Natural Language Processing Syntax

Joakim Nivre

Uppsala University Department of Linguistics and Philology joakim.nivre@lingﬁl.uu.se

Natural Language Processing 1(7) What is Syntax?

! Words do not occur in isolation – they combine into sentences Snoopy hugs Woodstock Woodstock hugs Snoopy Snoopy hugs birds *Birds hugs Snoopy Birds hug Snoopy Snoopy hugs them *Them hug Snoopy They hug Snoopy

! Syntax – the study of sentence structure

Natural Language Processing 2(7) Two Views of Syntax

! Dependency: ! Syntactic structure resides in relations between words ! Focus on the functional roles (subject, object,...) ! Long tradition in descriptive grammar going back to Antiquity ! Constituency: ! Syntactic structure consists in the composition of phrases ! Focus on structural categories (noun phrase, verb phrase,...) ! Introduced in structural linguistics in the 20th century

Natural Language Processing 3(7) Dependency

! Syntactic structure represented by dependency trees ! Words represented by tree nodes ! Dependencies represented by directed arcs between nodes ! Functional roles speciﬁed by arc labels

punct

obj pobj amod subj amod pmod amod

Economic news had little eﬀect on ﬁnancial markets . ADJ NOUN VERB ADJ NOUN ADP ADJ NOUN PUNCT

Natural Language Processing 4(7) Constituency

! Syntactic structure represented by phrase structure trees ! Words represented by terminal tree nodes (leaves) ! Phrases represented by internal tree nodes ! Phrase types speciﬁed by node labels ! Phrase structure trees can be deﬁned by context-free grammars

S ✑✑◗◗ ✑ ◗ ✑ ◗ ✑ VP❍ ◗ ✑ ❍ ◗ ✑ NP ◗ ✑ ✧❍ ◗ ✑ ✧ ❍ ◗ ✑ ✧ ◗ ✑ ✧ PP ◗ ✑ ✧ ❍❍ ◗ NP NP NP Punct ✟✟❍❍ ✟✟❍❍ ✟✟❍❍ Adj Noun Verb Adj Noun Prep Adj Noun

Economic news had little eﬀect on ﬁnancial markets .

Natural Language Processing 5(7) Two Complementary Views?

! Dependency trees explicitly represent ! dependency relations (directed arcs) ! functional categories (arc labels) ! possibly some structural categories (parts-of-speech) ! Phrase structure trees explicitly represent ! phrases (internal nodes) ! structural categories (node labels) ! possibly some functional categories (grammatical functions)

! Both are widely used in NLP – and hybrid representations exist

Natural Language Processing 6(7) Quiz

! True or false? 1. Syntax studies the internal structure or words 2. Syntax studies the order of words in sentences 3. Dependency trees show how words are related in sentences 4. Dependency trees show how sentences are composed of phrases

Natural Language Processing 7(7) Natural Language Processing Dependency

Joakim Nivre

Uppsala University Department of Linguistics and Philology joakim.nivre@lingﬁl.uu.se

Natural Language Processing 1(9) Dependency

! Syntactic structure consists of words,relatedbybinary, asymmetric relations called dependencies

Economic news had little eﬀect on ﬁnancial markets . ADJ NOUN VERB ADJ NOUN ADP ADJ NOUN PUNCT

Natural Language Processing 2(9) Dependency

! Syntactic structure consists of words,relatedbybinary, asymmetric relations called dependencies

Economic news had little eﬀect on ﬁnancial markets . ADJ NOUN VERB ADJ NOUN ADP ADJ NOUN PUNCT

Natural Language Processing 2(9) Dependency

! Syntactic structure consists of words,relatedbybinary, asymmetric relations called dependencies

Economic news had little eﬀect on ﬁnancial markets . ADJ NOUN VERB ADJ NOUN ADP ADJ NOUN PUNCT

Natural Language Processing 2(9) Dependency

! Syntactic structure consists of words,relatedbybinary, asymmetric relations called dependencies

Economic news had little eﬀect on ﬁnancial markets . ADJ NOUN VERB ADJ NOUN ADP ADJ NOUN PUNCT

Natural Language Processing 2(9) Dependency

! Syntactic structure consists of words,relatedbybinary, asymmetric relations called dependencies

