Lexical Semantics in American Corpus Annotation Projects

1
Lexical Semantics in American Corpus Annotation
Projects

• Lori Levin
• September 10, 2004
• Tutorial at Clairvoyance Corporation

2
What is Lexical Semantics?

• Lexical semantics is about the meanings of words.
• This tutorial is about the meanings of verbs and
  their arguments
• Sam opened the door with a key.
• They key opened the door.
• The door was opened by Sam with a key.
• The door opened (with a key).
• Sam bought a book from Sue.
• Sue sold a book to Sam.

3
Types of semantics not covered in this tutorial

• Sentence-level meaning
• Truth conditions of sentences
• This is a picture of a cell phone. (true)
• This is a picture of a book. (false)
• Compositional semantics
• How the meanings of a noun phrase and a verb
  phrase are combined into the meaning of a
  sentence.
• Quantifier scope.
• Everyone here speaks two languages.

4
Aspects of lexical semantics not covered in this
tutorial

• Nouns, adjectives, adverbs, and prepositions
• Selectional restrictions
• Colorless green ideas sleep furiously.
• Chomsky, 1957, Syntactic Structures
• Count and mass nouns
• There was water all over the driveway. (mass)
• There was dog all over the driveway. (count)
• Synonymy, hyponymy, antonymy, etc.
• car-automobil
• car-vehicle
• Hot-cold

5
Outline

• Background
• Predicates and Arguments
• Valency and subcategories of verb
• Optional arguments and adjuncts
• Semantic Roles
• Three approaches to lexical semantics
• A linguistic theory
• Lexical Conceptual Structure
• A lexicon project
• Frame Semantics
• A corpus annotation project (also building a
  lexicon)
• PropBank
• A multi-lingual semantic corpus annotation project

6
Predicates and Arguments

• Verbs (and sometimes nouns and adjectives)
  describe events, states, and relations that have
  a certain number of participants.
• The children devoured the spaghetti.
• Two participants
• The teacher handed the book to the student.
• Three particpants.
• Problems exist.
• One participant.
• The participants are referred to as arguments of
  the verb. (Like arguments of a function.)

7
Valency and Subcategorization

• Fillmore and Kay, Lecture Notes, Chapter 4
• The children devoured the spaghetti.
• The children devoured.
• The children devoured the spaghetti the cheese.
• She handed the baby a toy.
• She handed the baby.
• She handed the toy.
• Problems exist.
• Problems exist more problems.

8
Grammaticality

• An asterisk () indicates that a sentence is
  ungrammatical.
• A large percentage of linguists make these
  assumptions
• Human languages are like formal languages.
• Some sentences are in the set of legal sentences
  and some are not
• A human can act like a machine that accepts legal
  sentences and rejects illegal sentences.

9
Valency

• The number of participants is called the verbs
  valence or valency.
• Devour has a valency of two.
• Hand has a valency of three.
• Exist has a valency of one.
• Linguists took this term from chemistry how
  many electrons are missing from the outer shell.
• The first linguist to use the term was Charles
  Hockett in the 1950s.

10
Subcategorization

• Verbs are divided into subcategories that have
  different valencies.
• Here is how the terminology works
• Exist, devour, and hand have different
  subcategorizations
• i.e., They are in different subcategories
• Devour subcategorizes for a subject and a direct
  object.
• Devour is subcategorized for a subject and a
  direct object.
• Devour takes two arguments, a subject and a
  direct object (or an agent and a patient).

11
Arguments are not always Noun Phrases

• The italicized phrases are also arguments
• He looked very pale.
• Adjective Phrase
• The solution turned red.
• Adjective Phrase
• I want to go.
• Verb Phrase
• He started singing a song.
• Verb Phrase
• We drove to New York.
• Prepositional Phrase

12
Optional and Obligatory Arguments

• The direct object of eat is optional
• The children ate.
• The children ate cake.
• The direct object of devour is not optional
• The children devoured.
• The children devoured the cake.

13
Optional Arguments

• The dog ran.
• The dog ran from the house to the creek through
  the garden along the path.

14
Optional vs. Invisible Arguments

• a. What happened to the cake?
• b. The children ate.
• b. The children ate it.
• In English, Sentences b and b do not mean the
  same thing in this context.
• Compare to Japanese and Chinese.

