Semantic Roles & Labeling LING 571 Deep Processing in NLP - - PowerPoint PPT Presentation

Semantic Roles & Labeling

LING 571 — Deep Processing in NLP November 18, 2019
 Shane Steinert-Threlkeld

1

Announcements

• HW7: 89.4 average
• Only common mistake: similarity vs. distance

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sim(u, v) = 1 - distance(u, v)

Questions on HW #8

• For the mc_similarity portion
• You should use
• The numbers in the example_output are random. No meaning to them being < 1!
• For the WSD algorithm (mea culpa):
• The pseudocode is confusing so:

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wsim(w1, w2) = max

c1,c2 [simresnik (c1, c2)]

From Resnik (1999), eq. 2

Alternative Resnik WSD Pseudocode

Given: input word w0 and probe words {p1,…,pn} for pi in {p1,…,pn}: supported_sense = null most_information = 0.0 for sensew in SENSES(w0): for sensep in SENSES(pi): lcssynset = LOWESTCOMMONSUBSUMER(sensew, sensep) lcsinfo = INFORMATIONCONTENT(lcssynset) if lcsinfo > most_information: most_information = lcsinfo supported_sense = sensew

increment support[supported_sense] by most_information 4

Semantic Roles

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Semantic Analysis

• Full, deep compositional semantics
• Creates full logical form
• Links sentence meaning representation to logical world model representation
• Powerful, expressive, AI-complete
• Domain-specific slot-filling:
• Common in dialog systems, IE tasks
• Narrowly targeted to domain/task
• e.g. ORIGIN_LOC, DESTINATION_LOC, AIRLINE, …
• Often pattern-matching
• Low cost, but lacks generality, richness, etc

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Semantic Role Labeling

• Typically want to know
• Who did what to whom
• …where, when, and how
• Intermediate level:
• Shallower than full deep composition
• Abstracts away (somewhat) from surface form
• Captures general predicate-argument structure info
• Balance generality and specificity

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Examples

Yesterday Tom chased Jerry Yesterday Jerry was chased by Tom Tom chased Jerry yesterday Jerry was chased yesterday by Tom

• Semantic roles:
• Chaser: Tom
• ChasedThing: Jerry
• TimeOfChasing: yesterday
• Same across all sentence forms

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Full Event Semantics

• Neo-Davidsonian Style:
• ∃e Chasing(e) ∧ Chaser(e, Tom) ∧ ChasedThing(e, Jerry)

∧ TimeOfChasing(e, Yesterday)

• Same across all examples
• Roles: Chaser, ChasedThing, TimeOfChasing
• Specific to verb “chase”
• a.k.a. “Deep roles”

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Main Idea

• Extract the semantic roles without doing full semantic parsing
• Easier problem, but still useful for many tasks
• More data
• Better models

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Issues & Challenges

• How many roles for a language?
• Arbitrary!
• Each verb’s event structure determines sets of roles

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Issues & Challenges

• How can we acquire these roles?
• Manual construction?
• Some progress on automatic learning
• Mostly successful on limited domains (ATIS, GeoQuery)

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Issues & Challenges

• Can we capture generalities across verbs/events?
• Not really, each event/role is specific

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Thematic Roles

• Solution to instantiating a specific role for every verb
• Attempt to capture commonality between roles

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Thematic Roles

• Describe common semantic roles of verbal arguments
• e.g. subject of break is AGENT
• AGENT: volitional cause
• THEME: things affected by action
• Enables generalization over surface order of arguments
• JohnAGENT broke the windowTHEME
• The rockINSTRUMENT broke the windowTHEME
• The windowTHEME was broken by JohnAGENT

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Thematic Roles

• Verbs take different roles
• The break verb could be formed as:
• AGENT/Subject, THEME/Object (John broke the window)
• AGENT/Subject, THEME/Object, INSTRUMENT/PPwith (John broke the window with a rock)
• INSTRUMENT/Subject, THEME/Object (The rock broke the window)
• THEME/Subject (The window was broken)

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Thematic Roles

• Thematic grid, Θ-grid, case frame
• Set of thematic role arguments of verb
• subject: AGENT; Object: THEME, or
• subject: INSTR; Object:THEME
• Verb/Diathesis Alternations
• Verbs allow different surface realizations of roles
• DorisAGENT gave the bookTHEME to CarvGOAL
• DorisAGENT gave CarvGOAL the bookTHEME

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Canonical Roles

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Thematic Role Example AGENT The waiter spilled the soup EXPERIENCER John has a headache FORCE The wind blows debris from the mall into our yards. THEME Only after Benjamin Franklin broke the ice… RESULT The French government has built a regulation-size baseball diamond… CONTENT Mona asked “You met Mary Ann at a supermarket?” INSTRUMENT He turned to poaching catfish, stunning them with a shocking device… BENEFICIARY Whenever Ann Callahan makes hotel reservations for her boss… SOURCE I flew in from Boston. GOAL I drove to Portland.

Thematic Role Issues

• Hard to produce
• Standard set of roles
• Fragmentation: Often need to make more specific
• e.g. INSTRUMENTs can be subject or not
• Standard definition of roles
• Most AGENTs: animate, volitional, sentient, causal
• But not all… e.g.?

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From Levin and Rappaport Hovav 2005: a. John broke the window with a rock. b. The rock broke the window. a. Swabha ate the banana with a fork. b. * The fork ate the banana. [Google]Agent found the answer.

