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2. Syntactic Analysis: Parsing

2. Syntactic Analysis: Parsing. 2. Parsing . Suppose we want a previous grammar to recognize the sentence: “ The large cat eats the small rat ”. Definite Clause Grammar . sentence --> nounPhrase, verbPhrase. nounPhrase --> article, adjective, noun. verbPhrase --> verb, nounPhrase.

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2. Syntactic Analysis: Parsing

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  1. 2. Syntactic Analysis: Parsing 2. Parsing • Suppose we want a previous grammar to recognize the sentence: “The large cat eats the small rat” • Definite Clause Grammar sentence --> nounPhrase, verbPhrase. nounPhrase --> article, adjective, noun. verbPhrase --> verb, nounPhrase. • Using these rules we can determine whether a sentence is legal, and obtain its structure. • The sentence consists of: • Noun Phrase: The large cat • Verb Phrase: eats the small rat. • The verb phrase in turn consists of: • verb: eats • Noun Phrase: the small rat . • The sentence above is legal. Ch05B / 1

  2. 2. Syntactic Analysis: Parsing Parsing Tree • The structure of the sentence according to grammar can be represented as a tree: Ch05B / 2

  3. 2. Syntactic Analysis: Parsing • The tree structure gives you groupings of words. (e.g., the small cat). • These are meaningful groupings - considering these together helps in working out what the sentence means. • Basic approach of parsing is based on rewriting. • To parse a sentence you must be able to “rewrite” the “start” symbol (in this case sentence) to the sequence of syntactic categories corresponding to the sentence. • You can rewrite a symbol using one of the grammar rules if it corresponds to the LHS of a rule. You then just replace it with the symbols in LHS. e.g., • You can rewrite a symbol using one of the grammar rules if it corresponds to the LHS of a rule. You then just replace it with the symbols in LHS. e.g., • sentence • nounPhrase verbPhrase • article adjective noun verbPhrase • etc Ch05B / 3

  4. 2. Syntactic Analysis: Extended Grammar A little more on grammars • Example grammar will ONLY parse sentences of a very restricted form. • What about: • “John jumps” • The man jumps”. • John jumps in the pond • We need to add extra rules to cover some of these cases • Extended Grammar sentence --> nounPhrase, verbPhrase. nounPhrase --> article, adjective, noun. nounPhrase --> article, noun. nounPhrase --> properName. verbPhrase --> verb, nounPhrase. verbPhrase --> verb. • (Think how you might handle “in the pond”.) • Grammar now parses: • John jumps the pond. Ch05B / 4

  5. 2. Syntactic Analysis: Extended Grammar • And fails to parse ungrammatical ones like: • jumps pond John the NL Grammars • A good NL grammar should: • cover a reasonable subset of natural language. • Avoid parsing ungrammatical sentences • (or at least, ones that are viewed as not acceptable in the target application). • Assign plausible structures to the sentence, where meaningful bits of the sentence are grouped together. • But, the role is NOT to check that a sentence is grammatical. By excluding dodgy sentences the grammar is more likely to get the right structure of a sentence. Ch05B / 5

  6. 2. Syntactic Analysis: Extended Grammar • Consider following examples: • “John likes.” NOT OK • “John jumps.” OK • “John jumps in the water,” OK • “The small fluffy cat jumps.” OK • John like the cat. NOT OK. • The cats likes John. NOT OK. Better Grammar • Should deal with: • Intransive/Transitive verbs. Former are ones that don’t need following noun phrase. • Prepositional phrases (e.g., in the lake). Prepostion followed by noun phrase. • Series of adjectives. Recursive rule can be used. • Subject-verb agreement. Can add arguments to grammar rules/dictionary entries. Ch05B / 6

  7. 2. Syntactic Analysis: Extended Grammar sentence --> np(Num), vp(Num). np(Num) --> art, noun(Num). noun(sing) --> [cat]. 3. Semantics • Syntax: Uses grammar to structure sentence. • Semantics: Maps this to a structured representation that can be used in inference. (often referred to as sentence meaning) • Possible representations: • SQL. Map “Find me all the students who are taking AI3” to relevant SQL query. • Predicate Logic: Map “John loves anyone who is tall” onto relevant statement in predicate logic. • Other structured rep: (e.g., “case frame”: action: loves subject: john object: mary Ch05B / 7