15
Adjuncts

• Locations, times, manners, and other things that
  can go with almost any sentences are called
  adjuncts.
• The children ate the cake quickly at 200 in the
  kitchen.

16
How to tell arguments from adjuncts

• There are some general guidelines that are not
  always conclusive.
• Adjuncts are always optional.
• but some arguments are optional too
• Repeatability
• The children devoured the cake at 200 on Monday.
• Two temporal adjuncts
• The children devoured the cake in Pittsburgh in a
  restaurant.
• Two locative adjuncts
• The children devoured the cake the dessert.
• arguments are not repeatable

17
Semantic Roles Motivation

• The verb open appears in different
  subcategorization patterns
• Sam opened the door with a key.
• The key opened the door.
• The door was opened by Sam with a key.
• Sams opening of the door with a key
• How can we represent the meanings of these
  sentences in a way that shows that they are
  related?

18
Semantic Roles Motivation

• These sentences do not have the same meaning even
  though they have the same verb
• Sam interviewed Sue.
• Sue interviewed Sam.

19
Semantic Roles Motivation

• These sentences mean roughly the same thing even
  though they use different verbs
• Sam bought a toy from Sue.
• Sue sold a toy to Sam.

20
Semantic Roles Motivation

• The way to express riding a vehicle to a location
  is different in different languages
• Sam took a bus to school.
• Sam ascended to the bus and went to school.
  (Hebrew)
• Sam riding on the bus, went to school.
  (Japanese)
• Sam sat on the bus, went to school. (Chinese)
• Sam went to school by bus.
• Sam went to school by taking a bus.

21
Semantic role names in a meaning representation

• Sam opened the door with a key.
• The key opened the door.
• The door was opened by Sam with a key.
• Sams opening of the door with a key
• Open
• Agent Sam
• Patient door
• Instrument key

22
Semantic Roles Names in a Meaning Representation

• These sentences do not have the same meaning
• Sam interviewed Sue.
• Sue interviewed Sam.

• Interview
• Agent Sam
• Patient Sue
• Interview
• Agent Sue
• Patient Sam

23
Examples of Semantic Roles

• Agent an agent acts volitionally or
  intentionally
• The students worked.
• Sue baked a cake.

24
Examples of Semantic Roles

• Experiencer and Perceived
• An experiencer is an animate being that perceives
  something, cognizes about something, or or
  experiences an emotion.
• The perceived is the thing that the experiencer
  perceives or the thing that caused the emotional
  response.
• The students like linguistics.
• (emoter and perceived)
• The students saw a linguist.
• (perceiver and perceived)
• Linguistics frightens the students.
• (emoter and perceived)
• The students thought about linguistics.
• (cognizer and perceived)

25
Examples of Semantic Roles

• Patient A patient is affected by an action.
• Sam kicked the ball.
• Sue cut the cake.
• Beneficiary A beneficiary benefits from an event
• Sue baked a cake for Sam.
• Sue baked Sam a cake.
• Malefactive Someone is affected adversely by an
  event.
• My dog died on me.
• Instrument
• The boy opened the door with a key.
• The key opened the door.
• Location
• The clock stands on the shelf.
• I put the book on the shelf.

26
Three approaches to semantic roles in meaning
representations

• Ray Jackendoff (1972, 1990) Linguistic
  Theory
• Lexical Conceptual Structure
• The Motion/Location Metaphor
• Semantic Roles
• Charles Fillmore, FrameNet Project
  Lexicon
• Frame-semantics
• Martha Palmer, PropBank Project Corpus
  Annotation
• Predicate-specific role names
• Proto-grammatical relations

27
Ray Jackendoff

• Semantic Interpretation in Generative Grammar,
  MIT Press, 1972
• Semantic Structures, MIT Press, 1990.
• Theory of human cognition
• Used by many computational linguists

28
Lexical Conceptual Structure

• Primitives
• GO, BE, STAY, CAUSE, and several more
• TO, FROM, AWAY, TOWARD, VIA, and several more
• Types of entities
• Event, State, Thing, Place, Path
• Other tiers of representation are added in order
  to capture nuances of meaning and grammar
• Cause and affectedness
• Manner
• Actor and undergoer (see discussion of PropBank)

29
Example of Lexical Conceptual Structure

• Sam threw the ball across the room.
• event
• CAUSE thing SAM
• event GO
• thing BALL
• path TO
• place
  AT
• 
  thing other-side-of-room

