Fall 2004

1

EECS 595 / LING 541 / SI 661
Natural Language Processing

• Fall 2004
• Lecture Notes 8

2
Discourse Analysis
3
The problem

• Discourse
• Monologue and Dialogue (dialog)
• Human-computer interaction
• Example John went to Bills car dealership to
  check out an Acura Integra. He looked at it for
  about half an hour.
• Example Id like to get from Boston to San
  Francisco, on either December 5th or December
  6th. Its okay if it stops in another city along
  the way.

4
Information extraction and discourse analysis

• Example First Union Corp. is continuing to
  wrestle with severe problems unleashed by a
  botched merger and a troubled business strategy.
  According to industry insiders at Paine Webber,
  their president, John R. Georgius, is planning to
  retire by the end of the year.
• Problems with summarization and generation

5
Reference resolution

• The process of reference (associating John with
  he).
• Referring expressions and referents.
• Needed discourse models
• Problem many types of reference!

6
Example (from Webber 91)

• According to John, Bob bought Sue an Integra, and
  Sue bough Fred a legend.
• But that turned out to be a lie. - referent is a
  speech act.
• But that was false. - proposition
• That struck me as a funny way to describe the
  situation. - manner of description
• That caused Sue to become rather poor. - event
• That caused them both to become rather poor. -
  combination of several events.

7
Reference phenomena

• Indefinite noun phrases I saw an Acura Integra
  today.
• Definite noun phrases The Integra was white.
• Pronouns It was white.
• Demonstratives this Acura.
• Inferrables I almost bought an Acura Integra
  today, but a door had a dent and the engine
  seemed noisy.
• Mix the flour, butter, and water. Kneed the dough
  until smooth and shiny.

8
Constraints on coreference

• Number agreement John has an Acura. It is red.
• Person and case agreement () John and Mary have
  Acuras. We love them (where WeJohn and Mary)
• Gender agreement John has an Acura. He/it/she is
  attractive.
• Syntactic constraints
• John bought himself a new Acura.
• John bought him a new Acura.
• John told Bill to buy him a new Acura.
• John told Bill to buy himself a new Acura
• He told Bill to buy John a new Acura.

9
Preferences in pronoun interpretation

• Recency John has an Integra. Bill has a Legend.
  Mary likes to drive it.
• Grammatical role John went to the Acura
  dealership with Bill. He bought an Integra.
• (?) John and Bill went to the Acura dealership.
  He bought an Integra.
• Repeated mention John needed a car to go to his
  new job. He decided that he wanted something
  sporty. Bill went to the Acura dealership with
  him. He bought an Integra.

10
Preferences in pronoun interpretation

• Parallelism Mary went with Sue to the Acura
  dealership. Sally went with her to the Mazda
  dealership.
• ??? Mary went with Sue to the Acura dealership.
  Sally told her not to buy anything.
• Verb semantics John telephoned Bill. He lost his
  pamphlet on Acuras. John criticized Bill. He lost
  his pamphlet on Acuras.

11
An algorithm for pronoun resolution

• Two steps discourse model update and pronoun
  resolution.
• Salience values are introduced when a noun phrase
  that evokes a new entity is encountered.
• Salience factors set empirically.

12
Salience weights in Lappin and Leass
13
Lappin and Leass (contd)

• Recency weights are cut in half after each
  sentence is processed.
• Examples
• An Acura Integra is parked in the lot. (subject)
• There is an Acura Integra parked in the lot.
  (existential predicate nominal)
• John parked an Acura Integra in the lot. (object)
• John gave Susan an Acura Integra. (indirect
  object)
• In his Acura Integra, John showed Susan his new
  CD player. (demarcated adverbial PP)

14
Algorithm

• Collect the potential referents (up to four
  sentences back).
• Remove potential referents that do not agree in
  number or gender with the pronoun.
• Remove potential referents that do not pass
  intrasentential syntactic coreference
  constraints.
• Compute the total salience value of the referent
  by adding any applicable values for role
  parallelism (35) or cataphora (-175).
• Select the referent with the highest salience
  value. In case of a tie, select the closest
  referent in terms of string position.

15
Example

• John saw a beautiful Acura Integra at the
  dealership last week. He showed it to Bob. He
  bought it.

16
Example (contd)
17
Example (contd)
18
Example (contd)
19
Example (contd)
20
Example (contd)
21
Observations

• Lappin Leass - tested on computer manuals - 86
  accuracy on unseen data.
• Centering (Grosz, Josh, Weinstein) additional
  concept of a center at any time in discourse,
  an entity is centered.
• Backwards looking center forward looking centers
  (a set).
• Centering has not been automatically tested on
  actual data.

22
Discourse structure

• () Bill went to see his mother. The trunk is
  what makes the bonsai, it gives it both its grace
  and power.
• Coherence principle
• John hid Bills car keys. He was drunk
• ?? John hid Bills car keys. He likes spinach
• Rhetorical Structure Theory (Mann, Matthiessen,
  and Thompson)

