Semantics: Roles and Relations Prof. Sameer Singh CS 295: STATISTICAL NLP WINTER 2017 February 14, 2017 Based on slides from Jan Jurafsky, Noah Smith, Nathan Schneider, and everyone else they copied from.
Outline Structured Perceptron Word Senses Semantic Roles CS 295: STATISTICAL NLP (WINTER 2017) 2
Outline Structured Perceptron Word Senses Semantic Roles CS 295: STATISTICAL NLP (WINTER 2017) 3
Structured Prediction CS 295: STATISTICAL NLP (WINTER 2017) 4
Likelihood Learning CS 295: STATISTICAL NLP (WINTER 2017) 5
Perceptron Algorithm CS 295: STATISTICAL NLP (WINTER 2017) 6
Structured Perceptron CS 295: STATISTICAL NLP (WINTER 2017) 7
Structured Hinge Loss CS 295: STATISTICAL NLP (WINTER 2017) 8
Weight Averaging CS 295: STATISTICAL NLP (WINTER 2017) 9
Outline Structured Perceptron Word Senses Semantic Roles CS 295: STATISTICAL NLP (WINTER 2017) 10
Words and Senses Instead, a bank can hold the investments in a custodial account in the client’s name. But as agriculture burgeons on the east bank, the river will shrink even more. Senses • bank 1 : financial institution Each word can have many senses.. • bank 2 : sloping mound Most non-rare words in English do. CS 295: STATISTICAL NLP (WINTER 2017) 11
Homonymy Same form , completely different meanings… Homographs Homophones right 2 bank 2 write 1 bank 1 piece 2 bat 2 peace 1 bat 1 Information Retrieval Text to Speech Applications “bat care” “bass” (fish) or “bass” (guitar) • • Machine Translation Speech to Text Bat: murcielago or bate? “piece” or “peace” • • CS 295: STATISTICAL NLP (WINTER 2017) 12
Polysemy The bank was constructed in 1875 out of local brick. I withdrew the money from the bank. bank 3 bank 2 Same form , but very related meanings… Metronymy Systemic relationship between senses. Building Organization school, university, hospital Jane Austen wrote Emma Author Works of the Author I love Jane Austen! Plums have beautiful blossoms Tree Fruit I ate a preserved plum CS 295: STATISTICAL NLP (WINTER 2017) 13
Multiple senses or not? Which flights serve breakfast? Does Lufthansa serve Philadelphia? “Zeugma” Test Does Lufthansa serve breakfast and San Jose? Sounds weird, so there are multiple senses of “serve”. You are free to execute your laws, and your citizens, as you see fit. Riker, Star Trek: The Next Generation CS 295: STATISTICAL NLP (WINTER 2017) 14
How do we define the sense? Dictionary Define senses in relation to other senses! CS 295: STATISTICAL NLP (WINTER 2017) 15
Synonyms Substitute one for the other in any sentence. Perfect synonymy, doesn’t exist Many things define acceptability: politeness, slang, register, genre Substitute one for the other in most sentence. couch / sofa big / large automobile / car Synonymy is between sense, not words vomit / throw up water / H 2 0 CS 295: STATISTICAL NLP (WINTER 2017) 16
Antonyms Sense that are opposite with respect to one feature of meaning.. otherwise very similar! dark/light short/long fast/slow rise/fall hot/cold up/down in/out big/little Binary Opposition Reversives Or at opposite ends of a scale Opposite directions or change dark/light short/long fast/slow rise/fall up/down in/out hot/cold big/little CS 295: STATISTICAL NLP (WINTER 2017) 17
Hyponymy and Hypernymy Hyponyms / Subordinate One sense is a hyponym of another if the first sense is more specific, denoting a subclass of the other car is a hyponym of vehicle mango is a hyponym of fruit Hypernyms / Superordinate Conversely hypernym denotes one is a superclass of the other vehicle is a hypernym of car fruit is a hypernym of mango CS 295: STATISTICAL NLP (WINTER 2017) 18
WordNet Category Unique Strings Noun 117,798 Verb 11,529 Adjective 22,479 Adverb 4,481 CS 295: STATISTICAL NLP (WINTER 2017) 19
WordNet Hierarchy CS 295: STATISTICAL NLP (WINTER 2017) 20
Noun Relations CS 295: STATISTICAL NLP (WINTER 2017) 21
Verb Relations CS 295: STATISTICAL NLP (WINTER 2017) 22
Word Sense Disambiguation The bass line of the song is too weak. CS 295: STATISTICAL NLP (WINTER 2017) 23
Outline Structured Perceptron Word Senses Semantic Roles CS 295: STATISTICAL NLP (WINTER 2017) 24
Meaning is Subtle I’m thrilled to visit sunny California. I’m thrilled to visit California, where the weather is sunny. I’m thrilled to visit California, where it’s sunny. I’m excited to visit California, where it’s sunny. I’m excited to visit California, where it’s sunny out. I’m excited to spend time in California, where it’s sunny out. I’m not excited to visit sunny California. I’m thrilled to visit sunny Florida. I’m thrilled to visit sunny Mountain View. I’m thrilled to visit California because it’s sunny. I’m sort of happy about the California visit. CS 295: STATISTICAL NLP (WINTER 2017) 25