30
Lexical Conceptual Structure and Semantic Role
Names

• Sam threw the ball across the room.
• event
• CAUSE thing SAM agent
• event GO
• thing BALL
  theme
• path TO
• place
  AT
• 
  thing other-side-of-room
• 
  goal

31
The Motion/Location Metaphor

• J. S. Gruber, Studies in Lexical Relations, MIT
  Dissertation, 1965.
• Agent causes, manipulates, affects
• Theme changes location, is located somewhere, or
  exists
• Source the starting point of the motion
• Goal the ending point of the motion
• Path the path of the motion

32
Examples of Location and Directed Motion

• Many problems still exist.
• The clock sits on the shelf.
• The ball rolled from the door to the window along
  the wall.
• Same walked from his house to town along the
  river.
• Sue rolled across the room.
• The car turned into the driveway.

33
Being in a state or changing state

• The car is red.
• The ice cream melted.
• The glass broke.
• Sam broke the glass.
• The paper turned from red to green.
• The fairy godmother turned the pumpkin into a
  coach.

34
Having or Changing possession

• The teacher gave books to the students.
• The teacher gave the students books.
• The students have books.

35
Exchange of Information

• The teacher told a story to the students.
• The teacher told the students a story.

36
Extent

• The road extends/runs along the river from the
  school to the mall.
• The string reaches the wall.
• The string reaches across the room to the wall.

37
Strong points of LCS and the Motion/Location
Metaphor

• Sam manipulates a key, having an effect on the
  door, causing it to go from the state of being
  closed to the state of being open.
• Sam opened the door with a key.
• The key opened the door.
• The door was opened by Sam with a key.
• Sams opening of the door with a key

38
Strong points of LCS and the Motion/Location
Metaphor

• A toy goes from Sue to Sam. Some money goes from
  Sam to Sue.
• Differences in the causation tier.
• Sam bought a toy from Sue.
• Sue sold a toy to Sam.

39
Strong points of LCS and the Motion/Location
Metaphor

• Supports some inferences
• If X goes from A to B, then X is no longer at A.
• If X is created (begins to BE) during event Y,
  then X doesnt exist until Y is finished.

40
Strong or weak point?

• LCS wasnt designed with this kind of thing in
  mind, but it could be made to work.
• Sam took a bus to school.
• Sam ascended to the bus and went to school.
  (Hebrew)
• Sam riding on the bus, went to school.
  (Japanese)
• Sam sat on the bus, went to school. (Chinese)
• Sam went to school by bus.
• Sam went to school by taking a bus.

41
Problem with Thematic Roles and the
Motion/Location Metaphor

• It is not clear how to apply the metaphor to many
  verbs (Fillmore and Kay, Lecture Notes, pages
  4-22)
• He risked death.
• We resisted the enemy.
• She resembles her mother.

42
LCS Resources

• Bonnie Dorr, University of Maryland
• http//www.umiacs.umd.edu/bonnie/LCS_Database_Doc
  umentation.html
• LCS Lexicon for English
• English word senses are mapped to WordNet
• Handcrafted lexical entries for around 4000 verbs
• Automatically produced entries may be available
  for a full-sized lexicon
• LCS Dictionaries for other languages may be
  available
• May be handcrafted or produced partially
  automatically

43
Problem with Thematic Roles and the
Motion/Location Metaphor

• It is not clear how to apply the metaphor to many
  verbs (Fillmore and Kay, Lecture Notes, pages
  4-22)
• He risked death.
• We resisted the enemy.
• She resembles her mother.

44
Charles Fillmore, Collin Baker, and others
FrameNet Project

• http//www.icsi.berkeley.edu/framenet/
• Frame semantics
• Frames are networked using several relations
• Based on corpus analysis
• Lexical entries for around 7500 English verbs
• Other FrameNet projects in
• Spanish
• Japanese

45
Advantage of Frame Semantics

• FrameNet was designed to capture the similarities
  in sentences like these.
• Ride-vehicle frameSam took a bus to school.
• Sam ascended onto the bus and went to school.
  (Hebrew)
• Sam riding on the bus, went to school.
  (Japanese)
• Sam sat on the bus, went to school. (Chinese)
• Sam went to school by bus.
• Sam went to school by taking a bus.

