Transcript NLP — semantics

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
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NLP — semantics, page 1
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.
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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]).
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Semantic markers (2)
Here is a place for the noun senses of “ball”
(semantic markers) in a possible hierarchy:
physical
object
...
artifact
natural
object
...
...
equipment
tool
...
sports
equipment
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event
...
social natural
event
event
...
...
charity
entertainment
...
dance
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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.
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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 then
the subject → Agent, the object → Object)
found( Jim, penny )
Agent + Object + Beneficiary
if the subject is animate then the subject → Agent;
if the indirect object is animate then the indirect object
→ Beneficiary, the object → Object)
gave( Jim, penny, Jill )
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Case analysis (3)
Another example of semantic verb patterns
Agent + Content
(if subject is animate then subject → Agent;
subordinate 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.]
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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
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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.
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Case analysis (6)
A few examples of markers that mark exactly one case.
LocationThrough
Manner
LocationAt
Exclusion
Opposition
TimeFrom
TimeTo
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We walked around the courtyard.
She acted as my agent last year.
Sit beside me.
Everyone was pleased except her.
They persisted despite my warning.
He has been sick since the accident.
We worked till dawn.
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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.
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Case analysis (8)
An algorithm of case analysis
1. In the parse tree, identify all case markers.
2. Find case patterns of the main verb (assume a knowledge
base of patterns!).
3. 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.
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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 > each > what, who, whom > every, all, some, a
But: there is no universally approved, objective ordering.
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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”
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... 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
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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).
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... pragmatics (2)
Plan-based understanding
We can use scripts (see textbook, section 7.1.4).
Jim was hungry. He stopped at Nate’s deli.
A possible line of reasoning:
hungry
needs
food
buys
food
at
at
restaurant market
Scripts (and other similar
representations of plans)
help fill gaps in the story.
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fast
food
deli
formal
burgers
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... pragmatics (3)
Speech acts
assert—inform—explain;
ask if—ask what;
order—request.
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 it’s raining?
information
Honey, Fido needs a shower.
a command
... time out — and there is still so much to tell...
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