Computational Linguistics
Computational
Linguistics
Copyright © 2017 Graeme
Hirst, Suzanne Stevenson
and Gerald Penn. All rights
reserved.
7
7. Lexical semantics
Gerald Penn
Department of Computer Science, University of Toronto
CSC 2501 / 485
Fall 2018
Reading: Jurafsky & Martin: 19.1–4, 20.8; Bird et al: 2.5
2
• Word meanings and their internal structure.
• The structure of the relations among words
and meanings.
Lexical semantics
3
• Current focus in CL on lexical semantics:
• word senses;
• detailed lexical representations/vectors;
• organization of senses, or lexical entries more
generally (like a dictionary entry? Probably not).
Current CL research
4
Lexicon with entry for each word (or fixed phrase).
• Senses (meanings). For each:
• Surface form:
• Orthography, phonology, …
• Syntax:
• Part-of-speech, morphology, subcategorization,
…
• Behaviour, usage, …:
• Collocations, register and genre, …
Knowledge about words
5
• How are word senses defined?
• Grounded in world knowledge?
• Are they defined and fixed at all?
• Or wholly context-dependent? (See also slide 9)
• Constructional versus differential approaches.
Word senses
Sense is built from
elements of a set of
universal primitives
of meaning.
Sense is distinguished
from others by a set of
(ad hoc) differentia.
6
• Synonymy: Two (or more) words (synonyms)
having the same meaning.
• Homonymy, polysemy: Two (or more)
meanings having the same word (homonym,
polyseme).
• Lexical ambiguity
Relations between words and senses
What does this mean?
Lexical ambiguity: Homonymy
• Homonymy: meanings are unrelated.
[Etymology or history of word is not a deciding factor.]
• Due to same spelling (homography):
• bank for money, bank of river, bank of switches,
…bank → banque or bord or rangée or …?
bass: “bȧss” fish, “bāss” guitar;
bow: “bau” to the audience, tie a “bō”.
• Due to same sound (homophony):
• wood, would; weather, whether; you, ewe, yew;
bough, bow.
7
Lexical ambiguity: Polysemy 1
• Polysemy: meanings are related.
• run: of humans, rivers, buses, bus routes, …
line: of people, of type, drawn on paper, transit
route, …
• Often, no clear line between polysemy and
homonymy.
8
Lexical ambiguity: Polysemy 2
• Sense modulation by context:
• fast train, fast typist, fast road.
• Systematic polysemy or sense extension:
• bank as financial institution and as building;
window as hole in wall or what fits in hole;
bottle, book, DVD, Toyota, lamb, …
• Applies to most or all senses of certain semantic
classes.
9
14
• Hyponymy, hyperonymy: subtype,
supertype:
• sedan is a hyponym of car;
car is a hyperonym of sedan.
[hypo- = under; hyper- = over]
• The fundamental relation for creating a taxonomy:
a tree-like structure that expresses classes and
inheritance of properties.
[Terminology:
• is-a relation in ontologies of (language-independent) concepts;
• hyponymy relation in taxonomies of (language-dependent) senses.]
Relations between senses 1
15
• Meronymy, holonymy: part/whole, or
membership:
• leg is a meronym of chair;
chair is a holonym of leg and a meronym
of dining-set.
• Many subtypes of meronym relations.
Component-of: kitchen–apartment
Member-of: soldier–army
Portion-of: slice–pie
Relations between senses 2
Examples of meronymy from Roxana Girju, Adriana Badulescu, and Dan I. Moldovan, “Automatic discovery of part-whole relations”, Computational Linguistics, 32(1), 2006,
83–135, based on relations from Morton E. Winston, Roger Chaffin, and Douglas Herrmann, “A taxonomy of part-whole relations”, Cognitive Science, 11(4), 1987, 417–444.
16
• Entailment, implicature: various kinds:
• snore entails sleep;
manage implies try.
Relations between senses 3
18
• Problem: Need a complete lexicon for each
natural language.
• Dictionary as starting point? Limitations?
• Learner’s dictionary? Limitations?
• Text (corpus) as starting point? Limitations?
• Build by hand (lexicographers) or
automatically? Limitations?
Lexical acquisition 1
19
• Corpus-based pattern recognition methods.
• Accurate, representative information.
• Includes statistical information.
• Extraction from online dictionary.
• More knowledge-based.
• Can treat dictionary as highly specialized corpus.