46
Frame Semantics compared to the Motion/Location
Metaphor

• Frame Semantics has
• Many primitives
• Many semantic roles

47
FrameNet strong and weak points

• FrameNet is still under development and may
  change frequently.
• Versions are clearly identified.
• Lexical entries are very carefully hand crafted.

48
Martha Palmer and othersThe PropBank Project

• http//www.cis.upenn.edu/ace/
• Annotate the Penn TreeBank with
  predicate-argument information
• Corpus can be used for automatic learning of the
  surface realization of each argument

49
PropBank and FrameNet Close ties

• PropBank lexical entries are linked to FrameNet
  entries.
• There are more PropBank entries than FrameNet
  entries
• This paper contains some comparisons of PropBank
  and Framenet
• http//www.cis.upenn.edu/dgildea/gildea-acl02.pdf
  
• See also VerbNet
• http//www.cis.upenn.edu/group/verbnet/

50
Proto-roles and verb-specific roles

• http//www.cis.upenn.edu/dgildea/Verbs/
• Abandon
• Arg0abandoner
• Arg1thing abandoned, left behind
• Arg2attribute of arg1

51
PropBank multiple surface realizations of
arguments

• Sam opened the door with a key.
• The key opened the door.
• The door was opened by Sam with a key.
• Sams opening of the door with a key
• Arg0opener Sam
• Arg1thing opening door
• Arg2instrument key
• Arg3benefactive

52
PropBankHow are lexical entries used by
annotators?

• Intercoder agreement is a high priority for
  PropBank.
• Role names like agent and theme can be confusing.
• Verb-specific role names are more clear.

53
Annotation Procedure

• Identify the verb in a sentence.
• Look it up in the PropBank lexicon.
• Assign arg0arg-n appropriately by looking at the
  verb-specific roles.
• Always use the same arg-n for the same
  verb-specific role.

54
What are the arg-ns?

• The arg-n labels are arbitrary labels.
• However, PropBank tries to use them consistently
  across verbs.
• Arg0 tends to be an agent or the argument most
  likely to be the subject in active voice.
• Arg1 tends to be a theme or patient or the thing
  most likely to be
• The direct object of a transitive verb in active
  voice
• The subject of a verb in passive voice
• The subject of an intransitive verb

55
PropBank was not designed for this

• Sam took a bus to school.
• Sam ascended onto the bus and went to school.
  (Hebrew)
• Sam riding on the bus, went to school.
  (Japanese)
• Sam sat on the bus, went to school. (Chinese)
• Sam went to school by bus.
• Sam went to school by taking a bus.
• But it is linked to FrameNet

56
IAMTC (Interlingua Annotation of Multilingual
Text Corpora) Project

• http//aitc.aitcnet.org/nsf/iamtc/
• Collaboration
• New Mexico State University
• University of Maryland
• Columbia University
• MITRE
• Carnegie Mellon University
• ISI, University of Southern California

57
Goals of IAMTC

• Interlingua design
• Three levels of depth
• Annotation methodology
• manuals, tools, evaluations
• Annotated multi-parallel texts
• Foreign language original and multiple English
  translations
• Foreign languages Arabic, French, Hindi,
  Japanese, Korean, Spanish

58
Motivation for Corpus and Data

• Examine the surface realization of many phenomena
  
• In one language many surface realizations of the
  same phenomenon
• I think it is raining.
• It is probably raining.
• Across languages different syntactic
  constructions are used to express the same ideas

59
IL Development Staged, deepening

• IL0 simple dependency tree gives structure
• IL1 semantic annotations for Nouns, Verbs, Adjs,
  Advs, and Theta Roles
• Not yet semanticbuy?sell, many remaining
  simplifications
• Concept senses from ISIs Omega ontology
• Theta Roles from Dorrs LCS work
• Elaborate annotation manuals
• Tiamat annotation interface
• Post-annotation reconciliation process and
  interface
• Evaluation scores annotator agreement
• IL2 that comes next

60
Details of English IL0

• Deep syntactic dependency representation
• Removes auxiliary verbs, determiners, and some
  function words
• Normalizes passives, clefts, etc.
• Removes strongly governed prepositions
• Includes syntactic roles (Subj, Obj)
• Construction
• Dependency parsed using Connexor (English)
• Tapanainen and Jarvinen, 1997
• Hand-corrected
• Extensive manual and instructions on IAMTC Wiki
  website