23
Sample rhetorical relations
24
Example (from MMT)
1) Title Bouquets in a basket - with living
flowers 2) There is a gardening revolution going
on. 3) People are planting flower baskets with
living plants, 4) mixing many types in one
container for a full summer of floral beauty. 5)
To create your own "Victorian" bouquet of
flowers, 6) choose varying shapes, sizes and
forms, besides a variety of complementary colors.
7) Plants that grow tall should be surrounded by
smaller ones and filled with others that tumble
over the side of a hanging basket. 8) Leaf
textures and colors will also be important. 9)
There is the silver-white foliage of dusty
miller, the feathery threads of lotus vine
floating down from above, the deep greens, or
chartreuse, even the widely varied foliage colors
of the coleus. Christian Science Monitor,
April, 1983
25
Example (contd)
26
Cross-document structure
27
(No Transcript)
28
(No Transcript)
29
Dialogueand conversational agents
30
Abbott You know, strange as it may seem, they
give ball players nowadays very peculiar
names...Now, on the Cooperstown team we have
Who's on first, What's on second, I Don't Know is
on third- Costello That's what I want to find
out. I want you to tell me the names of the
fellows on the Cooperstown team. Abbott I'm
telling you. Who's on first, What's on second, I
Don't Know is on third. Costello You know the
fellows' names? Abbott Yes. Costello Well,
then, who's playin' first? Abbott Yes.
Costello I mean the fellow's name on first
base. Abbott Who. Costello The fellow's name
on first base for Cooperstown. Abbott Who.
Costello The guy on first base. Abbott Who is
on first base. Costello Well, what are you
asking me for? Abbott I'm not asking you--I'm
telling you. Who is on first. Costello I'm
asking you--who's on first? Abbott That's the
man's name.
31
Costello That's who's name? Abbott Yes.
Costello Well, go ahead, tell me! Abbott Who.
Costello The guy on first. Abbott Who.
Costello The first baseman. Abbott Who is on
first. Costello Have you got a first baseman on
first? Abbott Certainly. Costello Well, all
I'm trying to find out is what's the guy's name
on first base. Abbott Oh, no, no. What is on
second base. Costello I'm not asking you who's
on second.
32
What makes dialogue different

• Turns and utterances (turn-taking)
• Turn-taking rules
• At each TRP (transition-relevance place)
• designated speaker, any speaker, current speaker
• Barge-in possible
• Significant silence
• A Is there something bothering you or not? (1.0
  s)
• A Yes or no? (1.5 s)
• A Eh?
• B No.

33
Grounding

• Common ground between speaker and hearer.
• A returning on flight 1118
• C mm hmmm (backchannel, acknowledgment token)
• Other continuers
• Continued attention
• Relevant next contribution
• Acknowledgement (e.g. sure)
• Demonstration (paraphrasing, reformulating)
• Display (repeat verbatim)
• Example
• C I will take the 5 pm flight on the 11th.
• A On the 11th?

34
Conversational Implicature

• Example
• When do you want to travel?
• I have a meeting there early in the morning on
  the 13th.
• Implicature licensed inferences reasonable
  hearers can make.
• Quantity
• Agent there are three non-stop flights daily

35
Grices maxims

• Maxim of quantity
• make your contribution informative
• but not more than needed
• Maxim of quality
• do not say what you believe is false
• do not say that for which you lack evidence
• Maxim of relevance
• Maxim of manner
• avoid ambiguity
• avoid obscurity
• be brief
• be orderly

36
Dialogue acts

• Performative sentences
• I name this ship the Titanic
• I second that motion
• I bet you five dollars that it will snow tomorrow
• Speech acts
• locutionary acts uttering a sentence with a
  particular meaning
• illocutionary acts asking, promising, answering
• perlocutionary acts producing effects upon the
  feelings, thoughts, or actions of the addressee

37
Speech acts (contd)

• Assertives suggesting, putting forward,
  swearing, boasting, concluding
• Directives asking, ordering, requesting,
  inviting, advising, begging
• Commissives promising, planning, vowing,
  betting, opposing
• Expressives thanking, apologizing, welcoming,
  deploring
• Declarations I resign, youre fired.

38
Automatic interpretation of dialogue acts

• DAMSL - Dialogue Act Markup in Several Layers
• Agreement (Accept, Maybe, Reject-Part, Hold)
• Answer
• Understanding (Signal-not-understood,
  Signal-understood, ack, repeat-rephrase,
  completion)

39
Techniques for DA recognition

• Plan theoretic (agents, assumptions, goals)
• Cue-based (please, are you?, rising pitch,
  stress - agreement vs. backchannel)
• Statistical approaches

40
Semantic Analysis
41
Syntax-Driven Semantic Analysis

• Meaning representations are assigned to
  linguistic inputs
• Needed static knowledge from the lexicon and
  grammar
• Principle of compositionality

42
Principle of compositionality

• The meaning of a sentence is composed of the
  meanings of its parts.
• Ordering and grouping are important
• Problem which subcategorization frame? In
  general a lot of knowledge about the particular
  example is needed.

43
Example
? e Isa (e,Serving) Server (e, NachoMamas)
Served (e, fajitas)
S
NP
VP
NP
Proper-Noun
Mass-Noun
Verb
NachoMamas
serves
fajitas
44
Two important questions

• What does it mean for syntactic constituents to
  have meanings?
• What do these meanings have to be like so that
  they can be composed into larger meanings?

45
Semantic Augmentation to CFG Rules

• Semantic attachments
• A ? a1 an f(aj.sem, ,
  ak.sem)
• ProperNoun ? NachoMamas NachoMamasMassNoun
  ? fajitas fajitasNP ? ProperNoun
  ProperNoun.semNP ? MassNoun MassNoun.semVerb
  ? serves ? e,x,y Isa (e,Serving) Server (e,
  x) Served (e, y)Verb ? Isa (e,Serving)
  Server (e,x) Served (e,fajitas)
• Typical approaches include using lambda-calculus.

46
Readings for next time

• JM Chapter 21