Economic news had little eﬀect on ﬁnancial markets . ADJ NOUN VERB ADJ NOUN ADP ADJ NOUN PUNCT

Natural Language Processing 2(9) Dependency

! Syntactic structure consists of words,relatedbybinary, asymmetric relations called dependencies

Economic news had little eﬀect on ﬁnancial markets . ADJ NOUN VERB ADJ NOUN ADP ADJ NOUN PUNCT

Natural Language Processing 2(9) Dependency

! Syntactic structure consists of words,relatedbybinary, asymmetric relations called dependencies

obj pobj amod subj amod pmod amod

Economic news had little eﬀect on ﬁnancial markets . ADJ NOUN VERB ADJ NOUN ADP ADJ NOUN PUNCT

Natural Language Processing 2(9) Terminology

Higher Lower Head Dependent Governor Modiﬁer Regent Subordinate . . . .

Natural Language Processing 3(9) Terminology

Higher Lower Head Dependent Governor Modiﬁer Regent Subordinate . . . .

Natural Language Processing 3(9) Criteria for Heads and Dependents

! Criteria for a syntactic relation between a head H and a dependent D in a construction C: 1. H determines the syntactic category of C; H can replace C. 2. H determines the semantic category of C; D speciﬁes H. 3. H is obligatory; D may be optional. 4. H selects D and determines whether D is obligatory. 5. The form of D depends on H (agreement or government). 6. The linear position of D is speciﬁed with reference to H. ! Issues: ! Syntactic (and morphological) versus semantic criteria ! Exocentric versus endocentric constructions

Natural Language Processing 4(9) Some Clear Cases

Construction Head Dependent Exocentric Verb Subject (subj) Verb Object (obj) Endocentric Verb Adverbial (advmod) Noun Attribute (amod)

Economic news suddenly aﬀected ﬁnancial markets . ADJ NOUN ADV VERB ADJ NOUN PUNCT

Natural Language Processing 5(9) Some Clear Cases

Construction Head Dependent Exocentric Verb Subject (subj) Verb Object (obj) Endocentric Verb Adverbial (advmod) Noun Attribute (amod)

subj

Economic news suddenly aﬀected ﬁnancial markets . ADJ NOUN ADV VERB ADJ NOUN PUNCT

Natural Language Processing 5(9) Some Clear Cases

Construction Head Dependent Exocentric Verb Subject (subj) Verb Object (obj) Endocentric Verb Adverbial (advmod) Noun Attribute (amod)

subj obj

Economic news suddenly aﬀected ﬁnancial markets . ADJ NOUN ADV VERB ADJ NOUN PUNCT

Natural Language Processing 5(9) Some Clear Cases

Construction Head Dependent Exocentric Verb Subject (subj) Verb Object (obj) Endocentric Verb Adverbial (advmod) Noun Attribute (amod)

subj obj advmod

Economic news suddenly aﬀected ﬁnancial markets . ADJ NOUN ADV VERB ADJ NOUN PUNCT

Natural Language Processing 5(9) Some Clear Cases

Construction Head Dependent Exocentric Verb Subject (subj) Verb Object (obj) Endocentric Verb Adverbial (advmod) Noun Attribute (amod)

subj obj amod advmod amod

Economic news suddenly aﬀected ﬁnancial markets . ADJ NOUN ADV VERB ADJ NOUN PUNCT

Natural Language Processing 5(9) Some Tricky Cases

! Complex verb groups (auxiliary main verb) ↔ ! Subordinate clauses (complementizer verb) ↔ ! Coordination (coordinator conjuncts) ↔ ! Prepositional phrases (preposition nominal) ↔ ! Punctuation

I can see that they rely on this and that . PRON AUX VERB SCONJ PRON VERB ADP PRON CONJ PRON PUNCT

Natural Language Processing 6(9) Some Tricky Cases

subj ccomp

I can see that they rely on this and that . PRON AUX VERB SCONJ PRON VERB ADP PRON CONJ PRON PUNCT

Natural Language Processing 6(9) Some Tricky Cases

subj ccomp

I can see that they rely on this and that . PRON AUX VERB SCONJ PRON VERB ADP PRON CONJ PRON PUNCT

Natural Language Processing 6(9) Some Tricky Cases

I can see that they rely on this and that . PRON AUX VERB SCONJ PRON VERB ADP PRON CONJ PRON PUNCT