61
IL0 coding manuals for other languages

• Japanese
• Spanish
• Korean (in progress)
• Hindi (in progress)
• French (in progress)

62
Example of IL0
Sheikh Mohammed, who is also the Defense Minister
of the United Arab Emirates, announced at the
inauguration ceremony that we want to make Dubai
a new trading center
TrEd, Pajas, 1998
63
Example of IL0

• Sheikh Mohammed, who is also the Defens Minister
  of the United Arab Emirates, announced at the
  inauguration ceremony that we want to make Dubai
  a new trading center
• announced V Root
• Mohamed PN Subj
• Sheikh PN Mod
• Defense_Minister PN Mod
• who Pron Subj
• also Adv Mod
• of P Mod
• UAE PN Obj
• at P Mod
• ceremony N Obj
• inauguration N Mod

64
Dependency parser and Omega ontology
Omega (ISI)110,000 concepts (WordNet,
Mikrokosmos, etc.), 1.1 mill instances URL
http//omega.isi.edu
Dependency parser (Prague)
65
Details of IL1

• Intermediate semantic representation
• Annotations performed manually by each person
  alone
• Associate open-class lexical items with Omega
  Ontology items
• Replace syntactic relations by one of approx. 20
  semantic (theta) roles (from Dorr), e.g., AGENT,
  THEME, GOAL, INSTR
• No treatment of prepositions, quantification,
  negation, time, modality, idioms, proper names,
  NP-internal structure
• Nodes may receive more than one concept
• Average about 1.2
• Manual under development annotation tool built

66
Example of IL1
Sheikh Mohammed, who is also the Defense Minister
of the United Arab Emirates, announced at the
inauguration ceremony that we want to make Dubai
a new trading center
67
Example of IL1 internal representation

• The study led them to ask the Czech government to
  recapitalize CSA at this level.
• 3, lead, V, lead, Root, LEADltGET, GUIDE
• 2, study, N, study, AGENT, SURVEYltWORK, REPORT
• 4, they, N, they, THEME, ---, ---
• 6, ask, V, ask, PROPOSITION, ---, ---
• 9, government, N, government, GOAL,
  AUTHORITIES,
• GOVERNMENTAL-ORGANIZATION
• 8, Czech, Adj, Czech, MOD,
  CZECHCZECHOSLOVAKIA, ---
• 11, recapitalize, V, recapitalize,
  PROP, CAPITALIZEltSUPPLY, INVEST
• 12, csa, N, csa, THEME,
  AIRLINEltLINE, ---
• 16, at, P, value_at, GOAL, ---,
  ---
• 15, level, N, level, ---,
  DEGREE, MEASURE
• 14, this, Det, this,
  ---, ---, ---

Semantic Roles
Concepts from the Omega Ontology
68
Tiamat annotation interface
For each new sentence
Step 1 find Omega concepts for objects and events
Candidate concepts
Step 2 select event frame (theta roles)
69
Omega ontology

• Single set of all semantic terms, taxonomized and
  interconnected (http//omega.isi.edu )
• Merger of existing ontologies and other
  resources
• Manually built top structure from ISI
• WordNet (110,000 nodes) from Princeton
• Mikrokosmos (6000 nodes) from NMSU
• Penman Upper model (300 nodes) from ISI
• 1-million instances (people, locations) from ISI
  
• TAP domain relations from Stanford
• Undergoing constant reconciliation and pruning
• Used in several past projects (metadata formation
  for database integration MT QA summarization)

70
So far

• Annotations of 12 English texts
• 6 pairs of translations of 1 text from each
  source language
• 10 12 annotators for each text
• Approximately 144 annotated texts total
• Annotation manuals for IL0 and IL1
• Annotation tools
• Work on evaluation for interannotator agreement.
• Now, were working on IL2 specification and
  annotation.

71
Getting at Meaning(Two translations of Korean
original text)

• Starting on January 1
• of next year,
• SK Telecom subscribers
• can switch to
• less expensive LG Telecom or KTF.
• The Subscribers
• cannot switch again
• to another provider
• for the first 3 months,
• but they can cancel
• the switch
• in 14 days
• if they are not satisfied with services
• like voice quality.

• Starting January 1st
• of next year
• customers of SK Telecom
• can change their service company to
• LG Telecom or KTF
• Once a service company swap has been made,
• customers
• are not allowed to change
• companies again
• within the first three months,
• although they can cancel
• the change
• anytime within 14 days
• if problems
• such as poor call quality
• are experienced.

