Language and Learning Introduction to Artificial Intelligence COS302 Michael L. Littman Fall 2001.
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Transcript of Language and Learning Introduction to Artificial Intelligence COS302 Michael L. Littman Fall 2001.
Language and LearningLanguage and Learning
Introduction toIntroduction toArtificial IntelligenceArtificial Intelligence
COS302COS302
Michael L. LittmanMichael L. Littman
Fall 2001Fall 2001
Search and AISearch and AI
Powerful techniques. Do they solve Powerful techniques. Do they solve the whole AI problem?the whole AI problem?
Let’s do a thought experiment.Let’s do a thought experiment.
Chinese Room ArgumentChinese Room Argument
Searle: There is a fundamental Searle: There is a fundamental difference between symbol difference between symbol manipulation and understanding manipulation and understanding meaning.meaning.
Syntax vs. semanticsSyntax vs. semantics
Was Searle Right?Was Searle Right?
Yes/no: The richness of corpora.Yes/no: The richness of corpora.
What is judgment? Can it be What is judgment? Can it be automated?automated?
Famous QuotesFamous Quotes
““The difference between chess and The difference between chess and crossword puzzles is that, in chess, crossword puzzles is that, in chess, you know when you’ve won.” --- you know when you’ve won.” --- Michael L. LittmanMichael L. Littman
““Trying is the first stem toward Trying is the first stem toward failure.” --- Homer Simpson via my failure.” --- Homer Simpson via my cryptogram programcryptogram program
Cryptogram ExampleCryptogram Example
Is this wrong?Is this wrong?
Can you write a program that would Can you write a program that would agree with you on this?agree with you on this?
What would your program be like?What would your program be like?
Language ResourcesLanguage Resources
Three major resources:Three major resources:• DictionariesDictionaries• Labeled corporaLabeled corpora• Unlabeled corporaUnlabeled corpora
Each useful for different purposes.Each useful for different purposes.
Examples to follow…Examples to follow…
GoogleGoogle
Word matching on large corpusWord matching on large corpus
Hubs and authorities (unlabeled Hubs and authorities (unlabeled corpus, statistical processing)corpus, statistical processing)
Hand tuned ranking functionHand tuned ranking function
http://www.google.comhttp://www.google.com
Also machine translation…Also machine translation…
IonautIonaut
Question answering: Question answering: www.ionaut.comwww.ionaut.com
Hand-built question categorizationHand-built question categorizationNamed-entity tagger trained from Named-entity tagger trained from
tagged corpustagged corpusLarge unlabeled text corpusLarge unlabeled text corpusHand-tuned ranking rulesHand-tuned ranking rules
Ask JeevesAsk Jeeves
Hand-selected web pages and Hand-selected web pages and corresponding questionscorresponding questions
Proprietary mapping from query to Proprietary mapping from query to question in databasequestion in database
www.ask.comwww.ask.com
NL for DBNL for DB
Hand constructed rules turn Hand constructed rules turn sentences into DB queriessentences into DB queries
STARTSTART
http://www.ai.mit.edu/projects/http://www.ai.mit.edu/projects/infolab/ailab.htmlinfolab/ailab.html
ElizaEliza
Chatterbots very popular. Some Chatterbots very popular. Some believe they can replace “customer believe they can replace “customer care specialists”.care specialists”.
Generally a large collection of rules Generally a large collection of rules and example text.and example text.
http://www.uwec.edu/Academic/http://www.uwec.edu/Academic/Curric/jerzdg/if/WebHelp/eliza.htmCurric/jerzdg/if/WebHelp/eliza.htm
WordnetWordnet
Hand builtHand built
Rich interconnectionsRich interconnections
Showing up as a resource in many Showing up as a resource in many systems.systems.
http://www.cogsci.princeton.edu/cgi-http://www.cogsci.princeton.edu/cgi-bin/bin/
webwnwebwn
Spelling CorrectionSpelling Correction
Semi-automated selection of Semi-automated selection of confusable pairs.confusable pairs.
System trained on large corpus, System trained on large corpus, giving positive and negative giving positive and negative examples (WSJ)examples (WSJ)
http://l2r.cs.uiuc.edu/~cogcomp/eoh/http://l2r.cs.uiuc.edu/~cogcomp/eoh/spelldemo.htmlspelldemo.html
OneAcrossOneAcross
Large corpus of crossword answers: Large corpus of crossword answers: www.oneacross.comwww.oneacross.com
IR-style techniques to find relevant IR-style techniques to find relevant cluesclues
Ranking function trained from held-Ranking function trained from held-out cluesout clues
Learns from usersLearns from users
Essay GradingEssay Grading
Unsupervised learning to discover Unsupervised learning to discover word representationsword representations
Labeled graded essaysLabeled graded essays
http://www.knowledge-http://www.knowledge-technologies.com/IEAdemo.htmltechnologies.com/IEAdemo.html
More ApplicationsMore Applications
Word-sense disambiguationWord-sense disambiguation
Part of speech taggingPart of speech tagging
ParsingParsing
Reading comprehensionReading comprehension
SummarizationSummarization
CobotCobot
Cross-language IRCross-language IR
Text categorizationText categorization
SynonymsSynonyms
Carp = quit, argue, painful, scratch, Carp = quit, argue, painful, scratch, complaincomplain
Latent Semantic IndexingLatent Semantic Indexing• Corpus, deep statistical analysisCorpus, deep statistical analysis
Pointwise Mutual InformationPointwise Mutual Information• HugeHuge corpus, shallow analysis corpus, shallow analysis
WordNet…WordNet…
AnalogiesAnalogies
OvercoatOvercoat::warmthwarmth::::• GloveGlove::handhand• JewelryJewelry::wealthwealth• SlickerSlicker::moisturemoisture• DisguiseDisguise::identificationidentification• HelmetHelmet::protectionprotectionDictionary not sufficientDictionary not sufficientLabeled corpus probably wouldn’t helpLabeled corpus probably wouldn’t helpUnlabeled corpus, not obvious…Unlabeled corpus, not obvious…
What to LearnWhat to Learn
Difference between straight search Difference between straight search problems and languageproblems and language
Why learning might helpWhy learning might help
Three types of resources (hand-Three types of resources (hand-created, labeled, unlabeled)created, labeled, unlabeled)
Homework 5 (due 11/7)Homework 5 (due 11/7)
1.1. The value iteration algorithm from the The value iteration algorithm from the Games of ChanceGames of Chance lecture can be lecture can be applied to deterministic games with applied to deterministic games with loops. Argue that it produces the same loops. Argue that it produces the same answer as the “Loopy” algorithm from answer as the “Loopy” algorithm from the the Game TreeGame Tree lecture. lecture.
2.2. Write the matrix form of the game tree Write the matrix form of the game tree below.below.
ContinuedContinued
3. How many times (on average) do 3. How many times (on average) do you need to flip a coin before you you need to flip a coin before you flip 3 heads in a row? (a) Set this flip 3 heads in a row? (a) Set this up as a Markov chain, and (b) solve up as a Markov chain, and (b) solve it.it.
Homework 6 (due 11/14)Homework 6 (due 11/14)
1.1. Use the web to find sentences to Use the web to find sentences to support the analogy support the analogy traffic:street::water:riverbed. traffic:street::water:riverbed. Give the sentences and their Give the sentences and their sources.sources.
2.2. More soon…More soon…