Lexical acquisition 2
WordNet 1
• WordNet: A hierarchical (taxonomic) lexicon
and thesaurus of English.
• Developed by lexicographers at Princeton, 1990s
to present.
• Graph structure:
• Nodes are synsets (“synonym sets”) (≈ word
senses).
21
http://wordnetweb.princeton.edu/perl/webwn
http://wordnetweb.princeton.edu/perl/webwn
22
Noun slip
• faux pas#1, gaffe#1, solecism#1, slip#1, gaucherie#2 (a socially awkward
or tactless act)
• slip#2, slip-up#1, miscue#2, parapraxis#1 (a minor inadvertent mistake
usually observed in speech or writing or in small accidents or memory
lapses etc.)
• slip#3 (potter’s clay that is thinned and used for coating or decorating
ceramics)
• cutting#2, slip#4 (a part (sometimes a root or leaf or bud) removed from a
plant to propagate a new plant through rooting or grafting)
• slip#5 (a young and slender person) “he’s a mere slip of a lad”
• mooring#1, moorage#2, berth#2, slip#6 (a place where a craft can be
made fast)
• slip#7, trip#3 (an accidental misstep threatening (or causing) a fall) “he
blamed his slip on the ice”; “the jolt caused many slips and a few spills”
• slickness#3, slick#1, slipperiness#1, slip#8 (a slippery smoothness) “he
could feel the slickness of the tiller”
• strip#2, slip#9 (artifact consisting of a narrow flat piece of material)
• slip#10, slip of paper#1 (a small sheet of paper) “a receipt slip”
• chemise#1, shimmy#2, shift#9, slip#11, teddy#2 (a woman’s sleeveless
undergarment)
• …
Example
Synonyms for this sense
Gloss
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=faux+pas
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=gaffe
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=solecism
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=slip-up
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=miscue
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=parapraxis
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=cutting
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=mooring
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=moorage
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=berth
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=trip
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=slickness
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=slick
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=slipperiness
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=strip
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=slip+of+paper
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=chemise
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=shimmy
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=shift
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=teddy
23
Noun slip: Hyperonyms
• slip#10, slip of paper#1 (a small sheet of paper)
• sheet#2, piece of paper#1, sheet of paper#1 (paper used for writing or printing)
• paper#1 (a material made of cellulose pulp derived mainly from wood or rags or certain grasses)
• material#1, stuff#1 (the tangible substance that goes into the makeup of a physical object)
• substance#1 (the real physical matter of which a person or thing consists)
• matter#3 (that which has mass and occupies space)
• physical entity#1 (an entity that has physical existence)
• entity#1 (that which is perceived or known or inferred to have its own distinct existence (living or nonliving))
• part#1, portion#1, component part#1, component#2, constituent#3 (something determined in relation to something that includes it)
• relation#1 (an abstraction belonging to or characteristic of two entities or parts together)
• abstraction#6, abstract entity#1 (a general concept formed by extracting common features from specific examples)
• entity#1 (that which is perceived or known or inferred to have its own distinct existence (living or nonliving))
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=slip+of+paper
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=sheet
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=piece+of+paper
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=sheet+of+paper
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=paper
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=material
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=stuff
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=substance
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=matter
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=physical+entity
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=entity
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=part
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=portion
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=component+part
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=component
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=constituent
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=relation
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=abstraction
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=abstract+entity
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=entity
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Noun slip: Sister terms
• sheet#2, piece of paper#1, sheet of paper#1 (paper used for writing or printing)
• slip#10, slip of paper#1 (a small sheet of paper)
• signature#5 (a sheet with several pages printed on it; it folds to page size and is bound with other signatures to form a book)
• leaf#2, folio#2 (a sheet of any written or printed material (especially in a manuscript or book))
• tear sheet#1 (a sheet that can be easily torn out of a publication)
• foolscap#1 (a size of paper used especially in Britain)
• style sheet#1 (a sheet summarizing the editorial conventions to be followed in preparing text for publication)
• worksheet#1 (a sheet of paper with multiple columns; used by an accountant to assemble figures for financial statements)
• revenue stamp#1, stamp#6 (a small piece of adhesive paper that is put on an object to show that a government tax has been paid
• Sister terms belong to synsets
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=sheet
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=piece+of+paper
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=sheet+of+paper
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=slip+of+paper
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=signature
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=leaf
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=folio
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=tear+sheet
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=foolscap
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=style+sheet
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=worksheet
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=revenue+stamp
http://wordnet.princeton.edu/perl/webwn?o2=&o0=1&o7=1&o5=&o1=1&o6=&o4=&o3=&s=stamp
25
Eight senses of board in WordNet,
and their hyperonyms and hyponyms
D
ia
g
ra
m
f
ro
m
E
ll
e
n
V
o
o
rh
e
e
s
1
9
9
8
WordNet 2
• Graph structure (cont.):
• Edges from hyponymy relations: near-tree.
