计算机代考程序代写 data structure algorithm Computational – cscodehelp代写
Computational
Linguistics
CSC 485/2501 Fall 2021
2
2. Introduction to syntax and parsing
Department of Computer Science, University of Toronto
Reading: Jurafsky & Martin: 5.0–1, 12.0–12.3.3, 12.3.7, [13.1–2]. Bird et al: 8.0–4.
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Syntactic structure 1
• Syntax:
• The combinatorial structure of words.
• How words can be linearly organized: left/right precedence, and contiguity.
• How words can be hierarchically organized into phrases and sentences.
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Syntactic structure 2
The cat hunted the squirrel living in the tree with persistence.
[ [The cat]
[hunted [the squirrel [living [in [the tree] ] ] ]
[with [persistence] ] ] ]
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Syntactic structure 2
The cat hunted the squirrel living in the tree with persistence.
The cat hunted
the squirrel living
with persistence
in
the tree
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Syntactic structure 2
The cat hunted the squirrel living in the tree with persistence.
S
VNP PP
NP
VP
The cat hunted NP AP
DET N
DET N V the squirrel living
P NP PP with
P NP
N
in DET N persistence
the tree
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Syntactic structure 3
• Goal: meaning, interpretation, semantics. • So why do we care about syntax?
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Grammars and parsing
• Grammar:
• Formal specification of allowable structures.
• Knowledge
• Representation
• Parsing:
• Analysis of string of words to determine the
structure assigned by grammar. • Algorithm
• Process
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Using grammar to capture structure
• Main issues:
• Which words are grouped together into phrases.
• How words within a phrase project the properties of a single, common word (the head of the phrase).
• How different phrases relate to each other.
• Grammar encodes these relations. Some grammars interpret these relations with respect to meaning.
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Good and bad grammars
• There are many possible grammars for any natural language.
• Some are better than others. • Desiderata:
• Faithfulness to (vastly divergent) details about language.
• Economy of description.
• Fidelity to some prevailing linguistic intuition.
• Efficiency of parsing.
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Elements of grammar
• Primitives: lexical categories or parts of speech.
• Each word-type is a member of one or more.
• Each word-token is an instance of exactly one. e.g. The cat in the hat sat.
• Categories are open or closed to new words.
• Eight main categories, many subcategories.
X
Nine
X Seven
X
Twenty-three
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Elements of grammar
• Primitives: lexical categories or parts of speech.
• Each word-type is a member of one or more.
• Each word-token is an instance of exactly one.
• Categories are open or closed to new words.
• Eight main categories, many subcategories.
X
Nine
X Seven
X
Twenty-three
• The categories might possibly be language- specific as well.
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Parts of speech 1
• Nouns: denote an object, a concept, a place, …
• Count nouns: dog, spleen, Band-Aid, …
• Mass nouns: water, wheat, …
• Proper nouns: Fred, City, … • Pronouns: he, she, you, I, they, …
• Adjectives: denote an attribute of the denotation of a noun.
• Intersective: pink, furry, …
• Measure: big, …
• Intensional: former, alleged, …
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Parts of speech 2
• Determiners, articles: specify certain attributes of the denotation of a noun that are grammatically relevant.
• the, a, some, …
• Verbs: predicates, denote an action or a
state. Numerous distinctions, e.g. transitivity:
• Intransitive: sleep, die, …
• Transitive: eat, kiss, …
• Ditransitive: give, sell, …
• Copula: be, feel, become, …
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Parts of speech 3
• Adverbs: denote an attribute of the denotation of a predicate.
• Time and place: today, there, now, …
• Manner: happily, furtively, …
• Degree: much, very, …
• Prepositions: relate two phrases with a location, direction, manner, etc.
• up, at, with, in front of, before, …
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Parts of speech 4
• Conjunctions: combine two clauses or phrases:
• Coordinating conjunctions: and, or, but
• Subordinating conjunctions: because, while,…
• Interjections: stand-alone emotive expressions:
• um, wow, oh dear, balderdash, crikey, …
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Elements of grammar
• Combinations:
• Phrase: a hierarchical grouping of words and/or
phrases.
• Clause: a phrase consisting of a verb and (almost) all of its dependents.
• Sentence: a clause that is syntactically independent of other clauses.
• Can be represented by tree (or a labelled bracketing).
• Terminology: A constituent is a well-formed phrase with overtones of semantic and/or
psychological significance.
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Types of phrase 1
• Noun phrase (NP): • a mouse
• mice
• Mickey
• the handsome marmot
• the handsome marmot on the roof
• the handsome marmot whom I adore
• Verb phrase (VP):
• laughed loudly
• quickly gave the book to Mary
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Types of phrase 2
• Adjective phrase (AP): • green
• proud of Kyle
• very happy that you went
• Prepositional phrase (PP): • in the sink
• without feathers
• astride the donkey
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Clauses and sentences 1
• Clauses:
• Ross remarked upon Nadia’s dexterity
• to become a millionaire by the age of 30
• that her mother had lent her for the banquet
• Sentences:
• Ross remarked upon Nadia’s dexterity.
