summer school on natural language processing and text mining 2008 natural language generation an...

Summer School on Natural Language Processing and Text Mining 2008

Natural Language Generation

An Introductory Tour

Anupam BasuDept. of Computer Science &

EngineeringIIT Kharagpur

Text

Language Technology

Natural Language

Understanding

Natural Language

Generation

Speech Recognition

Speech Synthesis

Text

Meaning

Speech Speech

What is NLG? Thought / conceptualization of the world ------ Expression

The block c is on block a

The block a is under block c

The block b is by the side of a

The block b is on the right of a

The block b has its top free

The block b is alone ………

Conceptualization

Some intermediate form of representation

ON (C, A)

ON (A, TABLE)

ON (B, TABLE)

RIGHT_OF (B,A) …….

What to say?

Conceptualization

C

A B

On

Right_of

Is_aBlock

Is_a

What to say?

What to say ? How to say ?

Natural language generation is the process of deliberately constructing a natural language text in order to meet specified communicative goals.

[McDonald 1992]

Some of the Applications Machine Translation

Question Answering

Dialogue Systems

Text Summarization

Report Generation

Thought / Concept Expression Objective:

produce understandable and appropriate texts in human languages

Input: some underlying non-linguistic representation

of information

Knowledge sources required: Knowledge of language and of the domain

Involved Expertise Knowledge of Domain

What to say Relevance

Knowledge of Language Lexicon, Grammar, Semantics

Strategic Rhetorical Knowledge How to achieve goals, text types, style

Sociolinguistic and Psychological Factors Habits and Constraints of the end user as an information

processor

Asking for a pen

have(X, z) not have (Y,z)

want have (Y,z)

ask(give (X,z,Y)))

Could you please give me a pen?

Situation

Goal

Conceptualization

Expression

Why?

What?

How?

Summer School on Natural Language Processing and Text Mining 2008

Some Examples

Example System #1: FoG Function:

Produces textual weather reports in English and French Input:

Graphical/numerical weather depiction User:

Environment Canada (Canadian Weather Service) Developer:

CoGenTex Status:

Fielded, in operational use since 1992

FoG: Input

FoG: Output

Example System #2: STOP Function:

Produces a personalised smoking-cessation leaflet Input:

Questionnaire about smoking attitudes, beliefs, history User:

NHS (British Health Service) Developer:

University of Aberdeen Status:

Undergoing clinical evaluation to determine its effectiveness

STOP: InputSMOKING QUESTIONNAIRE

Please answer by marking the most appropriate box for each question like this:

Q1 Have you smoked a cigarette in the last week, even a puff?YES NO

Please complete the following questions Please return the questionnaire unanswered in theenvelope provided. Thank you.

Please read the questions carefully. If you are not sure how to answer, just give the best answer you can.

Q2 Home situation:Livealone

Live withhusband/wife/partner

Live withother adults

Live withchildren

Q3 Number of children under 16 living at home ………………… boys ………1……. girls

Q4 Does anyone else in your household smoke? (If so, please mark all boxes which apply)husband/wife/partner other family member others

Q5 How long have you smoked for? …10… years Tick here if you have smoked for less than a year

STOP: OutputDear Ms CameronThank you for taking the trouble to return the smoking questionnaire that we sent you. It appears from your answers that although you're not planning to stop smoking in the near future, you would like to stop if it was easy. You think it would be difficult to stop because smoking helps you cope with stress, it is something to do when you are bored, and smoking stops you putting on weight. However, you have reasons to be confident of success if you did try to stop, and there are ways of coping with the difficulties.