• Edges from meronymy relations: network.
• Index maps each word to all of its synsets.
• Separate trees for nouns, verbs, adjectives,
adverbs (with derivational cross-connections).
• Differential approach to meaning:
• The hyponyms of a node are differentiations of its
meaning.
26
WordNet 3
• WordNets now available or under
construction for many languages.
Afrikaans, Albanian, Arabic, Bantu, Basque, Bengali, Bulgarian,
Catalan, Chinese, Croatian, Czech, Danish, Dutch, English,
Estonian, Farsi (Persian), Finnish, French, German, Greek,
Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Italian, Irish,
Japanese, Kannada, Korean, Latin, Latvian, Macedonian, Maltese,
Marathi, Moldavian, Mongolian, Myanmar, Nepali, Norwegian,
Oriya, Polish, Portuguese, Romanian, Russian, Sanskrit, Serbian,
Slovenian, Spanish, Swedish, Tamil, Thai, Turkish, Vietnamese
www.globalwordnet.org, July 2013
27
Building and updating WordNets
• Problem: Need a complete lexicon and lexical
relations for each natural language.
• Dictionary as starting point? Limitations?
• Another WordNet as starting point? Limitations?
• Build by hand (lexicographers) or
automatically? Limitations?
• Text (corpus) as starting point? Limitations?
29
Hearst Discovering lexical relations 1
• Corpus-based method.
• Makes “suggestions” for lexicographers.
• Scan partially-parsed text looking for
instances of patterns:
“such NP1 as {NPi}* {or|and} NPi”
→ NP1 is a hyperonym of the NPi
30
Hearst, Marti. Automated discovery of WordNet relations. In: Fellbaum, Christane (editor), WordNet:
An electronic lexical database, The MIT Press, 1998, pages 131–151.
32
Hearst Discovering lexical relations 2
• Develop patterns
• “by hand”, or
• by scanning for sentences containing known related
pairs.
34
Hearst Results (good)
35
1. Some relations already in WordNet:
• fabric–silk, grain–barley, disorders–epilepsy, …
2. Some relations not already in WordNet (but
the words were):
• crops–milo, perishables–fruit, conditions–epilespy,
…
3. Some relations with words not yet in
WordNet:
• companies–Shell, institutions–Tufts, …
Hearst Results (less good)
36
4. Some too-general relations:
• things–exercise, topics–nutrition, areas–
Sacremento
5. Some too-context-specific relations:
• others–Meadowbrook, classics–Gaslight,
categories–drama, …
6. Some really bad relations (usually due to
parsing errors, not detecting full NP):
• children–Headstart, jobs–computer, companies–
sports
• Problems:
• Which word is the hyperonym?
A bearing is a structure that supports a rotating part
of a machine, such as a shaft, axle, spindle, or
wheel.
• Can’t find good patterns for meronyms.
• How to evaluate method quantitatively?
Hearst Limitations
37
Since Hearst’s paper 1
• Methods that use syntactic (not just lexical)
patterns, and which derive the patterns from
corpora.
• Methods that use senses, not words.
• Methods for finding coordinate (sister) terms
by distributional similarity in text.
• Methods that combine the evidence from all
of these to identify additional hyponym
relations.
• SISTER(X,Y) ∧ HYPONYM (Y,Z) ⇒ HYPONYM (X,Z)
38
Since Hearst’s paper 2
• Methods for meronymic relations.
• Each subtype tends to have its own indicators.
• These tend to have much more ambiguous
patterns than hyponymy.
• Complex methods for learning additional semantic
constraints on the patterns.
39
Since Hearst’s paper 3
• Methods for causal relations.
• Look esp for verbs such as give rise to, induce,
generate, cause, …
• “Learning ontologies from text” as important
research topic.
• “Learning commonsense knowledge from
text” as new research topic.
• Lots of interest right now in temporal
information (e.g., learning a timeline of events
described in a news story).
40
41
• Subcategorization of verbs:
• VPs can include more than one NP, can include
clauses of various types.
• Can classify verbs by kinds of VPs they permit.