• Nathan wants to become a millionaire by the age
of 30.
• Nadia rode the donkey that her mother had lent
her for the banquet.
• The handsome marmot on the roof [in dialogue].
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Clauses and sentences 2
• Clauses may act as noun phrases:
• To become a millionaire by the age of 30 is what
Ross wants.
• Nadia riding her donkey is a spectacular sight.
• Ross discovered that Nadia had been feeding his
truffles to the donkey.
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The structure of an idealized phrase
XP → ZP X YP
XP
subject or pre-modifier
head xxxx object,complementor word post-modifier, adjunct
ZP X
YP
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Example phrases
AP VP ADVAS ADVVPPPP
very happy
that you went
quickly go
S
NP AUX VP Kim will go
to the store
with Maya
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Formal definition of a CFG
• A context-free grammar is a quadruple G = (Vt,Vn, P, S), where
• Vt is a finite set of terminal symbols.
• Vn is a finite set of non-terminal symbols.
• P is a finite set of production rules of the form A→ α
where A ∈ Vn and α is a sequence of symbols in (Vn ∪ Vt)*.
• S ∈ Vn is the start symbol.
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A very simple grammar
S = S,P = {
S → NP VP NP → Det N
NP → Det Adj N NP → NP PP VP → V
VP → V NP
PP → P NP
Det →the|a|an
Adj →old|red|happy|…
N → dog | park | statue | contumely | run | …
V → saw | ate | run | disdained | … P →in|to|on|under|with|…
Vt and Vn can be inferred from the production rules.
The lexicon:
In practice, a sep- arate data structure
Lexical categories: NT’s that rewrite as a single T.
}
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Terminology
• Non-terminal (NT):
A symbol that occurs on the left-hand side (LHS) of
some rule.
• Pre-terminal: a kind of non-terminal located on the LHS of a lexical entry.
• Terminal (T):
A symbol that never occurs on the LHS of a rule.
• Start symbol:
A specially designated NT that must be the root of
any tree derived from the grammar.
In our grammars, it is usually S for sentence.
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Parsing 1
• Parsing: Determining the structure of a sequence of words, given a grammar.
• Which grammar rules should be used?
• To which symbols (words / terminals and nodes / non-terminals) should each rule apply?
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Parsing 2
• Input:
• A context-free grammar.
• A sequence of words Time flies like an arrow
or, more precisely, of sets of parts of speech. {noun,verb} {noun,verb} {verb,prep} {det} {noun}
• Process:
• (Working from left to right?,) guess how each word fits in.
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Depth-first Parsing 3
• If a guess leads to failure (parse is stymied), back up to a choice point and try a different guess.
• Backtracking, non-determinism.
• At each guess, must save state of parse on a
stack.
• (Or, explore in parallel.)
• Want to guess right:
• Order of preference for rules.
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Top-down parsing 1
• Top-down or rule-directed parsing:
“Can I take these rules and match them to this input?”
• Initial goal is an S.
• Repeatedly look for rules that decompose /expand current goals and give new goals.
E.g., goal of S may decompose to goals NP and VP.
• Eventually get to goals that look at input. E.g., goal of NP may decompose to det noun.
• Succeed iff entire input stream is accounted for as S. 31
Top-down parsing 2
• Example: A recursive descent parser. >>> nltk.app.rdparser()
• Operations on leftmost frontier node:
• Expand it.
• Match it to the next input word.
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Top-down parsing 3
• Choice of next operation (in NLTK demo):
• If it’s a terminal, try matching it to input.
• If it’s a non-terminal, try expanding with first-listed untried rule for that non-terminal.
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Bottom-up parsing 1
• Bottom-up or data-directed parsing: “Can I take this input and match it to these rules?”
• Try to find rules that match a possible PoS of the input words …
• … and then rules that match the constituents so formed.
• Succeed iff the entire input is eventually matched to an S.
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Bottom-up parsing 2
• Example: A shift–reduce parser. >>> nltk.app.srparser()
• Operations:
• Shift next input word onto stack.
• Match the top n elements of stack to RHS of rule, reduce them to LHS.
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Bottom-up parsing 3
• Choice of next operation (in NLTK demo):
• Always prefer reduction to shifting.
• Choose the first-listed reduction that applies.
• Choice of next operation (in real life):
• Always prefer reduction to shifting for words, but not necessarily for larger constituents.
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Problems
• Neither top-down nor bottom-up search exploits useful idiosyncrasies that CFG rules, alone or together, often have.
• Problems:
• Recomputation of constituents.
• Recomputation of common prefixes.
• Solution: Keep track of:
• Completed constituents.
• Partial matches of rules.
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