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Yapay Zeka

2007 Ocak

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Doğal Dil İşleme (Natural Language Processing)

Doğal Dil Anlama (Natural Lang. Understanding)• Belirli amaçlarla söylenen ve yazılan cümleleri

anlama ile ilgilidir.

Doğal Dil Üretme (Natural Lang. Generation)• (Tam tersi) Söylenmek istenenleri, Türkçe veya

İngilizce gibi doğal bir dil ile temsiliyle ilgilenir.

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Natural Language Processing

• Natural language processing (NLP) is a subfield of artificial intelligence and linguistics.

• It studies the problems of automated generation and understanding of natural human languages.

• Natural language generation systems convert information from computer databases into normal-sounding human language, and natural language understanding systems convert samples of human language into more formal representations that are easier for computer programs to manipulate.

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Natural Language Understanding

Stages of Natural Language Understanding

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Doğal Dil Anlamanın 4 Aşaması

Speech Recognition (Ses Tanıma)

• İşlenmemiş ses sinyalleri analiz edilerek, konuşulan kelime dizisi elde edilir.

Syntactic Analysis (Sözdizimsel Analiz)

• Dilin gramer bilgisi kullanılarak kelimeler analiz edilir ve cümle yapısı elde edilir.

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Doğal Dil Anlamanın 4 Aşaması

Semantic Analysis (Anlam Analizi)

• Cümle yapısından ve içindeki kelimelerin anlamından yararlanılarak tüm cümlenin kısmi anlamı elde edilir.

Pragmatic Analysis (Pragmatik Analiz)

• Genel durum yani şartlara bakılarak cümlenin anlamı tamamlanır. Kim, ne zaman, nerede kime dedi gibi.

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Reasoning (Akıl Yürütme)

Reasoning is the act of using reason to derive a conclusion from certain premises, using a given methodology. The two most commonly used explicit methods to reach a conclusion are deductive reasoning and inductive reasoning.

From wikipedia

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Chaining (Zincirleme)There are two main methods of reasoning when using inference rules: backward chaining and forward chaining.

Forward chaining starts with the data available and uses the inference rules to conclude more data until a desired goal is reached. An inference engine using forward chaining searches the inference rules until it finds one in which the if-clause is known to be true. It then concludes the then-clause and adds this information to its data. It would continue to do this until a goal is reached. Because the data available determines which inference rules are used, this method is also called data driven.Backward chaining starts with a list of goals and works backwards to see if there is data which will allow it to conclude any of these goals. An inference engine using backward chaining would search the inference rules until it finds one which has a then-clause that matches a desired goal. If the if-clause of that inference rule is not known to be true, then it is added to the list of goals.

From wikipedia

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Forward Chainingİleri Zincirleme

• For example, suppose that the goal is to conclude the color of my pet Fritz, given that he croaks and eats flies, and that the rulebase contains the following two rules:

1. If Fritz croaks and eats flies - Then Fritz is a frog 2. If Fritz is a frog - Then Fritz is green

• The given rule (that Fritz croaks and eats flies) would first be added to the knowledgebase, as the rulebase is searched for an antecedent that matches its consequent; This is true of the first Rule, so the conclusion (that Fritz is a Frog) is also added to the knowledgebase, and the rulebase is again searched. This time, the second rules' antecedent matches our consequent, so we add to our knowledgebase the new conclusion (that Fritz is green). Nothing more can be inferred from this information, but we have now accomplished our goal of determining the color of Fritz.

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Backward ChainingGeri Zincirleme

• For example, suppose that the goal is to conclude the color of my pet Fritz, given that he croaks and eats flies, and that the rulebase contains the following two rules:

1. If Fritz croaks and eats flies - Then Fritz is a frog 2. If Fritz is a frog - Then Fritz is green

• This rulebase would be searched and the second rule would be selected, because its conclusion (the Then clause) matches the goal (that Fritz is green). It is not yet known that Fritz is a frog, so the If statement is added to the goal list (in order for Fritz to be green, he must be a frog). The rulebase is again searched and this time the first rule is selected, because its Then clause matches the new goal that was just added to the list (whether Fritz is a frog). The If clause (Fritz croaks and eats flies) is known to be true and therefore the goal that Fritz is a frog can be concluded (Fritz croaks and eats flies, so must be green; Fritz is green, so must be a frog).

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Comparing Forward Chaining & Backward Chaining

• “left to right”

• work forward from conditions to conclusion

• data-driven• bottom-up

– gather facts to infer conclusion

– i.e. building up from smaller pieces into a bigger one

• “right to left”• work backward from

hypothesis to goals• goal-driven• top-down

– break down hypothesis into subgoals

– decompose a big piece into smaller ones

Forward ChainingForward Chaining Backward ChainingBackward Chaining

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Forward vs. Backward

Forward Chaining : • The top-down approach of forward chaining is commonly

used in expert systems, such as CLIPS.

Backward Chaining :• Because the list of goals determines which rules are

selected and used, this method is called goal driven, in contrast to data-driven forward-chaining inference. This bottom-up appoach is often employed by expert systems.

• Programming languages such as Prolog or Eclipse supports backward chaining.

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Propositional logic: Syntax

Propositional logic is the simplest practical logic– The proposition symbols P1, P2 etc are sentences– If S is a sentence, (S) is also (negation)– If S1 and S2 are sentences, (S1 S)) is also (conjunction)– If S1 and S2 are sentences, (S1 S2) is also (disjunction)– If S1 and S2 are sentences, (S1 S2) is also (implication)– If S1 and S2 are sentences, (S1 S2) is also (biconditional)

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Truth tables for connectives

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Logical equivalence• Two sentences are logically equivalent means

– They are true in the same models

Some well-known equivalences

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First-order logic

• Whereas propositional logic assumes the world contains facts,

• first-order logic (like natural language) assumes the world contains– Objects: people, houses, numbers, colors, baseball

games, wars, …– Relations: red, round, prime, brother of, bigger than, part

of, comes between, …– Functions: father of, best friend, one more than, plus, …

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Syntax of FOL: Basic elements• Constants KingJohn, 2, SNU,... • Predicates Brother, sibling, >,...

– (sibling means either brother or sister)

• Functions Sqrt, LeftLegOf,...• Variables x, y, a, b,...• Connectives , , , , • Equality =

– (can be treated as just a special predicate)

• Quantifiers , – Note: we follow standard mathematical terminology, so

constants have capitals, variables are lower case– This is the opposite convention to prolog

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Semantic Networks

Bilgi bir ağ (network) veya çizge (graph) olarak temsil edilir.

Animal

Reptile

Elephant

Nellie

Mammal

apples

large

headsubclasssubclass

haspart

subclass

instance

likes

sizeAfricalivesin

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Frames

Frames were the next development, allowing more convenient “packaging” of facts about an object. Frames look much like modern classes, without the methods. We use the terms “slots” and “slot values”

mammal: subclass: animal

elephant: subclass: mammal size: large haspart: trunk

Nellie: instance: elephant likes: apples