Summer School on Natural Language Processing and Text Mining 2008

Approaches

Template-based generation• Most common technique

• In simplest form, words fill in slots: “The train from Source to Destination will

leave platform number at time hours” Most common sort of NLG found in

commercial systems

Pros and Cons Pros

Conceptually simple

No specialized knowledge needed

Can be tailored to a domain with good performance

Cons

Not general

No variation in style – monotonous

Not scalable

Modern Approaches Rule Based approach

Machine Learning Approach

Summer School on Natural Language Processing and Text Mining 2008

Some Critical Issues

Context Sensitivity in Connected Sentences X-town was a blooming city. Yet, when the hooligans

started to invade the place, __________ . The place was not livable any more.

the place was abandoned by its population

the place was abandoned by them

the city was abandoned by its population

it was abandoned by its population

its population abandoned it……..

Referencing John is Jane’s friend. He loves to swim with

his dog in the pool. It is really lovely.

I am taking the Shatabdi Express tomorrow. It is a much better train than the Rajdhani Express. It has a nice restaurant car, while the other has nice seats.

ReferencingJohn stole the book from Mary, but he was

caught.

John stole the book from Mary, but the fool was caught.

AggregationThe dress was cheap.The dress was beautiful

The dress was cheap and beautifulThe dress was cheap yet beautiful

I found the boy. The boy was lost.I found the boy who was lost

I found the lost boy.

Sita bought a story book. Geeta bought a story book.

???? Sita and Geeta bought a story book.???? Sita bought a story book and Geeta

also bought a story book

Choice of words (Lexicalization)The bus was in time. The journey was fine.

The seats were bad.

The bus was in perfect time. The journey was fantastic. The seats were awful.

The bus was in perfect time. The journey was fantastic. However, the seats were not that good.

Summer School on Natural Language Processing and Text Mining 2008

General Architecture

Component Tasks in NLG Content Planning

=== Macroplanner Document Structuring

Sentence Planner === Microplanning Aggregation ; Lexicalization; Referring Expression Generation

Surface Form Realization Linguistic realization; Structure Realization

A Pipelined Architecture

Document Planning

Microplanning

Surface Realizatio

n

Document Plan

Text Specification

An ExampleConsider two assertions

has (Hotel_Bliss, food (bad))has (Hotel_Bliss, ambience (good))

Content Planning selects information orderingHotel Bliss has bad food but its ambience is good

Hotel Bliss has good ambience but its food is good

has (Hotel_Bliss, food (bad))Sentence Planning

choose syntactic templates choose lexicon bad or awful food or cuisine good or excellent Aggregate the two propositions

Generate referring expressionsIt or this restaurant

OrderingA big red ball OR A red big ball

Have

Entity Feature

Modifier

Subj Obj

Realizationcorrect verb inflection Have Has

may require noun inflection (not in this case) Articles required? Where? Conversion into final string Capitalization and Punctuation

Content Planning What to say

Data collection Making domain specific inferences Content selection Proposition formulation

Each proposition A clause Text structuring

Sequential ordering of propositions Specifying Rhetorical Relations

Content Planning Approaches Schema based (McKeown 1985)

Specify what information, in which order The schema is traversed to generate discourse

plan

Application of operators (similar to Rule Based approach) --- Hovy 93 The discourse plan is generated dynamically

Output is Content Plan Tree

Discourse

Demograph

Detailed view Summary

Name Age

Blood SugarCare

Group nodes

Content Plan Plan Tree Generation Ordering – of Group nodes Propositions

Rhetorical relations between leaf nodes

Paragraph and sentence boundaries

Rhetorical Relations

You should ...I’m in ... You can get ...The show ... It got a ...

MOTIVATION

MOTIVATION

EVIDENCE

ENABLEMENT

Rhetorical RelationsThree basic rhetorical relationships: SEQUENCE ELABORATION CONTRAST

Others like Justification Inference

Nucleus and Satellites

I love to collect classic cars

My favourite car is Toyota Innova

I drive my Maruti 800

Elaboration

Contrast

N

Target Text The month was cooler and drier than average, with the average number of rain days, but the total rain for the year so far is well below average. Although there was rain on every day for 8 days from 11th to 18th, rainfall amounts were mostly small.

