * The womans goed home. Syntactic Analysis (Parsing) * Fishing went boys older Extracting Meaning Colorless green ideas sleep furiously. Sue cooked. The potatoes cooked. * Sue and the potatoes cooked. Putting it All in Context My cat saw a bird out the window. It batted at it. What isn’t said Winnie doesn’t like August. He doesn’t like melted ice cream. Ambiguity – the Core Problem:  Ambiguity – the Core Problem Time flies like an arrow. Fruit flies like a banana. I hit the boy with the blue shirt (a bat). I saw the Grand Canyon (a Boeing 747) flying to New York. I know more beautiful women than Kylie. I only want potatoes or rice and beans. The boys may not come. Is there water in the fridge? Who cares? Have you finished writing your paper? I’ve written the outline. Morphological Analysis and POS Tagging:  Morphological Analysis and POS Tagging Morphological Analysis: played = play + ed = play (V) + PAST saw = see (V) + PAST leaves = Morphological Analysis and POS Tagging:  Morphological Analysis and POS Tagging Morphological Analysis: played = play + ed = play (V) + PAST saw = see (V) + PAST leaves = leaf (N) + PL = leave (N) + PL = leave (V) + 3rdS compute Morphological Analysis and POS Tagging:  Morphological Analysis and POS Tagging Morphological Analysis: played = play + ed = play (V) + PAST saw = see (V) + PAST leaves = leaf (N) + PL = leave (N) + PL = leave (V) + 3rdS compute computer computerize computerization POS Tagging: I hit the bag. Morphological Analysis Using a Finite State Transducer:  Morphological Analysis Using a Finite State Transducer Stochastic POS Tagging:  Stochastic POS Tagging Naïve Bayes Classification: Choose the POS tag that is most likely for the current word given its context. For example: Secretariat expected to race tomorrow. Using Bayes Rule We want to choose the tag tj with maximum likelihood: Parsing - Building a Tree:  Parsing - Building a Tree John hit the ball. S NP VP N V NP John hit DET N the ball (S (NP (N John)) (VP (V hit) (NP (DET the) (N ball)))) Why Cats Paint:  Why Cats Paint Why Paint Cats:  Why Paint Cats The Importance of Parsing Even When We’re Not Doing Full Understanding:  The Importance of Parsing Even When We’re Not Doing Full Understanding Find me all the: Lawyers whose clients committed fraud vs Lawyers who committed fraud vs Clients whose lawyers committed fraud Grammar Rules:  Grammar Rules We can build a parse tree using a grammar with rules such as: S  NP VP NP  N NP  N PP VP  V VP  V NP VP  VP PP The Lexicon is Important:  The Lexicon is Important * The cat with a furry tail purred a collar. Mary imagined a cat with a furry tail. Mary decided to go. * Mary decided a cat with a furry tail. Mary decided a cat with a furry tail would be her next pet. Mary gave Lucy the food. * Mary decided Lucy the food. Mary asked the cat. Mary demanded a raise. * Mary asked a raise. Mary asked for a raise. Parsing: Dealing with Ambiguity:  Parsing: Dealing with Ambiguity Water the flowers with brown leaves. Water the flowers with the hose. English: Using Domain Knowledge:  Using Domain Knowledge (plant (isa living thing)) (flower (isa plant) (has parts leaf)) (water (isa action) (instrument mustbe container)) (hose (isa container)) A Harder One:  A Harder One John saw a boy and a girl with a red wagon with one blue and one white wheel dragging on the ground under a tree with huge branches. How Bad is the Ambiguity?:  How Bad is the Ambiguity? Kim (1) Kim and Sue (1) Kim and Sue or Lee (2) Kim and Sue or Lee and Ann (5) Kim and Sue or Lee and Ann or Jon (14) Kim and Sue or Lee and Ann or Jon and Joe (42) Kim and Sue or Lee and Ann or Jon and Joe or Zak (132) Kim and Sue or Lee and Ann or Jon and Joe or Zak and Mel (469) Kim and Sue or Lee and Ann or Jon and Joe or Zak and Mel or Guy (1430) Kim and Sue or Lee and Ann or Jon and Joe or Zak and Mel or Guy and Jan (4862) The number of parses for an expression with n terms is the n’th Catalan number: Parsing: Gapping:  Parsing: Gapping English: Who did you say Mary