• Thematic roles of a verb — some common
mappings:
Subject ≈ Agent / Experiencer
Object ≈ Theme
Object of preposition ≈ Goal / Location/
Recipient / Instrument
Properties of verbs Revision
42
Verbs are more complex than nouns.
• They are predicates that encode relations
between their arguments.
• They place selectional restrictions on their
arguments.
• E.g., agent of eat must be animate; theme must be
physical, edible.
• Different senses of verb may impose different
selectional restrictions.
• So argument types may disambiguate verb-sense.
• There are numerous subregularities in how senses
cluster together, in fact.
Lexical semantics of verbs 1
43
• Their taxonomy is more difficult to determine.
• Grouping is not as intuitively clear.
• Differentiating sister nodes is more complex.
Lexical semantics of verbs 2
44
WordNet for verbs is not very useful.
• Only shallow hierarchy of troponymy and
hyperonymy.
• e.g., to saunter is to walk in a certain manner.
• Insufficient information about thematic roles,
selectional restrictions, and subcategorization.
• No information about regularity in behaviour of
classes of verbs.
Lexical semantics of verbs 3
45
Verb
• S: (v) spray (be discharged in sprays of liquid) “Water sprayed all over the
floor”
• S: (v) spray (scatter in a mass or jet of droplets) “spray water on someone”;
“spray paint on the wall”
• S: (v) spray (cover by spraying with a liquid) “spray the wall with paint”
Verb
• S: (v) spray (be discharged in sprays of liquid) “Water sprayed all over the
floor”
◦ direct hyperonym / inherited hyperonym / sister term
• S: (v) scatter, sprinkle, dot, dust, disperse (distribute loosely) “He
scattered gun powder under the wagon”
• S: (v) discharge (pour forth or release) “discharge liquids”
• S: (v) spread, distribute (distribute or disperse widely) “The
invaders spread their language all over the country”
◦ derivationally related form
◦ sentence frame
• Something —-s
• Something is —-ing PP
http://wordnetweb.princeton.edu/perl/webwn?o2=&o0=1&o7=&o5=&o1=1&o6=&o4=&o3=&s=spray&i=6&h=000000000#c
http://wordnetweb.princeton.edu/perl/webwn?o2=&o0=1&o7=&o5=&o1=1&o6=&o4=&o3=&s=spray&i=7&h=000000000#c
http://wordnetweb.princeton.edu/perl/webwn?o2=&o0=1&o7=&o5=&o1=1&o6=&o4=&o3=&s=spray&i=8&h=000000000#c
http://wordnetweb.princeton.edu/perl/webwn?o2=&o0=1&o7=&o5=&o1=1&o6=&o4=&o3=&s=spray&h=000000000&j=6#c
http://wordnetweb.princeton.edu/perl/webwn?o2=&o0=1&o7=&o5=&o1=1&o6=&o4=&o3=&s=spray&h=00000010010000&j=7#c
http://wordnetweb.princeton.edu/perl/webwn?o2=&o0=1&o7=&o5=&o1=1&o6=&o4=&o3=&r=1&s=spray&i=7&h=0000001112312302222010000#c
http://wordnetweb.princeton.edu/perl/webwn?o2=&o0=1&o7=&o5=&o1=1&o6=&o4=&o3=&r=2&s=spray&i=7&h=0000001112312302222010000#c
http://wordnetweb.princeton.edu/perl/webwn?o2=&o0=1&o7=&o5=&o1=1&o6=&o4=&o3=&s=spray&i=8&h=0000001112312302222010000#c
http://wordnetweb.princeton.edu/perl/webwn?o2=&o0=1&o7=&o5=&o1=1&o6=&o4=&o3=&s=scatter
http://wordnetweb.princeton.edu/perl/webwn?o2=&o0=1&o7=&o5=&o1=1&o6=&o4=&o3=&s=sprinkle
http://wordnetweb.princeton.edu/perl/webwn?o2=&o0=1&o7=&o5=&o1=1&o6=&o4=&o3=&s=dot
http://wordnetweb.princeton.edu/perl/webwn?o2=&o0=1&o7=&o5=&o1=1&o6=&o4=&o3=&s=dust
http://wordnetweb.princeton.edu/perl/webwn?o2=&o0=1&o7=&o5=&o1=1&o6=&o4=&o3=&s=disperse
http://wordnetweb.princeton.edu/perl/webwn?o2=&o0=1&o7=&o5=&o1=1&o6=&o4=&o3=&s=spray&i=11&h=0000001112312302222010000#c
http://wordnetweb.princeton.edu/perl/webwn?o2=&o0=1&o7=&o5=&o1=1&o6=&o4=&o3=&s=discharge
http://wordnetweb.princeton.edu/perl/webwn?o2=&o0=1&o7=&o5=&o1=1&o6=&o4=&o3=&s=spray&i=14&h=0000001112312302222010000#c
http://wordnetweb.princeton.edu/perl/webwn?o2=&o0=1&o7=&o5=&o1=1&o6=&o4=&o3=&s=spread
http://wordnetweb.princeton.edu/perl/webwn?o2=&o0=1&o7=&o5=&o1=1&o6=&o4=&o3=&s=distribute
http://wordnetweb.princeton.edu/perl/webwn?o2=&o0=1&o7=&o5=&o1=1&o6=&o4=&o3=&s=spray&i=19&h=0000001112312302222010000#c
http://wordnetweb.princeton.edu/perl/webwn?o2=&o0=1&o7=&o5=&o1=1&o6=&o4=&o3=&s=spray&h=00000011123123022220000&j=20#c
46
• Groups (English) verbs by diathesis
alternations
— syntactic patterns of argument structure.