  8. 3. Semantics • How do we get from the parsed sentence to this kind of representation? • In general rather tricky, but to illustrate idea we will show how it could be done for “John loves Mary” by adding extra arguments to a prolog grammar. • We want to map that sentence to • loves(john, mary). • We will cheat by assuming that the functor pf Prolog structured objects can be a variable. • Verb(Object, Subject) Grammar with Semantics Sentence(Verb(Subject, Object)) --> nounPhrase(Subject), verbPhrase(Verb, Object). nounPhrase(Subject) --> properName(Subject). verbPhrase(Verb, Object) --> verb(Verb), nounPhrase(Object). • General idea is that we can “compose” the sentence meaning by working out the “meaning” of the syntactic constituents and sticking the results together somehow. Ch05B / 8

  9. 4. Pragmatics • But can’t get very far without knowing something about the world, and the context in which a sentence is uttered. • Pragmatics deals with this. • Example. Determining referents of pronouns etc. • “John likes that blue car. He buys it.” • We need context to determine what he is referring to in “that blue car”, “he”, it”. • Then can create meaning: likes(john, car1) and buys(john, car1). • Pragmatics is also about what people DO with language. • Making sense of, and generating language involves mapping language to goals. • “Do you have the time?” -> speaker wants to know the time. • “When is the last train to London?” -> speaker probably wants to go there. • We can apply some of our planning ideas to this problem. Ch05B / 9

  10. 4. Pragmatics Pragmatics and Plans • As an example of a plan-based approach to language, consider the actions of requesting, informing, asking. • Referred to as “speech acts”. • We can describe these as planning operators. • The preconditions and effects refer to speaker and hearer’s beliefs and desires. • We use a notation to describe these: • knows(Agent, Fact) • wants(Agent, State/Action) • e.g., wants(fred, kiss(fred, mary)) • knows(fred, loves(mary, joe)) Ch05B / 10

  11. 4. Pragmatics More speech acts • Sketch of inform, request, • inform(Speaker, Hearer, Fact) pre: knows(Speaker, Fact) wants(Speaker, knows(Hearer, Fact)) add: knows(Hearer, Fact) knows(Speaker, knows(Hearer, Fact)) • How does this oversimplify the “informing” action? • request(Speaker, Hearer, do(Hearer, Action)) pre: wants(Speaker, Action) knows(Speaker, cando(Hearer, Action)) add: wants(hearer, Action) • (Note: A bit tricky to integrate with ordinary planning rules.) Ch05B / 11

  12. 4. Pragmatics Putting it all together • Given sentences like spoken by John about Fred: • “What is the time?” • He has missed the train. • Can now • parse the sentence • map that to a structured representation that is good for inference. • Use context and knowledge of goals/plans to obtain from that: • wants(john, know(john, time1)) (where time1 is the time at some instant) • believes(john, missed(fred, train2)) Ch05B / 12

  13. 4. Pragmatics Language Generation • Language processing also about generation of language. • Structured representation --> NL text. • Simplest generation method is using templates, mapping representation straight to text template (with variables/slots to fill in). • loves(X, Y) -> X “loves” Y • gives(X, Y, Z) -> X “gives the” Y “to” Z • Mail-merge tools in word processors work similarly, extracting data from simple database to fill slots. • But much more to language generation in general. Templates are very rigid. • Consider “John eats the cheese. John eats the apple. John sneezes. John laughs.” • Better as “John eats the cheese and apple, then sneezes. He then laughs.” • Getting good style involves working out how to map many facts to one sentence, when to use pronouns, when to use “connectives” like “then”. Ch05B / 13

  14. 4. Pragmatics • Serious language generation involves deciding: • what to say. • how to order and structure it. • How to break it up into sentences. • How to refer to objects (using pronouns, and expressions like “the cat” etc). • How to express things in terms of grammatically correct sentences. • Often starting point is a communicative goal Ch05B / 14

  15. Summary • Natural Language Processing covers understanding and generating spoken and written language, from sentences to large texts. • Focus on understanding sentences. First step is to parse sentence to derive structure. • Use grammar rules which define constituency structure of language. • Parse gives tree structure which shows how words are grouped together. • NL Processing includes: • Syntax • Semantics • Pragmatics • And involves: • Generating language • Understanding language Ch05B / 15

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