Introduction to Semantics and Pragmatics

LING 2000 - 2006 NLP 2

NLP tends to focus on:

• Syntax– Grammars, parsers, parse trees, dependency

structures• Semantics

– Subcategorization frames, semantic classes, ontologies, formal semantics

• Pragmatics– Pronouns, reference resolution, discourse

models

LING 2000 - 2006 NLP 3

Semantics and Pragmatics

High-level Linguistics (the good stuff!)

Semantics: the study of meaning that can be determined from a sentence, phrase or word.

Pragmatics: the study of meaning, as it depends on context (speaker, situation)

LING 2000 - 2006 NLP 4

Language to Logic

• John went to the book store. John store1, go(John, store1)

• John bought a book. buy(John,book1)

• John gave the book to Mary. give(John,book1,Mary)

• Mary put the book on the table. put(Mary,book1,table1)

LING 2000 - 2006 NLP 5

SemanticsSame event - different sentences

John broke the window with a hammer.

John broke the window with the crack.

The hammer broke the window.

The window broke.

LING 2000 - 2006 NLP 6

Same event - different syntactic frames

John broke the window with a hammer. SUBJ VERB OBJ MODIFIER

John broke the window with the crack. SUBJ VERB OBJ MODIFIER

The hammer broke the window. SUBJ VERB OBJ

The window broke. SUBJ VERB

LING 2000 - 2006 NLP 7

Semantics -predicate arguments

break(AGENT, INSTRUMENT, PATIENT)

AGENT PATIENT INSTRUMENT John broke the window with a hammer.

INSTRUMENT PATIENT The hammer broke the window.

PATIENT The window broke.

Fillmore 68 - The case for case

LING 2000 - 2006 NLP 8

AGENT PATIENT INSTRUMENT John broke the window with a hammer. SUBJ OBJ MODIFIER

INSTRUMENT PATIENT The hammer broke the window. SUBJ OBJ

PATIENT The window broke. SUBJ

CIS 8590 – Fall 2008 NLP 9

Natural Language Processing Applications and Tasks

• Machine Translation• Question-Answering• Information Retrieval• Information Extraction

LING 2000 - 2006 NLP 10

Machine Translation

• One of the first applications for computers– bilingual dictionary > word-word translation

• Good translation requires understanding!– War and Peace, The Sound and The Fury?

• What can we do? Sublanguages.– technical domains, static vocabulary– Meteo in Canada, Caterpillar Tractor Manuals,

Botanical descriptions, Military Messages

LING 2000 - 2006 NLP 11

Example translation

Machine Translation

• The Story of the Stone – =The Dream of the Red Chamber (Cao Xueqin 1792)

• Issues: (“Language Divergences”)– Sentence segmentation– Zero-anaphora– Coding of tense/aspect

– Penetrate -> penetrated– Stylistic differences across languages

• Bamboo tip plaintain leaf -> bamboos and plantains– Cultural knowledge

• Curtain -> curtains of her bed

Machine Translation• Chinese gloss: Dai-yu alone on bed top think-of-with-gratitude Bao-chai

again listen to window outside bamboo tip plantain leaf of on-top rain sound sigh drop clear cold penetrate curtain not feeling again fall down tears come

• Hawkes translation: As she lay there alone, Dai-yu’s thoughts turned to Bao-chai… Then she listened to the insistent rustle of the rain on the bamboos and plantains outside her window. The coldness penetrated the curtains of her bed. Almost without noticing it she had begun to cry.

Language Families

Babelfish Demo

http://babelfish.yahoo.com/

Old example:

The spirit is willing, but the flesh is weak.

Question Answering

• What does “door” mean?• What year was Abraham Lincoln born?• How many states were in the United States

when Lincoln was born?• Was there a military draft during the Hoover

administration?• What do US scientists think about whether

human cloning should be legal?

Modern QA systems

• Still in infancy• Simple factoid questions beginning to work OK• Annual government-sponsored “bakeoff”

called TREC

QA Demo

UIUC QA Demo

Qualim QA Demo

NLP 19

Issues in NLP

• Ambiguity!