gave the ball to? Sentences like this make specifying the grammar difficult. They also make it hard to use a simple, context-free parser. Semantics: The Meaning of Words:  Semantics: The Meaning of Words Getting it right for the target application: “month”  RainfallByStation.month Dealing with ambiguity: “spring”  or or “stamp”  or Semantics: The Meaning of Phrases:  Olive oil Semantics: The Meaning of Phrases Semantics is (mostly) compositional. (oil (made-from olives)) But What About This One?:  But What About This One? Baby oil And Another One:  And Another One Riding jacket Leather jacket Letter jacket Rain jacket Idioms Don’t Work This Way:  Idioms Don’t Work This Way I’m going to give her a piece of my mind. He bent over backwards to make the sale. I’m going to brush up on my Spanish. Putting Phrases Together:  Putting Phrases Together Bill cooked the potatoes. The potatoes cooked in about an hour. The heat from the fire cooked the potatoes in 30 minutes. (cooking-event (agent ) (object ) (instrument ) (time-frame ) *Bill and the potatoes cooked. Language at its Most Straightforward – Propositional Content:  Language at its Most Straightforward – Propositional Content Bill Clinton was the 42nd president of the United States. Texas is in France. The Matrix is playing at the Dobie. Lunch is at noon. What time is it? When There’s More - Presuppositions:  When There’s More - Presuppositions What is Clinton famous for? Where’s The Matrix playing? Who is the king of France? Have you started making it to your morning classes? I’m going to check out all the five star restaurants in Cleveland on this trip. When There’s More – (Shared?) Presuppositions:  When There’s More – (Shared?) Presuppositions “I don’t think Alexander’s getting a proper education,” he said to her one evening. “Oh, he’s okay.” “I asked him to figure what change they’d give back when we bought the milk today, and he didn’t have the faintest idea. He didn’t even know he’d have to subtract.” “Well, he’s only in second grade,” Muriel said. “I think he ought to switch to a private school.” “Private schools cost money.” “So? I’ll pay.” She stopped flipping the bacon and looked over at him. “What are you saying?” she said. “Pardon?” What are you saying, Macon? Are you saying you’re committed?” Anne Tyler, The Accidental Tourist, cited in (YJDU, p. 175) Coherence:  Coherence Winnie doesn’t like melted ice cream. He always dreads August. * Winnie doesn’t like melted ice cream. He always dreads January. Winnie wanted to go to the store. He went to find Christopher Robin. * Winnie wanted to go to the store. He counted quickly to 10. Winnie walked into the room. Christopher Robin looked up and smiled. * Winnie walked into the room. The earth rotates around the sun. We Can’t Say it All:  We Can’t Say it All Christopher Robin and Winnie decided to go out for lunch. They remembered that Coji’s doesn’t have hot dogs on Saturdays, so they went to Buzzy’s. They got their food, slathered on the mustard, and walked home. Conversational Postulates:  Conversational Postulates Grice’s maxims: The Maxim of Quantity: Be as informative as required. Don’t be more so. The Maxim of Quality: Do not say what you believe to be false. Do not say that for which you lack sufficient evidence. Maxim of relevance: Be relevant Maxim of manner: Avoid obscurity of expression Avoid ambiguity Be brief. Be orderly. Conversational Postulates and Scalar Implicature:  Conversational Postulates and Scalar Implicature A: Have you done the first math assignment yet? B: I’m going to go buy the book tomorrow. Another Example of Scalar Implicature:  Another Example of Scalar Implicature A: When did you get home last night? B: I was in bed by midnight. When There’s More – Conversational Postulates and Inference:  When There’s More – Conversational Postulates and Inference A: Joe doesn't seem to have a girl-friend these days. B: He's been going to Dallas a lot lately. When There’s More – Conversational Postulates and Inference:  When