Natural Language Processing 6(9) Some Tricky Cases

I can see that they rely on this and that . PRON AUX VERB SCONJ PRON VERB ADP PRON CONJ PRON PUNCT

Natural Language Processing 6(9) Some Tricky Cases

I can see that they rely on this and that . PRON AUX VERB SCONJ PRON VERB ADP PRON CONJ PRON PUNCT

Natural Language Processing 6(9) Treebanks

! Treebanks ! Syntactically annotated corpora are called treebanks ! Treebanks can be used to train and evaluate syntactic parsers ! Dependency treebanks ! Treebanks with dependency-based annotation ! Example: Prague Dependency Treebank of Czech ! Annotation schemes can vary considerably across languages

Natural Language Processing 7(9) Universal Dependencies (UD)

! Standardized framework for dependency annotation ! Consistent analysis across typologically diﬀerent languages

obl

obj case det nsubj det det

DET NOUN VERB DET NOUN ADP DET NOUN the dog chased the cat from the room koira jahtasi kissan huoneesta NOUN VERB NOUN NOUN Case=Nom Case=Acc Case=Ela

nsubj obj obl

Natural Language Processing 8(9) Quiz

The big bear scared the little dog.

! True or false 1. The word dog is a dependent of the word bear 2. The word bear is a dependent of the word scared 3. The word scared is a dependent of the word little 4. The word little is a dependent of the word dog

Natural Language Processing 9(9) Natural Language Processing Constituency

Joakim Nivre

Uppsala University Department of Linguistics and Philology joakim.nivre@lingﬁl.uu.se

Natural Language Processing 1(11) Constituency

! Word groups can act as single units Los Angeles a high-class spot such as Mindy’s three parties from Brooklyn they

! Such groups of words are called constituents ! Constituents have similar internal structure and behavior

Natural Language Processing 2(11) The girl in the corner + wears a yellow hat and dark sunglasses. [The girl + in the corner] [wears + a yellow hat and dark sunglasses]. [The + girl] [in + the corner] wears [a yellow hat + and + dark sunglasses]. The girl in [the + corner] wears [a + yellow hat] and [dark + sunglasses]. The girl in the corner wears a [yellow + hat] and dark sunglasses.

Immediate Constituency Analysis

! We can ﬁnd constituents by recursive decomposition:

The girl in the corner wears a yellow hat and dark sunglasses.

Natural Language Processing 3(11) [The girl + in the corner] [wears + a yellow hat and dark sunglasses]. [The + girl] [in + the corner] wears [a yellow hat + and + dark sunglasses]. The girl in [the + corner] wears [a + yellow hat] and [dark + sunglasses]. The girl in the corner wears a [yellow + hat] and dark sunglasses.

Immediate Constituency Analysis

! We can ﬁnd constituents by recursive decomposition:

The girl in the corner wears a yellow hat and dark sunglasses. The girl in the corner + wears a yellow hat and dark sunglasses.

Natural Language Processing 3(11) [The + girl] [in + the corner] wears [a yellow hat + and + dark sunglasses]. The girl in [the + corner] wears [a + yellow hat] and [dark + sunglasses]. The girl in the corner wears a [yellow + hat] and dark sunglasses.

Immediate Constituency Analysis

! We can ﬁnd constituents by recursive decomposition:

The girl in the corner wears a yellow hat and dark sunglasses. The girl in the corner + wears a yellow hat and dark sunglasses. [The girl + in the corner] [wears + a yellow hat and dark sunglasses].

Natural Language Processing 3(11) The girl in [the + corner] wears [a + yellow hat] and [dark + sunglasses]. The girl in the corner wears a [yellow + hat] and dark sunglasses.

Immediate Constituency Analysis

! We can ﬁnd constituents by recursive decomposition:

Natural Language Processing 3(11) The girl in the corner wears a [yellow + hat] and dark sunglasses.

Immediate Constituency Analysis

! We can ﬁnd constituents by recursive decomposition:

The girl in the corner wears a yellow hat and dark sunglasses. The girl in the corner + wears a yellow hat and dark sunglasses. [The girl + in the corner] [wears + a yellow hat and dark sunglasses]. [The + girl] [in + the corner] wears [a yellow hat + and + dark sunglasses]. The girl in [the + corner] wears [a + yellow hat] and [dark + sunglasses].