72
Color Key

• Black same meaning and same expression
• Green small syntactic difference
• Khaki Lexical difference
• Red Not contained in the other text
• Purple Larger difference.
• Need to use some inference to know that the
  meaning is the same

73
Getting at meaning(Two translations of a
Japanese original text)

• This year,
• too,
• in addition to
• the birth
• of Mitsubishi Chemical,
• which has already been announced,
• other rather large-scale mergers
• may continue,
• and be recorded
• as a "year of mergers."

• This year,
• which has already seen
• the announcement
• of the birth
• of Mitsubishi Chemical Corporation
• as well as
• the continuous
• numbers of big mergers,
• may
• too
• be recorded
• as the "year of the merger
• for all we know.

More lexical similarity. More differences in
dependency relations.
74
Additional Topics in Lexical Semantics
75
English Transitivity Alternations

• Beth Levin, 1993
• Identified around 100 transitivity alternations
  in English.

76
Transitivity Alternations and Semantic Classes
Examples

• Causative-Inchoative change of state verbs
• Sam broke the glass. (causative)
• The glass broke. (inchoative)
• Sam opened the door.
• The door opened.
• Sam kicked the ball.
• The ball kicked.
• In other languages
• Inchoative verbs may be reflexive (e.g., Romance
  languages)
• There may be a causative marker on the transitive
  verb.
• Inchoative means beginning.
• Beginning a change of state?

77
Transitivity Alternations and Semantic Classes
Examples

• Dative Shift giving and telling
• I gave Sam the book.
• I gave the book to Sam.
• I told the story to the children.
• I told the children the story.
• I drove the car to New York.
• I drove New York the car.
• In other languages
• The goal may not be able to become a direct
  object. (Romance languages)
• The goal may become a direct object in the
  presence of an applicative morpheme. (Bantu
  languages)

78
Transitivity Alternations and Semantic Classes
Examples

• Spray-Load Alternation filling and covering.
• Sam sprayed the wall with paint.
• Sam sprayed paint on the wall.
• Sam loaded the truck with hay.
• Sam loaded hay onto the truck.

79
Transitivity Alternations and Semantic Classes
Examples

• There Insertion stative, appearing
• Problems exist.
• There exist problems.
• A ghost appeared.
• There appeared a ghost.
• The students worked.
• There worked some students.
• The students disappeared.
• There disappeared some students.

80
Transitivity Alternations and Semantic Classes
Examples

• Locative subjects
• Bees swarmed in the garden.
• The garden swarmed with bees.
• Temporal subjects
• 1990 saw the fall of the government.

81
Transitivity Alternations and Semantic Classes
Examples

• Middle Telic verbs? (see below)
• You can cut this bread.
• This bread cuts easily.
• You can sell these books easily.
• These books sell well.
• People like these books.
• These books like well.

82
Transitivity Alternations and Semantic Classes
Examples

• Resultative Secondary Predication theme version
• Sam hammered the nail.
• Sam hammered the nail flat.
• The lake froze.
• The lake froze solid.

83
Transitivity Alternations and Semantic Classes
Examples

• Resultative Secondary Predication agent version
• He screamed himself hoarse.
• He cried himself to sleep.

84
Class shifts

• Manner of motion to change of location
• The bottle floated.
• The bottle floated into the cave.
• The ball bounced.
• The ball bounced across the room.
• Sound to change of location
• The car rumbled.
• The car rumbled down the street.
• The dress rustled.
• She rustled across the room.

85
How universal?

• How universal is argument structure?
• If an English word has an agent and a patient,
  will the translation-equivalent in another
  language have an agent and patient?
• If an English word has a subject and object, will
  the translation-equivalent in another language
  have a subject and object?
• Less likely
• I met him.
• I met with him.

