ch 13&14 Flashcards

1
Q

status of language processing

A

still rough but good enough

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2
Q

who worked on finite automata and expression of grammer

A

Kleene, Shannon (applying Markov models)

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3
Q

automata

A

construct made of states designed to determine if the input should be accepted or rejected

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4
Q

Shannon wrote a paper on

A

mathematical foundations of communication

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5
Q

formal language theory:

A

parsing and symbolic approach

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6
Q

stochastic approach to language

A

statistical modeling, capturing ambiguity

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7
Q

OCR

A

Optical Character Recognition

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8
Q

4 main paradigms of language (1970’s)

A

stochastic, symbolic, logic-based, and discourse models

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9
Q

components of languages pt 1

A

morphology, (form of words root-derived), syntax (structure), semantics (meaning)

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10
Q

components of languages pt 2

A

parsing (breaking down-prepositional phrase), lexical (alphabet),

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11
Q

components of language pt 3

A

pragmatics (study of uses), ellipsis, (omission of syntactical part of sentence, clear meaning)

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12
Q

Chomsky: syntax and grammar rules

A

necessary but not sufficient

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13
Q

Shanks CD systems pt 1

A

MARGIE (parser, generator, limited in only able to deal with single sentences)

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14
Q

CD

A

conceptual dependency

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15
Q

Shanks CD systems pt 2

A

SAM (extended MARGIE, able to do scripts, read, paraphrase, translate,)

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16
Q

Shanks CD systems pt 3

A

PAM (used text generator, writing in story goals)

17
Q

Shanks CD systems pt 4

A

CYRUS (combined earlier systems, modeled memories of cyrus vance secretary of state, learn, recognize, guess info)

18
Q

markov model is

A

a state transition diagram in which each state has a finite set, diff probabilities

19
Q

markov model assumes

A

transitions depend only on the current state and the input, prior doesn’t matter

20
Q

Penn Treebank Project

A

collection of annotated sentences, parse tree for each

21
Q

operator schemata

A

actions/events, variables,

22
Q

precondition list

A

what must be true or complete before operator can apply

23
Q

progression

A

initial state of working toward a goal

24
Q

regression

A

working backwards from initial goal

25
Q

hierarchical planning

A

prioritizing goals

26
Q

opportunistic planning

A

exploits conditions and opportunities (short stop, multtitask etc)

27
Q

Conditional planning

A

just in case planning

28
Q

General Problem Solver

A

greedy algorithm, minimize distance between state and goal

29
Q

STRIPS

A

Stanford Research Institute Problem Solver

30
Q

STRIPS

A

algorithm, similar to greedy, but with first order logic

31
Q

The frame problem

A

Keeping track of changes, details, contigencies

32
Q

Least Commitment Planning

A

partial form plans until decision expands constraints,

33
Q

Select-and-Commit

A

attempts choice then tests info according to, changing if wrong

34
Q

Depth first with backtracking

A

backtracking, struggles with exponential growth

35
Q

Beam Search

A

maintains top k searches, always chooses top k of next ones

36
Q

One, then best

A

assume, then test or decide whether or not to backtrack

37
Q

Dependency directed search

A

store a listing of decisions or contraints, you can see the affects if one is removed

38
Q

distributed planning

A

divided problem, allows separate agents to solve