Natural Language Processing 3(11) Immediate Constituency Analysis

! We can ﬁnd constituents by recursive decomposition:

Natural Language Processing 3(11) Test for Constituents

! Substitution: similar constituents can replace each other The girl in the corner wears a yellow hat and dark sunglasses The girl wears a yellow hat and dark sunglasses She wears a yellow hat and dark sunglasses *In the corner wears a yellow hat and dark sunglasses ! Movement: words in a constituent move together She went to Paris on Monday. On Monday she went to Paris. *Paris on Monday she went to. *On she went to Paris Monday.

Natural Language Processing 4(11) Constituent Types

! Noun phrase (NP) she the house Robin Hood and his merry men ! Verb phrase (VP) blushed loves Mary was told to sit down and be quiet ! Prepositional phrase (PP) on it with the telescope through the foggy dew

Natural Language Processing 5(11) Context-Free Grammar

! Acontext-freegrammar(CFG)consistsof ! a finite set of nonterminal symbols ! a finite set of terminal symbols ! a distinguished nonterminal symbol S (for Start) ! a finite set of rules of the form

A α → where A is a nonterminal and α is a (possibly empty) sequence of nonterminal and terminal symbols

Natural Language Processing 6(11) Grammar Lexicon

Example Grammar

S NP VP Punct Verb had → → VP VP PP Noun news → → VP Verb NP Noun eﬀect → → NP NP PP Noun markets → → NP Adj Noun Adj Economic → → PP Prep NP Adj little → → Adj ﬁnancial → Prep on → Punct . →

Natural Language Processing 7(11) Example Grammar

Grammar Lexicon S NP VP Punct Verb had → → VP VP PP Noun news → → VP Verb NP Noun eﬀect → → NP NP PP Noun markets → → NP Adj Noun Adj Economic → → PP Prep NP Adj little → → Adj ﬁnancial → Prep on → Punct . →

Natural Language Processing 7(11) NP VP Punct Adj Noun VP Punct Economic Noun VP Punct Economic news VP Punct Economic news Verb NP Punct Economic news had NP Punct Economic news had NP PP Punct Economic news had Adj Noun PP Punct Economic news had little Noun PP Punct Economic news had little effect PP Punct Economic news had little effect Prep NP Punct Economic news had little effect on NP Punct Economic news had little effect on Adj Noun Punct Economic news had little effect on financial Noun Punct Economic news had little effect on financial markets Punct Economic news had little effect on financial markets .

Derivations

Natural Language Processing 8(11) Adj Noun VP Punct Economic Noun VP Punct Economic news VP Punct Economic news Verb NP Punct Economic news had NP Punct Economic news had NP PP Punct Economic news had Adj Noun PP Punct Economic news had little Noun PP Punct Economic news had little effect PP Punct Economic news had little effect Prep NP Punct Economic news had little effect on NP Punct Economic news had little effect on Adj Noun Punct Economic news had little effect on financial Noun Punct Economic news had little effect on financial markets Punct Economic news had little effect on financial markets .

Derivations

S NP VP Punct

Natural Language Processing 8(11) Economic Noun VP Punct Economic news VP Punct Economic news Verb NP Punct Economic news had NP Punct Economic news had NP PP Punct Economic news had Adj Noun PP Punct Economic news had little Noun PP Punct Economic news had little effect PP Punct Economic news had little effect Prep NP Punct Economic news had little effect on NP Punct Economic news had little effect on Adj Noun Punct Economic news had little effect on financial Noun Punct Economic news had little effect on financial markets Punct Economic news had little effect on financial markets .

Derivations

S NP VP Punct Adj Noun VP Punct

Natural Language Processing 8(11) Economic news VP Punct Economic news Verb NP Punct Economic news had NP Punct Economic news had NP PP Punct Economic news had Adj Noun PP Punct Economic news had little Noun PP Punct Economic news had little effect PP Punct Economic news had little effect Prep NP Punct Economic news had little effect on NP Punct Economic news had little effect on Adj Noun Punct Economic news had little effect on financial Noun Punct Economic news had little effect on financial markets Punct Economic news had little effect on financial markets .