Verbs are key! CS 295: STATISTICAL NLP (WINTER 2017) 26
Syntax ≠ Semantics CS 295: STATISTICAL NLP (WINTER 2017) 27
Need for “Roles” The police officer detained the subject at the scene of the crime. Who? The police officer Did what? detained To whom? The subject Where? at the scene of the crime When? - CS 295: STATISTICAL NLP (WINTER 2017) 28
Thematic Roles The waiter spilled the soup. Agent Experiencer John has a headache. The wind blows debris into our yard. Content Force Jesse broke the window. The city built a regulation-sized baseball diamond. Instrument Theme Mona asked, “You met Mary Ann at the supermarket?” He poached catfish, stunning them with a shocking device. Result Source I flew in from Boston. Ann Callahan makes hotel reservations for her boss. Beneficiary Goal I drove to Portland. CS 295: STATISTICAL NLP (WINTER 2017) 29
Problem with Thematic Roles Difficult to have a good set of roles that works all the time, where each role can have a small, concrete definition 47 high-level classes, divided into 193 more specific classes - Levin (1993), VerbNet Fewer Roles More Roles PropBank FrameNet “Proto”-arguments, shared across verbs Each verb sense is part of a “frame” Exact definition depends on verb sense Each frame has its own arguments CS 295: STATISTICAL NLP (WINTER 2017) 30
Prop Bank • “Frames” are verb senses • Arguments of each verb are mapped onto Arg0, Arg1, Arg2 • Arguments are always constituents (annotated over syntax) fall.08 (fall back on) fall.01 (move downward) fall.10 (fall for a trick) CS 295: STATISTICAL NLP (WINTER 2017) 31
FrameNet • “Frames” can be any content word (~1000 frames) • Each frame has its own argument roles, everything is hierarchical • Annotated without syntax, arguments can be anything Relations between Frame Frames Verb Roles / Senses Arguments CS 295: STATISTICAL NLP (WINTER 2017) 32
“Change position on a scale” VERBS: dwindle move soar escalation shift advance edge mushroom swell explosion tumble climb explode plummet swing fall decline fall reach triple fluctuation ADVERBS: decrease fluctuate rise tumble gain increasingly diminish gain rocket growth dip grow shift NOUNS: hike double increase skyrocket decline increase drop jump slide decrease rise CS 295: STATISTICAL NLP (WINTER 2017) 33
“Change position on a scale” Core Roles A TTRIBUTE The A TTRIBUTE is a scalar property that the I TEM possesses. D IFFERENCE The distance by which an I TEM changes its position on the scale. F INAL STATE A description that presents the I TEM ’s state after the change in the A TTRIBUTE ’s value as an independent predication. F INAL VALUE The position on the scale where the I TEM ends up. I NITIAL STATE A description that presents the I TEM ’s state before the change in the A T - TRIBUTE ’s value as an independent predication. I NITIAL VALUE The initial position on the scale from which the I TEM moves away. I TEM The entity that has a position on the scale. V ALUE RANGE A portion of the scale, typically identified by its end points, along which the values of the A TTRIBUTE fluctuate. Some Non-Core Roles D URATION The length of time over which the change takes place. S PEED The rate of change of the V ALUE . G ROUP The G ROUP in which an I TEM changes the value of an A TTRIBUTE in a specified way. Figure 22.3 The frame elements in the change position on a scale frame from the FrameNet Labelers CS 295: STATISTICAL NLP (WINTER 2017) 34
“Change position on a scale” (22.20) [ I TEM Oil] rose [ A TTRIBUTE in price] [ D IFFERENCE by 2%]. (22.21) [ I TEM It] has increased [ F INAL STATE to having them 1 day a month]. (22.22) [ I TEM Microsoft shares] fell [ F INAL VALUE to 7 5/8]. (22.23) [ I TEM Colon cancer incidence] fell [ D IFFERENCE by 50%] [ G ROUP among men]. (22.24) a steady increase [ I NITIAL VALUE from 9.5] [ F INAL VALUE to 14.3] [ I TEM in dividends] (22.25) a [ D IFFERENCE 5%] [ I TEM dividend] increase ... CS 295: STATISTICAL NLP (WINTER 2017) 35
Relations between Frames Inherits from: Is Inherited by: event Perspective on: Is Perspectivized in: Uses: Is Used by: Subframe of: change_position_on_scale Has Subframe(s): Precedes: Is Preceded by: Is Inchoative of: change_of_temperature proliferating_in_number Is Causative of: CS 295: STATISTICAL NLP (WINTER 2017) 36
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