There’s More – Conversational Postulates and Inference A: Let’s go to the movies tonight. B: I have to study for an exam. When There’s More – Conversational Postulates and Inference:  When There’s More – Conversational Postulates and Inference Reviewer of new book: It is well-bound and free of typographical errors. When There’s More – Conversational Postulates and Inference:  When There’s More – Conversational Postulates and Inference A: What do you think of my new dress? B: It’s interesting. When There’s More – Conversational Postulates and Illocutionary Force:  When There’s More – Conversational Postulates and Illocutionary Force Do you know what time it is? When There’s More – Conversational Postulates and Illocutionary Force:  When There’s More – Conversational Postulates and Illocutionary Force Do you know what time it is? What time is it? When There’s More – Conversational Postulates and Illocutionary Force:  When There’s More – Conversational Postulates and Illocutionary Force Do you know what time it is? What time is it? I’m freezing. When There’s More – Conversational Postulates and Illocutionary Force:  When There’s More – Conversational Postulates and Illocutionary Force Do you know what time it is? What time is it? I’m freezing. Get up and go close the window. When There’s More – Conversational Postulates and Illocutionary Force:  When There’s More – Conversational Postulates and Illocutionary Force Do you know what time it is? What time is it? I’m freezing. Get up and go close the window. Politeness Sometimes it is Very Subtle:  Sometimes it is Very Subtle A Bangkok dry cleaner asks its customers to, “Drop your trousers here for best results.” A Norwegian cocktail lounge asks, “Ladies are requested not to have children in the bar.” In the window of a Barcelona travel agency, “Go away.” In a laundry in Rome, “Ladies, leave your clothes here and spend the afternoon having a good time.” Underpaid guards in a Budapest zoo, “Please do not feed the animals. If you have any suitable food, give it to the guards on duty.” What About Applications Where Almost is OK?:  What About Applications Where Almost is OK? Searching the web Leaving some of the work for people Retrieval failures are ok www.google.com Snooping Going the Other Way: Generation:  Going the Other Way: Generation (c (isa cooking-event) (agent x ) (object y) (instrument z) (time-frame )) (x (isa man) (name Bill) (height 6') (attire (head- covering h)) (born-location b)) (y (some-of potatoes) (type-of Idaho)) (maturity new)) (z (isa microwave) (brand Sharp)) (h (isa gimme) (color red)) (gimme (subclass hat)) (b (isa city) (name Austin)) Going the Other Way: Generation:  Going the Other Way: Generation An example application: text summarization http://www1.cs.columbia.edu/nlp/newsblaster/ One Problem - What is Reusable?:  One Problem - What is Reusable? Because we’re dealing with a mapping problem, we can’t build a definitive, useful for all applications, NL interface. However, we can build: Lexicons that contain morphological and POS information, as well as meanings given in terms of standard KBs such as Wordnet. Morphological analyzers Syntactic parsers that appeal to a statistical and/or semantic filter through a common interface Inference engines Dialogue management systems that handle linguistic-level issues and plug into domain-specific knowledge Spoken Language - Understanding:  Spoken Language - Understanding Spoken Language - Generation:  Spoken Language - Generation The issues: Figuring out what to say (same as for written language) Pronouncing words Linking them together Getting the prosody right A Recent Loebner Prize Winner:  A Recent Loebner Prize Winner > Why do you think people want to go to the moon? Jabberwacky: You don't. Plastic and metal, perhaps. No flesh. > Can you imagine life before plastic? Japperwacky: Can you? Sure. Milk came in glass bottles. Jabberwacy: You can come here whenever you want. I will welcome you.

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