• May be subtle semantic differences between
alternations.
• Shows mapping between semantics of
verbs and their syntactic behaviour /
subcategorization.
Levin’s verb classification 1
Levin, Beth. English Verb Classes and Alternations. University of Chicago Press, 1993.
Palmer, Martha; Gildea, Daniel; Xue, Nianwen. Semantic Role Labeling. Synthesis Lectures on Human
Language Technologies #6, Morgan & Claypool, 2010. www.morganclaypool.com/toc/hlt/1/1
47
[Verb class 45.1]
break, crack, rip,…
Jay broke Bill’s finger.
*Jay broke Bill on the
finger.
Jay broke the vase.
Vases break easily.
[Verb class 20]
touch, stroke, tickle,
…
Kay touched Bill’s neck.
Kay touched Bill on the
neck.
Kay touched the cat.
*Cats touch easily.
Examples of verb class behaviour 1
• Motion/contact required for
body-part alternation.
• Change of state required for
middle construction.
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[Alternation 2.3.1]
The spray–load alternation
Nadia sprayed paint onto the wall.
Nadia sprayed the wall with paint.
Paint sprayed onto the wall.
*The wall sprayed with paint.
*Walls spray easily.
Example of diathesis alternation
Greater suggestion of
‘completeness’ of action
Other verbs that undergo this alternation:
brush, cram, crowd, dust, jam, load, scatter, splash, …
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• ~80 alternations, ~190 verb classes, ~3000
English verbs classified.
Subsequently extended by other researchers (Korhonen and
Briscoe 2004).
• Different senses of a verb may fall into
different classes.
• Used extensively in CL; basis for VerbNet.
Levin’s verb classification 2
Anna Korhonen and Ted Briscoe. Extended lexical-semantic classification of English verbs.
HLT–NAACL Workshop on Computational Lexical Semantics, Boston, 2004.
50
• Embeds Levin’s classes in a computational
lexicon.
• Adds thematic roles and semantics.
• Uses WordNet senses.
VerbNet
Karin Kipper, Hoa Trang Dang, Martha Palmer. Class-based construction of a verb lexicon. 17th National
Conference on Artificial Intelligence, 2000.
Karin Kipper Schuler. VerbNet: A Broad-Coverage Comprehensive Verb Lexicon. PhD thesis, University of
Pennsylvania, 2005.
http://verbs.colorado.edu/~mpalmer/papers/aaai.ps.gz
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Class Spray-9.7
http://verbs.colorado.edu/verb-
index/vn/spray-9.7.php
Thematic roles and
restrictions on them
Semantic form for the
kind of event E the
frame represents
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Class Spray-9.7
http://verbs.colorado.edu/verb-
index/vn/spray-9.7.php
Thematic roles and
restrictions on them
Semantic form for the
kind of event E the
frame represents
Unspecified
argument
Restriction on
preposition PREP
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Class Spray-9.7-1
WordNet and FrameNet
sense numbers
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Class Spray-9.7-1-1
Class Spray-9.7-2
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• Semantics-first classification of verbs (and
nouns).
• Frame: “A conceptual structure that describes
a particular type of situation, object, or event
along with its participants and props.”*
• Groups of predicates in same semantic class
share case frames.