Derivations

S NP VP Punct Adj Noun VP Punct Economic Noun VP Punct

Natural Language Processing 8(11) Economic news Verb NP Punct Economic news had NP Punct Economic news had NP PP Punct Economic news had Adj Noun PP Punct Economic news had little Noun PP Punct Economic news had little effect PP Punct Economic news had little effect Prep NP Punct Economic news had little effect on NP Punct Economic news had little effect on Adj Noun Punct Economic news had little effect on financial Noun Punct Economic news had little effect on financial markets Punct Economic news had little effect on financial markets .

Derivations

S NP VP Punct Adj Noun VP Punct Economic Noun VP Punct Economic news VP Punct

Natural Language Processing 8(11) Economic news had NP Punct Economic news had NP PP Punct Economic news had Adj Noun PP Punct Economic news had little Noun PP Punct Economic news had little effect PP Punct Economic news had little effect Prep NP Punct Economic news had little effect on NP Punct Economic news had little effect on Adj Noun Punct Economic news had little effect on financial Noun Punct Economic news had little effect on financial markets Punct Economic news had little effect on financial markets .

Derivations

S NP VP Punct Adj Noun VP Punct Economic Noun VP Punct Economic news VP Punct Economic news Verb NP Punct

Natural Language Processing 8(11) Economic news had NP PP Punct Economic news had Adj Noun PP Punct Economic news had little Noun PP Punct Economic news had little effect PP Punct Economic news had little effect Prep NP Punct Economic news had little effect on NP Punct Economic news had little effect on Adj Noun Punct Economic news had little effect on financial Noun Punct Economic news had little effect on financial markets Punct Economic news had little effect on financial markets .

Derivations

S NP VP Punct Adj Noun VP Punct Economic Noun VP Punct Economic news VP Punct Economic news Verb NP Punct Economic news had NP Punct

Natural Language Processing 8(11) Economic news had Adj Noun PP Punct Economic news had little Noun PP Punct Economic news had little effect PP Punct Economic news had little effect Prep NP Punct Economic news had little effect on NP Punct Economic news had little effect on Adj Noun Punct Economic news had little effect on financial Noun Punct Economic news had little effect on financial markets Punct Economic news had little effect on financial markets .

Derivations

S NP VP Punct Adj Noun VP Punct Economic Noun VP Punct Economic news VP Punct Economic news Verb NP Punct Economic news had NP Punct Economic news had NP PP Punct

Natural Language Processing 8(11) Economic news had little Noun PP Punct Economic news had little effect PP Punct Economic news had little effect Prep NP Punct Economic news had little effect on NP Punct Economic news had little effect on Adj Noun Punct Economic news had little effect on financial Noun Punct Economic news had little effect on financial markets Punct Economic news had little effect on financial markets .

Derivations

S NP VP Punct Adj Noun VP Punct Economic Noun VP Punct Economic news VP Punct Economic news Verb NP Punct Economic news had NP Punct Economic news had NP PP Punct Economic news had Adj Noun PP Punct

Natural Language Processing 8(11) Economic news had little effect PP Punct Economic news had little effect Prep NP Punct Economic news had little effect on NP Punct Economic news had little effect on Adj Noun Punct Economic news had little effect on financial Noun Punct Economic news had little effect on financial markets Punct Economic news had little effect on financial markets .

Derivations

Natural Language Processing 8(11) Economic news had little effect Prep NP Punct Economic news had little effect on NP Punct Economic news had little effect on Adj Noun Punct Economic news had little effect on financial Noun Punct Economic news had little effect on financial markets Punct Economic news had little effect on financial markets .

Derivations

Natural Language Processing 8(11) Economic news had little effect on NP Punct Economic news had little effect on Adj Noun Punct Economic news had little effect on financial Noun Punct Economic news had little effect on financial markets Punct Economic news had little effect on financial markets .

Derivations

Natural Language Processing 8(11) Economic news had little effect on Adj Noun Punct Economic news had little effect on financial Noun Punct Economic news had little effect on financial markets Punct Economic news had little effect on financial markets .

Derivations

Natural Language Processing 8(11) Economic news had little effect on financial Noun Punct Economic news had little effect on financial markets Punct Economic news had little effect on financial markets .

Derivations

Natural Language Processing 8(11) Economic news had little effect on financial markets Punct Economic news had little effect on financial markets .

Derivations

Natural Language Processing 8(11) Economic news had little eﬀect on ﬁnancial markets .