• World Knowledge – it’s needed for understanding, but computers don’t have it

Ambiguity

• Computational linguists are obsessed with ambiguity

• Ambiguity is a fundamental problem of computational linguistics

• Resolving ambiguity is a crucial goal

Ambiguity

• Find at least 5 meanings of this sentence:– I made her duck

Ambiguity• Find at least 5 meanings of this sentence:

– I made her duck• I cooked waterfowl for her benefit (to eat)• I cooked waterfowl belonging to her• I created the (plaster?) duck she owns• I caused her to quickly lower her head or body• I waved my magic wand and turned her into undifferentiated waterfowl• At least one other meaning that’s inappropriate for gentle company.

Ambiguity is Pervasive• I caused her to quickly lower her head or body

– Lexical category: “duck” can be a N or V• I cooked waterfowl belonging to her.

– Lexical category: “her” can be a possessive (“of her”) or dative (“for her”) pronoun

• I made the (plaster) duck statue she owns– Lexical Semantics: “make” can mean “create” or “cook”

Ambiguity is Pervasive

• Grammar: Make can be:– Transitive: (verb has a noun direct object)

• I cooked [waterfowl belonging to her]

– Ditransitive: (verb has 2 noun objects)• I made [her] (into) [undifferentiated waterfowl]

– Action-transitive (verb has a direct object and another verb)

– I caused [her] [to move her body]

Ambiguity is Pervasive• Phonetics!

– I mate or duck– I’m eight or duck– Eye maid; her duck– Aye mate, her duck– I maid her duck– I’m aid her duck– I mate her duck– I’m ate her duck– I’m ate or duck– I mate or duck

LING 2000 - 2006 NLP 26

Headlines

• Police Begin Campaign To Run Down Jaywalkers

• Iraqi Head Seeks Arms

• Teacher Strikes Idle Kids

• Miners Refuse To Work After Death

• Juvenile Court To Try Shooting Defendant

Kinds of knowledge needed?

• Consider the following interaction with HAL the computer from 2001: A Space Odyssey

• Dave: Open the pod bay doors, Hal.• HAL: I’m sorry Dave, I’m afraid I can’t do

that.

Knowledge needed to build HAL?

• Speech recognition and synthesis– Dictionaries (how words are pronounced)– Phonetics (how to recognize/produce each sound of English)

• Natural language understanding– Knowledge of the English words involved

• What they mean• How they combine (what is a `pod bay door’?)

– Knowledge of syntactic structure• I’m I do, Sorry that afraid Dave I’m can’t

What’s needed?

• Dialog and pragmatic knowledge– “open the door” is a REQUEST (as opposed to a

STATEMENT or information-question)– It is polite to respond, even if you’re planning to

kill someone.– It is polite to pretend to want to be cooperative

(I’m afraid I can’t…)– What is `that’ in `I can’t do that’?

• Even a system to book airline flights needs much of this kind of knowledge

Eliza: Weizenbaum (1966)• User: You are like my father in some ways.• ELIZA: What resemblance do you see• User: You are not very aggressive but I think you don’t want me to notice

that.• ELIZA: What makes you think I am not aggressive• User: You don’t argue with me• ELIZA: Why do you think I don’t argue with you• User: You are afraid of me• ELIZA: Does it please you to believe I am afraid of you

Modern conversational agents• Switchboard Receptionists

– Implications: need to know how a native or non-native speaker of English is likely to pronounce a random American name. Eek.

• Airline and other simple dialogue systems– Lots of fabulously fun pragmatic issues

• Grounding• Error handling

• In general– Now that there are commercial installations– Big incentive to use linguistic or any other kind of knowledge if it

actually improves performance

Well-understood Semantic Tasks

• Named-entity Recognition

• Coreference Resolution

• Semantic Role Labeling

• Sentiment Classification

Entities

Named Entity Tagging: Identify all the proper names in a text

Sally went to see Up in the Air at the local theater.

Coreference Resolution: Identify all references (aka ‘mentions’) of people, places and things in text, and determine which mentions are ‘co-referential’.

John stuck his foot in his mouth.

Semantic Role Labeling

Semantic role labeling is computational task of assigning semantic roles to phrases

B-A0 REL B-A1 I-A1 B-AM I-AM I-AM John broke the window with a hammer.

Sentiment Classification

Given a review (about a movie, hotel, Amazon product, etc.), a sentiment classification system tries to determine what opinions are expressed in the review.

Coarse-level objective: is the review positive, negative, or neutral overall?

Fine-grained objective: what are the positive aspects (according to the reviewer), and what are the negative aspects?