PSY 369: Psycholinguistics Language Comprehension: Sentence comprehension

Center embedded structures The house burned down.

Center embedded structures The house burned down. The house the handyman painted burned down.

Center embedded structures The house burned down. The house the handyman painted burned down. This one may be legal, but that doesn’t mean that it is (easily) comprehensible ( the handyman that the teacher hired painted the house that burned down ) The house the handyman the teacher hired painted burned down.

S NP Ndet Themanhit dog withtheleash.the Theman

S NPVP V Ndet Themanhit dog withtheleash.the Themanhit

S NPVP VNP NdetN Themanhit dog withtheleash.the Themanhitdogthe

S NPVP VNP NdetN Themanhit dog withtheleash.the Themanhitdog PP withtheleash the Modifier

S NPVP VNP NdetN Themanhit dog withtheleash.the Themanhitdog PP withtheleash the Instrument

Themanhit dog withtheleash.the How do we know which structure to build?

Parsing The syntactic analyser or “parser” Main task: To construct a syntactic structure from the words of the sentence as they arrive

Different approaches Serial Analysis (Modular): Build just one based on syntactic information and continue to try to add to it as long as this is still possible Interactive Analysis: Use multiple levels (both syntax and semantics) of information to build the “best” structure Parallel Analysis: Build both alternative structures at the same time Minimal Commitment: Stop building - and wait until later material clarifies which analysis is the correct one.

Sentence Comprehension Modular

Sentence Comprehension Modular Interactive models

Sentence Comprehension Garden path sentences A garden path sentence invites the listener to consider one possible parse, and then at the end forces him to abandon this parse in favor of another.

Sentence Comprehension Garden path sentences The horse raced past the barn fell. VP S NP The horse

Sentence Comprehension Garden path sentences The horse raced past the barn fell. VP V raced S NP The horse

Sentence Comprehension Garden path sentences The horse raced past the barn fell. VP VPP PNP racedpast S NP The horse

Sentence Comprehension Garden path sentences The horse raced past the barn fell. VP VPP PNP racedpastthe barn S NP The horse

Sentence Comprehension Garden path sentences The horse raced past the barn fell. VP VPP PNP racedpastthe barn S NP The horse fell

Sentence Comprehension Garden path sentences The horse raced past the barn fell. VP VPP PNP racedpastthe barn S NP The horse raced is initially treated as a past tense verb

Sentence Comprehension Garden path sentences The horse raced past the barn fell. VP VPP PNP racedpastthe barn S NP The horse fell raced is initially treated as a past tense verb This analysis fails when the verb fell is encountered

Sentence Comprehension Garden path sentences The horse raced past the barn fell. VP VPP PNP racedpastthe barn S NP The horse fell raced is initially treated as a past tense verb This analysis fails when the verb fell is encountered raced can be re-analyzed as a past participle. VP V raced PP PNP pastthe barn S NP The horsefell NPRR V

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A serial model Formulated by Lyn Frazier (1978, 1987) Build trees using syntactic cues: phrase structure rules plus two parsing principles Minimal Attachment Late Closure

A serial model Minimal Attachment Prefer the interpretation that is accompanied by the simplest structure. simplest = fewest branchings (tree metaphor!) Count the number of nodes = branching points Marcie kissed Ernie and his brother… The girl hit the man with the umbrella.

S NP the girl VP V hit NP the man PP P with NP the umbrella S NP the girl VP V hit NP the man PP P with NP the umbrella The girl hit the man with the umbrella. 8 Nodes 9 nodes Minimal attachment Preferred

Minimal attachment Garden path sentences The spy saw the cop with a telescope. minimal attach non- minimal attach Modular prediction Build this structure first Interactive prediction Build this structure first

Sentence Comprehension Garden path sentences The spy saw the cop with a revolver. minimal attach non- minimal attach Modular prediction Build this structure first Interactive prediction Build this structure first Lexical information rules this one out

MANon-MA S NP the spy VP V saw NP the cop PP P with NP the revolver S’ but the cop didn’t see him S NP the spy VP V saw NP the cop PP P with NP the revolver S’ but the cop didn’t see him The spy saw the cop with the binoculars.. The spy saw the cop with the revolver … (Rayner & Frazier, ‘83) <- takes longer to read

A serial model Late Closure Incorporate incoming material into the phrase or clause currently being processed. OR Associate incoming material with the most recent material possible. She said he tickled her yesterday Tom said that Bill had written his paper yesterday. They were cooking apples.

Parsing Preferences.. late closure She said he tickled her yesterday S np she vp v said S' np he vp v tickled np her adv yesterday S np she vp v said S' np he vp v tickled np her adv yesterday Preferred (Both have 10 nodes, so use LC not MA)

Interactive Models The evidence questioned in the trial … The person questioned in the trial … evidence typically gets questioned, but can’t do the questioning Other factors (e.g., semantic context, co- occurrence of usage & expectation) may provide cues about the likely interpretation of a sentence

Interactive Models Other factors (e.g., semantic context, co- occurrence of usage & expectation) may provide cues about the likely interpretation of a sentence The evidence questioned in the trial … The person questioned in the trial … A lawyer often asks questions (more often than answering them)

Semantic expectations Taraban & McCelland (1988) Expectation The couple admired the house with a friend but knew that it was over-priced. The couple admired the house with a garden but knew that it was over-priced. Other factors (e.g., semantic context, co- occurrence of usage & expectation) may provide cues about the likely interpretation of a sentence

The Non-MA structure may be favoured Semantic expectations Taraban & McCelland, 1988 The couple admired the house with a friend but knew that it was over- priced. The couple admired the house with a garden but knew that it was over- priced.

Intonation as a cue A: I’d like to fly to Davenport, Iowa on TWA. B: TWA doesn’t fly there... B1: They fly to Des Moines. B2: They fly to Des Moines. A1: I met Mary and Elena’s mother at the mall yesterday. A2: I met Mary and Elena’s mother at the mall yesterday.

Chunking, or “phrasing” A1: I met Mary and Elena’s mother at the mall yesterday. A2: I met Mary and Elena’s mother at the mall yesterday.

Phrasing can disambiguate I met Mary and Elena’s mother at the mall yesterday Mary & Elena’s mother mall One intonation phrase with relatively flat overall pitch range.

Phrasing can disambiguate I met Mary and Elena’s mother at the mall yesterday Mary mall Elena’s mother Separate phrases, with expanded pitch movements.

Summing up Is ambiguity resolution a problem in real life? Yes (Try to think of a sentence that isn’t partially ambiguous) Many factors might influence the process of making sense of a string of words. (e.g. syntax, semantics, context, intonation, co- occurrence of words, frequency of usage, …)