Derivations

Natural Language Processing 8(11) Derivations

S NP VP Punct Adj Noun VP Punct Economic Noun VP Punct Economic news VP Punct Economic news Verb NP Punct Economic news had NP Punct Economic news had NP PP Punct Economic news had Adj Noun PP Punct Economic news had little Noun PP Punct Economic news had little effect PP Punct Economic news had little effect Prep NP Punct Economic news had little effect on NP Punct Economic news had little effect on Adj Noun Punct Economic news had little effect on financial Noun Punct Economic news had little effect on financial markets Punct Economic news had little effect on financial markets .

Natural Language Processing 8(11) Phrase Structure Trees

S NP VP Punct Adj Noun VP Punct Economic Noun VP Punct Economic news VP Punct Economic news Verb NP Punct Economic news had NP Punct Economic news had NP PP Punct Economic news had Adj Noun PP Punct Economic news had little Noun PP Punct Economic news had little effect PP Punct Economic news had little effect Prep NP Punct Economic news had little effect on NP Punct S Economic news had little effect on Adj Noun Punct ✑◗ Economic news had little effect on financial Noun Punct ✑ ◗ Economic news had little effect on financial markets Punct ✑ ◗ Economic news had little effect on financial markets . ✑ ◗ ✑ VP ◗ ✑ ❍❍ ◗ ✑ NP ◗ ✑ ✧❍ ◗ ✑ ✧ ❍ ◗ ✑ ✧ ◗ ✑ ✧ PP ◗ ✑ ✧ ❍❍ ◗ NP NP NP Punct ✟✟❍❍ ✟✟❍❍ ✟✟❍❍ Adj Noun Verb Adj Noun Prep Adj Noun

Economic news had little eﬀect on ﬁnancial markets .

Natural Language Processing 9(11) Treebanks and Grammars

! Constituency treebanks ! Treebanks with constituency-based annotation ! Example: Penn Treebank of English ! Treebank grammars ! We can extract CFGs from constituency treebanks ! Treebank grammars can be used to build syntactic parsers

Natural Language Processing 10(11) Quiz

The big bear scared the little dog.

! True or false 1. The substring dog is a noun phrase 2. The substring little dog is a noun phrase 3. The substring the little dog is a noun phrase 4. The substring scared the little dog is a noun phrase 5. The substring scared the little dog is a verb phrase

Natural Language Processing 11(11) Natural Language Processing Universal Dependencies

Joakim Nivre

Uppsala University Department of Linguistics and Philology joakim.nivre@lingﬁl.uu.se

Natural Language Processing 1(13) Universal Dependencies (UD)

! Framework for multilingual grammatical annotation ! Morphological layer: ! Part-of-speech tags ! Morphological features ! Lemmas ! Syntactic layer: ! Dependency tree