• Includes both a lexicon and a corpus of anno-
tated sentences to illustrate predicate usage.
FrameNet
*Josef Ruppenhofer et al. FrameNet II: Extended theory and practice. June 2010.
Example
Frame APPLY-HEAT:
bake, barbecue, blanch, boil, braise, broil, …, poach,
roast, saute, scald, simmer, singe, steam, stew, toast
Nadia fried the sliced onions in a skillet.
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Cook Heating instrumentFood
Frame elements
Josef Ruppenhofer et al. FrameNet II: Extended theory and practice. June 2010.
Core elements
Container
Semantic Type Container
Cook
Semantic Type Sentient
Food
Semantic Type —
Heating_instrument
Semantic Type Physical_entity
Temperature_setting
Semantic Type Temperature
Non-core elements
Co_participant
Semantic Type —
Degree
Semantic Type Degree
Duration
Semantic Type Duration
Manner
Semantic Type Manner
Means
Semantic Type State_of_affairs
Medium
Semantic Type —
Place
Semantic Type Locative_relation
Purpose
Semantic Type State_of_affairs
Time
Semantic Type Time
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Frame elements of Apply_heat
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https://framenet.icsi.berkeley.edu/fndrupal/index.php?q=frameIndex
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Apply_heat
This frame differs from Cooking_creation in focusing on the process of handling
the ingredients, rather than the edible entity that results from the process.
Inherits From: Activity, Intentionally_affect
Is Inherited By: —
Is Used By: Cooking_creation
Is Causative of: Absorb_heat
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http://framenet.icsi.berkeley.edu/index.php?option=com_wrapper&Itemid=118&frame=Activity&source=frame&sourcevar=Apply_heat
http://framenet.icsi.berkeley.edu/index.php?option=com_wrapper&Itemid=118&frame=Intentionally_affect&source=frame&sourcevar=Apply_heat
http://framenet.icsi.berkeley.edu/index.php?option=com_wrapper&Itemid=118&frame=Cooking_creation&source=frame&sourcevar=Apply_heat
http://framenet.icsi.berkeley.edu/index.php?option=com_wrapper&Itemid=118&frame=Absorb_heat&source=frame&sourcevar=Apply_heat
https://framenet.icsi.berkeley.edu/fndrupal/index.php?q=frameIndex
59
Lexical entry for an Apply_heat word: bake
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INI = Indefinite
null instantiation
CNI = Constructional
null instantiation
Grammatical functions: Dependent, External argument, Object
https://framenet.icsi.berkeley.edu/fndrupal/index.php?q=frameIndex
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Lexical entry for an Apply_heat word: bake
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Valence patterns
https://framenet.icsi.berkeley.edu/fndrupal/index.php?q=frameIndex
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As capital of Europe’s most explosive economy, Dublin seems to be
changing before your very eyes.
Text with FrameNet annotations 1
Subscripts: Frames
Italics: Unannotated words
Yellow: Named entities
https://framenet.icsi.berkeley.edu/fndrupal/index.php?q=fulltextIndex
62
As capital of Europe’s most explosive economy, Dublin seems to be
changing before your very eyes.
Text with FrameNet annotations 2
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https://framenet.icsi.berkeley.edu/fndrupal/index.php?q=fulltextIndex
FrameNet in other languages
• FrameNets now available or under
construction for several other languages.
Brazilian Portuguese, Chinese, German, Japanese, Spanish,
Swedish
https://framenet.icsi.berkeley.edu/fndrupal/framenets_in_other_languages, June 2014
63
FrameNet vs VerbNet 1
Complementary resources:
• VerbNet:
• Groups by syntactic behaviour (Levin classes).
• Any resultant grouping by meaning is side-effect.
• FrameNet:
• Groups by meaning class (frame).
• Not limited to verbs.
• Any resultant grouping by syntactic behaviour is
side-effect.
64
FrameNet vs VerbNet 2
• Combine both with WordNet.
• Algorithmic methods to map VerbNet entries to
FrameNet entries and vice versa.
• Semi-automatic methods to map VerbNet
constraints into the WordNet hierarchy.
65
Lei Shi and Rada Mihalcea. “Putting pieces together: Combining FrameNet, VerbNet and WordNet for
robust semantic parsing.” 6th International Conference on Intelligent Text Processing and Computational
Linguistics (Springer Lecture Notes in Computer Science 3406), 2005, 100–111.