Thematic Role Issues

• Strategies:
• Generalized semantic roles: PROTO-AGENT/PROTO-PATIENT
• Defined heuristically: PropBank
• Define roles specific to verbs/nouns: FrameNet

20

PropBank

• Sentences annotated with semantic roles
• Penn and Chinese Treebank
• Roles specific to verb sense
• Numbered: Arg0, Arg1, Arg2, …
• Arg0: PROTO-AGENT; Arg1: PROTO-PATIENT, etc

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PropBank

• Arguments >1 are Verb-specific
• e.g. agree.01
• Arg0: Agreer
• Arg1: Proposition
• Arg2: Other entity agreeing
• Ex1: [Arg0 The group] agreed [Arg1 it wouldn’t make an offer]

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PropBank

• Resources:
• Annotated sentences
• Started w/Penn Treebank
• Now: Google answerbank, SMS, webtext, etc
• Framesets:
• Per-sense inventories of roles, examples
• Span verbs, adjectives, nouns (e.g. event nouns)

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PropBank

• propbank.github.io
• Recent status:
• 5940 verbs w/8121 framesets
• 1880 adjectives w/2210 framesets
• Continued into OntoNotes
• [CoNLL 2005 and 2012 shared tasks]

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AMR

• “Abstract Meaning Representation”
• Sentence-level semantic representation
• Nodes: Concepts
• English words; PropBank: predicates; or keywords

(‘person’)

• Edges: Relations
• PropBank thematic roles (ARG0-ARG5)
• Others including ‘location,’ ‘name,’ ‘time,’ etc…
• ~100 in total

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AMR 2

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see-01 i dog person run-02 name “Joe” garden

ARG0 ARG1 poss name

• p1

location ARG0-of

• AMR Bank: (now) ~40K annotated

sentences

• JAMR parser: 63% F-measure (2015)
• Alignments between word spans & graph

fragments

• Example: “I saw Joe’s dog, which was

running in the garden.”

From Liu et. al (2015)

AMR 3

• Towards full semantic parsing
• “Deeper” than base PropBank, but:
• No real quantification
• No articles
• No real vs. hypothetical events (e.g. “wants to go”)

27

FrameNet (Fillmore et al)

• Key insight:
• Commonalities not just across different sentences w/same verb but across different

verbs (and nouns and adjectives)

• PropBank
• [Arg0 Big Fruit Co.] increased [Arg1 the price of bananas].
• [Arg1 The price of bananas] was increased by [Arg0 BFCo].
• [Arg1 The price of bananas] increased [Arg2 5%].
• FrameNet
• [ATTRIBUTE The price] of [ITEM bananas] increased [DIFF 5%].
• [ATTRIBUTE The price] of [ITEM bananas] rose [DIFF 5%].
• There has been a [DIFF 5%] rise in [ATTRIBUTE the price] of [ITEM bananas].

28

FrameNet

• Semantic roles specific to frame
• Frame: script-like structure, roles (frame elements)
• e.g. CHANGE_POSITION_ON_SCALE: increase, rise
• ATTRIBUTE; INITIAL_VALUE; FINAL_VALUE
• Core, non-core roles
• Relationships between frames, frame elements
• Add causative: CAUSE_CHANGE_POSITION_ON_SCALE

29

Change of position on scale

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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

Core Roles

31

Core Roles

ATTRIBUTE The ATTRIBUTE is a scalar property that the ITEM possesses. DIFFERENCE The distance by which an ITEM changes its position on the scale. FINAL_STATE A description that presents the ITEM’s state after the change in the ATTRIBUTE’s value as an independent predication. FINAL_VALUE The position on the scale where the ITEM ends up. INITIAL_STATE A description that presents the ITEM’s state before the change in the ATTRIBUTE’s value as an independent predication. INITIAL_VALUE The initial position on the scale from which the ITEM moves away. ITEM The entity that has a position on the scale. VALUE_RANGE A portion of the scale, typically identified by its end points, along which the values of the ATTRIBUTE fluctuate.

Some Non-Core Roles

DURATION The length of time over which the change takes place. SPEED The rate of change of the VALUE. GROUP The GROUP in which an ITEM changes the value of an ATTRIBUTE in a specified way.

FrameNet

• Current status:
• 1224 frames
• 13669 lexical units (mostly verbs, nouns)
• 10749 frame element relations
• Annotations over:
• Newswire (WSJ, AQUAINT)
• American National Corpus
• Under active development
• Still relatively limited coverage

32

Semantic Role Labeling

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Semantic Role Labeling

• Task of automatically assigning semantic roles for each argument

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Typical Strategy

• Assign Parse to Input String
• Traverse parse to find all predicates
• For each predicate, examine each node and decide semantic role (if any)

35

Typical Strategy

function SEMANTICROLELABEL(words) returns labeled tree parse←PARSE(words) for each predicate in parse do for each node in parse do featurevector←EXTRACTFEATURES(node, predicate, parse) CLASSIFYNODE(node, featurevector, parse)

36

J&M 3rd ed, ch 20.6

Semantic Role Labeling Features

• Governing predicate
• Phrase Type (NP, VP, etc)
• Headword of constituent
• Headword POS
• PATH from current node to predicate (NP↑S↓VP↓VBD)
• …

37

Typical Strategy

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NP↑S ↓VP ↓VBD PATH(NP-SBJ)

Some Semantic Role Labeling Applications

• Question answering:
• Who did what to whom?
• Machine translation
• Maintain agents/thematic roles through translation
• Dialogue systems

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Scaling up SRL

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Neural SRL

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He et al 2017

Can be global or contextual [contextual tends to improve] No “detour” through syntactic parse

QA-SRL

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the paper

Editorial: should’ve been /casserole/

QA-SRL vs. PropBank

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QA-SRL

• Much more info, including live data explorer:
• http://qasrl.org/
• AI2 NLP Highlights podcast most recent episode ft. Luke Zettlemoyer:
• https://soundcloud.com/nlp-highlights/96-question-answering-as-an-annotation-

format-with-luke-zettlemoyer

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