NLP

1
NLP semantics

• Points
• Semantic analysis
• Semantic markers
• Case analysis
• Syntactic patterns
• Case lists
• An algorithm
• Quantifier scope
• A taste of discourse analysis
• A look at pragmatics

2
Semantic analysis

• Semantic analysis may follow parsing map a parse
  tree (a syntactic structure) into a
  representation of meaning (a knowledge
  structure).
• Semantics resides at both sides of parsing, and
  elements of meaning come from words. Lexical
  knowledge lives in dictionaries. It has two
  forms.
• Morphological and syntactic information about
  the word part-of-speech (class), number, case,
  gender, tense, requirements (for verbs), and so
  on.
• Semantic information about the word, for
  example, a semantic marker that locates in a
  hierarchy of concepts the concept that the word
  denotes.

3
Semantic markers
Suppose that a dictionary entry contains both
syntactic and semantic information. (Verb
patterns would be unused for other classes.) For
example lexicon( Word, Class, SyntCategories,
Root, VerbPattern, Semantics). The word
ball could have at least these two
entries lexicon( ball, verb, inf, pres,
ball, trans, makeBall). lexicon( ball, noun,
sg, ball, _unused, sportsEquipment,
dance).
4
Semantic markers (2)
Here is a place for the noun senses of ball
(semantic markers) in a possible hierarchy
5
Case analysis
It is one of many methods of semantic analysis,
based on familiar ideas recognize a general
situation (denoted by a verb) and roles in this
situation (denoted usually by noun
phrases). Examples of syntactic verb
patterns intransitive (subject, verb) Jim
laughed. transitive (subject, verb, object) Jim
found a penny. bitransitive (subject, verb,
indirect object, object) Jim gave a penny to
Jill. to-inf (subject, verb, infinitive
clause) Jim wanted to laugh. object to-inf
(subject, verb, object, infinitive clause) Jim
wanted Jill to laugh. for-object to-inf
(subject, verb, for object, infinitive
clause) Jim waited for Jill to laugh.
6
Case analysis (2)
Examples of semantic verb patterns Agent(if the
subject is animate then the subject ?
Agent) laughed( Jim ) Agent Object(if the
subject is animate thenthe subject ? Agent, the
object ? Object) found( Jim, penny ) Agent
Object Beneficiaryif the subject is animate
then the subject ? Agentif the indirect object
is animate then the indirect object ?
Beneficiary, the object ? Object) gave( Jim,
penny, Jill )
7
Case analysis (3)
Another example of semantic verb patterns Agent
Content(if subject is animate then subject ?
Agentsubordinate sentence ? Content) wanted(
Jim, ? Jim laugh ) wanted( Jim, ? Jill laugh
) We need some form of a pointer to the semantic
structure for the embedded sentence. Recall the
boxed propositions in the conceptual graph
notation.
8
Case analysis (4)
Lists of cases used in NLP systems have usually
more than a few elements. Here is an
example. Participant cases Accompaniment, Agent,
Beneficiary, Exclusion, Experiencer, Instrument,
Object, Recipient Causality cases Cause, Effect,
Opposition, Purpose Spatial cases Direction,
LocationAt, LocationFrom, LocationTo,
LocationThrough, Orientation, Order Temporal
cases Frequency, TimeAt, TimeFrom, TimeThrough,
TimeTo Quality cases Content, Manner, Material,
Measure
9
Case analysis (5)
A case marker is a syntactic element that signals
the presence of a case. A preposition (in, at,
from, of, for, ...) may mark cases. A position of
a noun phrase (subject, direct object, indirect
object) also marks case. Subject Agent Jim hit
the ball. Experiencer Jim grew hungry as time
passed. Instrument The ball broke the
window. Cause The wind broke the window with a
branch. Indirect object Recipient I threw the dog
a ball. Beneficiary I wrote her a reference
letter to her boss. Direct object Object John
hits the ball.
10
Case analysis (6)
A few examples of markers that mark exactly one
case. LocationThrough We walked around the
courtyard. Manner She acted as my agent last
year. LocationAt Sit beside me. Exclusion Everyone
was pleased except her. Opposition They
persisted despite my warning. TimeFrom He has
been sick since the accident. TimeTo We worked
till dawn.
11
Case analysis (7)
Examples of markers that mark many cases at,
for. Direction The deer ran right at the
hunters. LocationAt I stood at the
door. TimeAt The case will be heard at
noon. Manner The car moves at high
speed. Content She is good at arts. Measure It
stopped at fifty. Cause She was amazed at his
insolence. LocationTo Aim for the
heart. Direction Run for the train. Content I
stand for social responsibility. TimeThrough They
worked for three hours. Beneficiary I'd walk a
mile for them. Purpose This drug is for people
with a flu. Measure Sell it for fifty
dollars. Cause He received a medal for
courage. Recipient This mail is for
everyone. TimeAt Call him for ten o'clock.
12
Case analysis (8)