86
How Universal?

• How universal are alternations and semantic
  classes?
• If an English word undergoes a transitivity
  alternation, will the translation equivalent in
  another language undergo the same transitivity
  alternation?
• Even less likely. (Mitamura, 1989)

87
Importance of Transitivity Alternations in
Language Technologies

• For any task that requires understanding
  (question answering, information extraction,
  machine translation) you need to know the
  semantic roles of the NPs.
• The glass broke. (subject is patient)
• The kids ate. (subject is agent)
• I gave them some books (object is recipient)

88
Importance of Transitivity Alternations in
Language Technologies

• So you need multiple lexical mappings for each
  verb
• break lt agent patientgt
• subj obj
• break lt patient gt
• subj
• give lt agent theme recipientgt
• subj obj obl
• give lt agent theme recipientgt
• subj obj2 obj

89
Importance of Transitivity Alternations in
Language Technologies

• To speed up lexicon acquisition, assigning a verb
  to a semantic class and automatically generating
  its alternations is faster than listing all of
  its lexical mappings by hand.
• I gave books to the students.
• I gave the students books.
• Books were given to the students.
• The students were given books.
• There were books given to the students.
• There were students given books.

90
Lexical Aspect

• State
• The clock sat on the shelf.
• Activity
• The children painted.
• Accomplishment
• The children walked to school.
• Achievement
• The ambassador arrived in Moscow.

91
Lexical Aspect

• Took examples from this web page
  http//www.sfu.ca/person/dearmond/322/322.event.cl
  ass.htm
• Vendler, Linguistics in Philosophy, 1967
• Dowty, Word Meaning and Montague Grammar, 1979
• Tenny, Aspectual Roles and the Syntax-Semantics
  Interface, 1994

92
Activities and Accomplishments

• Activity
• The children painted for an hour.
• ?The children painted in an hour.
• The children will paint in an hour.
• They will start in an hour.
• The children almost painted.
• Almost started painting
• Test for telicity
• If you start to paint and stop, you have painted.
• Fails test for telicity.

• Accomplishment
• ?The children walked to school for an hour.
• The children walked to school in an hour.
• The children will walk to school in an hour.
• They will start in an hour, or it will take an
  hour.
• The children almost walked to school.
• Almost started walking, or almost reached school
• Test for telicity
• If you start to walk to school and stop, you may
  not have walked to school.
• Passes test for telicity.

93
Telicity

• Telic has a goal or endpoint (accomplishment)
• Atelic does not have a goal or endpoint
  (activity)
• Telicity can change depending on the sentence
• He built houses for a year/in a year.
• He built a house in a year/?for a year.

94
Achievements

• The ambassador almost arrived in Moscow.
• Only means almost finished not almost started.

95
States (English)

• Stative Simple present tense means present
  time. Present progressive does not sound good.
• He knows the answer.
• He is knowing the answer.
• Non-stative Simple present tense means habitual
  or generic. Present progressive means present
  time.
• He paints.
• He is painting.

96
Consequences of Lexical Aspect for Language
Technologies

• English
• You have to know the lexical aspect of the verb
  in order to know what the tense morphemes mean.
• The simple present tense means habitual with a
  non-stative verb, but means present time with a
  stative verb.
• You have to know the lexical aspect of the verb
  in order to know what the adverbials mean.
• Almost can mean almost started, almost
  finished, or both.

97
Consequences of Telicity

• Japanese
• Telic verbs with te iru have a resultative
  meaning
• Aite iru is open or has been opened, not is
  opening
• Otite iru is dropped (is on the floor), not is
  dropping (unless it takes a very long time to
  fall, like a leaf falling off of a sky scraper)
• Atelic verbs with te iru have a progressive
  meaning
• Tabete iru is eating, not has eaten

98
Consequences of Telicity

• Japanese -te aru (with passive-like meaning)
  only applies to telic verbs because it focuses on
  a resulting state. (e.g., wash (arau), but not
  praise (homeru))
• Sara ga aratte aru.
• Plate subj wash
• ???Taroo ga homete aru.

99
Consequences of Telicity Finnish

• Angelica Kratzer, Telicity and the Meaning of
  Objective Case, International Round Table The
  Syntax and Semantics of Aspect, Universite de
  Paris, Nov. 2000.
• Telic direct object can have partitive or
  accusative case (with a slight difference in
  meaning)
• Ammu-i-n karhu-a
• Shoot-past-1sg bear-part
• I shot at a/the bear
• Ammu-i-n karhu-n
• Shoot-past-1sg bear-acc
• I shot the bear
• Atelic can only have partitive case despise,
  admire, envy, love, study, play, listen, pull