Natural Language Processing 2(13) nsubj obj

Snoopy hugged Woodstock

obj nsubj iobj

Snoopy gave Woodstock food

Predicates and Arguments nsubj obj iobj

nsubj

Snoopy barked

Natural Language Processing 3(13) obj nsubj iobj

Snoopy gave Woodstock food

Predicates and Arguments nsubj obj iobj

nsubj nsubj obj

Snoopy barked Snoopy hugged Woodstock

Natural Language Processing 3(13) Predicates and Arguments nsubj obj iobj

nsubj nsubj obj

Snoopy barked Snoopy hugged Woodstock

obj nsubj iobj

Snoopy gave Woodstock food

Natural Language Processing 3(13) obl nsubj obj case

Snoopy hugged Woodstock in Paris

obl nsubj obj case

Snoopy gave food to Woodstock

Predicates and Modiﬁers advmod obl

nsubj advmod

Snoopy barked angrily

Natural Language Processing 4(13) obl nsubj obj case

Snoopy gave food to Woodstock

Predicates and Modiﬁers advmod obl

obl nsubj advmod nsubj obj case

Snoopy barked angrily Snoopy hugged Woodstock in Paris

Natural Language Processing 4(13) Predicates and Modiﬁers advmod obl

obl nsubj advmod nsubj obj case

Snoopy barked angrily Snoopy hugged Woodstock in Paris

obl nsubj obj case

Snoopy gave food to Woodstock

Natural Language Processing 4(13) nmod case

coﬀee with cream

nmod amod case

black coﬀee with cream

Noun Phrases amod nmod

amod

black coﬀee

Natural Language Processing 5(13) nmod amod case

black coﬀee with cream

Noun Phrases amod nmod

nmod amod case

black coﬀee coﬀee with cream

Natural Language Processing 5(13) Noun Phrases amod nmod

nmod amod case

black coﬀee coﬀee with cream

nmod amod case

black coﬀee with cream

Natural Language Processing 5(13) Quiz

Snoopy likes tasty bagels

! Which words are dependents of likes? 1. Snoopy 2. likes 3. tasty 4. bagels

Natural Language Processing 6(13) Quiz

Snoopy likes tasty bagels

! Which dependency relation does bagels have to its head? 1. nsubj 2. obj 3. obl 4. nmod

Natural Language Processing 7(13) nsubj aux obj

Snoopy will hug Woodstock

nsubj obl aux case aux det

Snoopy could have slept on the roof

Function Words aux case det

obj nsubj det

Snoopy hugged the bird

Natural Language Processing 8(13) nsubj obl aux case aux det

Snoopy could have slept on the roof

Function Words aux case det

obj nsubj nsubj det aux obj

Snoopy hugged the bird Snoopy will hug Woodstock

Natural Language Processing 8(13) Function Words aux case det

obj nsubj nsubj det aux obj

Snoopy hugged the bird Snoopy will hug Woodstock

nsubj obl aux case aux det

Snoopy could have slept on the roof

Natural Language Processing 8(13) xcomp nsubj mark obj

Snoopy promised to hug Woodstock

Subordinate Clauses ccomp xcomp mark ccomp mark nsubj nsubj aux obj

Snoopy promised that he would hug Woodstock

Natural Language Processing 9(13) Subordinate Clauses ccomp xcomp mark ccomp mark nsubj nsubj aux obj

Snoopy promised that he would hug Woodstock

xcomp nsubj mark obj

Snoopy promised to hug Woodstock

Natural Language Processing 9(13) acl acl obj det nsubj obj det nsubj

the bird who hugged Snoopy the bird who Snoopy hugged

Adverbial and Adnominal Clauses advcl acl advcl mark nsubj obj nsubj obj

Snoopy hugs Woodstock if Lucy hugs Linus

Natural Language Processing 10(13) acl obj det nsubj

the bird who Snoopy hugged

Adverbial and Adnominal Clauses advcl acl advcl mark nsubj obj nsubj obj

Snoopy hugs Woodstock if Lucy hugs Linus

acl det nsubj obj

the bird who hugged Snoopy

Natural Language Processing 10(13) Adverbial and Adnominal Clauses advcl acl advcl mark nsubj obj nsubj obj

Snoopy hugs Woodstock if Lucy hugs Linus

acl acl obj det nsubj obj det nsubj

the bird who hugged Snoopy the bird who Snoopy hugged

Natural Language Processing 10(13) Adverbial and Adnominal Clauses advcl acl advcl mark nsubj obj nsubj obj

Snoopy hugs Woodstock if Lucy hugs Linus

acl:relcl acl:relcl obj det nsubj obj det nsubj

the bird who hugged Snoopy the bird who Snoopy hugged

Natural Language Processing 10(13) punct

nsubj obj ﬂat compound

Other Relations ﬂat compound punct

nsubj obj

Charlie Brown ate a tuna bagel .

Natural Language Processing 11(13) punct

obj compound

Other Relations ﬂat compound punct

nsubj ﬂat

Charlie Brown ate a tuna bagel .

Natural Language Processing 11(13) punct

Other Relations ﬂat compound punct

nsubj obj ﬂat compound

Charlie Brown ate a tuna bagel .

Natural Language Processing 11(13) Other Relations ﬂat compound punct

punct

nsubj obj ﬂat compound

Charlie Brown ate a tuna bagel .

Natural Language Processing 11(13) Quiz

Snoopy could have tried to hug Woodstock

! Which verb is the root of the dependency tree? 1. could 2. have 3. tried 4. hug

Natural Language Processing 12(13) Quiz

Snoopy could have tried to hug Woodstock

! Which verbs have the dependency relation aux to its head? 1. could 2. have 3. tried 4. hug

Natural Language Processing 13(13)