• An algorithm of case analysis
• In the parse tree, identify all case markers.
• Find case patterns of the main verb (assume a
  knowledge base of patterns!).
• Apply rules based on lexical, syntactic and
  semantic features to match case markers with
  cases.
• Examples of rules see slides 6-7 for more
• active sentence, animate subject subject ? Agent
• Jim laughed.
• passive sentence, inanimate subject subject ?
  Object
• The window was broken.
• passive sentence, animate subject subject ?
  Experiencer
• Jim was detained.

13
Quantifier scoping

• Every author wrote a book.
• ?a ? b author(a) ? book(b) ? wrote(a, b)
• skolemize ?a author(a) ? book(s(a)) ? wrote(a,
  s(a))
• ? b ?a author(a) ? book(b) ? wrote(a, b)
• skolemize ?a author(a) ? book(B0) ? wrote(a,
  B0)
• Only one scoping is correct which one?
• The man picked up all papers.
• THE m ?p man(m) ? paper(p) ? pickedUp(m, p)
• ?p THE m man(m) ? paper(p) ? pickedUp(m, p)
• A simple algorithm fixed precedence, for
  example,
• the gt each gt what, who, whom gt every, all, some,
  a
• But there is no universally approved, objective
  ordering.

14
A taste of discourse analysis

• Text units beyond sentences examples
• A story (such as a fairy tale, a drama, ...).
• A news item.
• Dialogue.
• Technical text (manual, textbook, documentation).
• A document in a document base (abstract, patent
  description, ...).
• Links between sentences/phrases in a larger text
• Textual ordering.
• Temporal link (for example, an event precedes
  another event).
• Jim saw the bus. He ran to catch it.
• saw precedes ran

15
... discourse analysis (2)

• Causal link (for example, reason, effect,
  prerequisite).
• Jim saw the bus pull away. He waved to the
  driver.
• waved could be an effect of saw
• Coreference linking references to the same
  entity.
• Jim bought a book. He liked it a lot.
• he Jim, 'it' book (and bought precedes
  liked)
• Jim bought a book. The price was good.
• price is a property of books (and it enables
  buying)
• Jim bought a book. He paid 10.
• paying is an element of (is included in) buying
• Jim bought a book. The dust-jacket was red.
• dust-jackets are parts of books

16
A look at pragmatics

• Focus
• Here is one tiny example from a hypothetical NLP
  interface to an airline reservation system
• I want to fly to Vancouver tomorrow night.
• There is a flight at 6.
• When does it arrive?
• At 8 local time.
• Is it WestJet?
• No, Air Canada.
• Show me others. ? shift of focus
• Modelling beliefs who knows what, who believes
  what.
• This can be done formally, in advanced forms of
  logic, for example in autoepistemic logic (check
  it out).

17
... pragmatics (2)

• Plan-based understanding
• We can use scripts (see textbook, section 7.1.4).
• Jim was hungry. He stopped at Nates deli.
• A possible line of reasoning

Scripts (and other similar representations of
plans) help fill gaps in the story.
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... pragmatics (3)

• Speech acts
• assertinformexplain
• ask ifask what
• orderrequest.
• Indirect speech acts
• The form disagrees with the intention a question
  (interrogative) or a statement (declarative)
  really means something different.
• Could you pass the salt? a request
• Do you know that its raining? information
• Honey, Fido needs a shower. a command

... time out and there is still so much to
tell...