100
Consequences of Telicity Chinese

• Lisa Lai Shen Cheng, Aspects of the
  Ba-Construction, Lexicon Project Working Papers
  24, Carol Tenny (ed.), MIT, 1988.
• Ta ba shu mai le.
• He BA book sell ASP
• He sold the book
• Factors determining grammaticality of the
  ba-construction
• Aspect markers occurs with le and zhe, but not
  with zai and guo.
• Definiteness The direct object has to be
  interpretable as definite.
• Telicity of the verb tui le (pushed) vs. tui
  dao le (pushed down push-fall) la le (pull) vs.
  la dao le (pull down pull-fall) dai le
  (bring/carry) vs. dai lai le (bring here
  carry-come)

101
Ba and Telicity

• Wo ba Lisì tui-le.
• I BA Lisi push-ASP
• I pushed Lisi.
• Wo ba Lisì tui-dao-le.
• I BA Lisi push-fall ASP
• I pushed Lisi and he fell.

102
Ba and Telicity

• Ta ba Zhangsan la-le.
• He BA Zhangsan pull-ASP
• He pulled Zhangsan.
• Ta ba Zhangsan la-dao-le.
• He BA Zhangsan pull-fall-ASP
• He pulled Zhangsan and Zhangsan fell.

103
Ba and Telicity

• Ta ba dìan-nao dài-le.
• He BA computer bring-ASP
• He brought the computer.
• (Does this really mean He carried the
  computer?)
• Ta ba dìan-nao dài-lái-le.
• He BA computer bring-come-ASP
• He brought the computer here.

104
Ba and Telicity

• Ta ba fángjian da-sao-le.
• He BA room hit-sweep-ASP
• He cleaned the room.
• Ta ba fángjian da-sao de hen ganjìng.
• He BA room hit-sweep DE very clean
• He cleaned the room and the result is that the
  room is very clean.

105
Two kinds of intransitive verbs subject is
agentive or not

• Sam worked.
  agentive
• Sam fell (by accident). non-agentive
• Unaccusative an intransitive verb whose subject
  is not agentive.
• Because the noun phrase would have been
  accusative if the verb were transitive?
• Unergative an intransitive verb whose subject is
  agentive.
• Because the noun phrase would have been ergative
  if the verb were transitive?
• Confusing terminology by David Perlmutter and
  Paul Postal.
• Highly influential and insightful contribution to
  linguistic theory also by David Perlmutter and
  Paul Postal.

106
Consequences of Unaccusativity or Agentivity

• English Resultative secondary predication
• He screamed hoarse.
• ?He worked to exhaustion.
• He worked himself to exhaustion
• It broke to pieces.
• It froze solid.

107
Consequences of Unaccusativity or Agentivity
German Impersonal Passive

• http//www.wm.edu/CAS/modlang/gasmit/grammar/passi
  ve/impspass.htm
• Hier wird nicht geparkt.
• No parking here.
• Im Gang wird nicht geraucht.
• No smoking in the corridor.
• Es wurde viel getanzt und gesungen.
• There was lots of dancing and singing.
• Works with agentive verbs only.
• Not with break, fall, etc.

108
Consequences of Unaccusativity Italian partitive
clitics

• http//www.sfu.ca/person/dearmond/405/405.ergative
  .unaccusative.htm
• Sono passate tre settimane.
• Are passed three weeks
• Three weeks have passed.
• Ne sono passate tre.
• Of-them are passed three
• Three of them have passed.
• Ne sono arrivati(?) tre.
• Of-them are arrived three
• Three of them have arrived.
• Ne hanno telefonato(?) tre.
• Of-them have phoned three
• Three of them have arrived.

109
Importance of unaccusativity

• Non agentive subjects, direct object, subjects of
  passives
• The water froze solid.
• He hammered the nail flat.
• The nail was hammered flat.
• Agentive subjects and subjects of active,
  transitive verbs.
• He hammered the nail exhausted.
• Doesnt mean that he became exhausted as a result
  of hammering the nail.
• He screamed hoarse.
• Doesnt mean that he became hoarse as a result of
  screaming.

110
Importance of Unaccusativity

• Non-agentive subjects behave like direct objects.
  
• Passive subjects correspond to direct objects of
  active sentences.
• The Unaccusative Hypothesis (Perlmutter and
  Postal) Maybe non-agentive subjects are direct
  objects at some level of representation.

111
Example of insight from the unaccusative
hypothesis

• Why cant German unaccusative verbs become
  impersonal passives?
• They are already passive! The non-agentive
  subject was at some point an object that got
  promoted.