Document Structuring in WeatherReporterThe Message Set:

MonthlyTempMsg ("cooler than average")MonthlyRainfallMsg ("drier than average")RainyDaysMsg ("average number of rain days")RainSoFarMsg ("well below average")RainSpellMsg ("8 days from 11th to 18th")RainAmountsMsg ("amounts mostly small")

Document Structuring in Weather Reporter

RainSoFarMsg

CONTRAST

RainAmounts

Msg

CONTRAST

ELABORATION

RainSpellMsg

RainyDaysMsg

ELABORATION

MonthlyTmpMsg

SEQUENCE

MonthlyRainfallMsg

Some Common RST Relationships Elaboration: The satellite presents more details about the

content of the nucleus

Contrast: The nuclei presents things, which are similar in some respects but different in some other relevant way. Multinuclear – no distinction bet. N and S

Purpose: S presents the goal of performing the activity presented in the nucleus

Condition: S presents something that must occur before the situation presented in N can occur

Result: N results from S

Planning Approach

Save Document

The system saves the document

Choose Save option

Select Folder

Type Filename

Click Save Button

A dialog box displayed

Dialog box closed

Planning OperatorName: Expand Purpose

Effect:(COMPETENT hearer(DO-ACTION ?action))

Constraints:(AND (get_all_substeps ?action ?subaction)

(NOT (singular list ?subaction))Nucleus:

(COMPETENT hearer (DO-SEQUENCE ?subaction))

Satellite:(((RST-PURPOSE (INFORM hearer (DO ?action)))

Expand SubactionsEffect:

(COMPETENT hearer (DO-SEQUENCE ?actions))

Constraints:NIL

Nucleus:(for each ?actions (RST-SEQUENCE

(COMPETENT hearer (DO-ACTION ?actions))))

Satellites:NIL

Purpose

Result

Choose Save Dialog

Box Opens

Choose Folder

Sequence

Discourse To save a file

1. Choose save option from file menu A dialog box will appear

2. Choose the folder 3. Type the file name 4. Click the Save button The system will save the document

Rhetorical Relations – Difficult to inferJohh abused the duckThe duck buzzed John

1. John abused the duck that had buzzed him

2. The duck buzzed John who had abused it3. The duck buzzed John and he abused it4. John abused the duck and it buzzed him

Summer School on Natural Language Processing and Text Mining 2008

On Clause Aggregation

Benefits of Aggregation Conciseness

Same information with fewer words

Cohesion We want a semantic unit – not a jumble of

disconnected phrases

Fluency Less effort to read Unambiguous and acc. to communication

conventions

Complex interactions Aggregation adds to fluency

The patient was admitted on Monday and released on Friday.

Someone ate apples. Someone ate oranges

Someone, who ate apples also ate oranges

Aggregation OperatorsCategory Operators Resources Surface

markersInterpretive Summarization

InferenceCommon sense knowledgeOntology

Referential Ref. expr. GenerationQuantified expression

Ontology Discourse

Each, all both some

Syntactic ParatacticHypotactic

Syntactic rulesLexicon

And, with, who, which

Lexical Paraphrasing Lexicon

InterpretiveJohn punched MaryMary kicked John => John fought with MaryJohn kicked Mary

Not always meaning preserving

Note use of Ontology

John kicked Mary + John punched Mary =/>

John fights with Mary

Referential Aggregation Reference Expression generation

The patient is Mary [name]. The patient is female [gender] The patient is 80 years old [age]. The patient has hypertension [med.history]

The patient is Mary. She is an 80 year old female. She has hypertension.

How much info in one sentence?

Reference ( Quantification) John is doing well Mary is doing well All the patients are

doing well

Note the use of background knowledge

The patient’s leftarm The patient’s right arm Each arm

Note the use of Ontology

Syntactic Aggregation Paratactic: Entities are of equal syntactic status

Ram likes Sita and Geeta

Main operator is co-ordinating conjunction

Hypotactic: Unequal statusNP modified by a PP

Ram likes Sita, who is a nurse

Lexical Aggregation In hypotactic aggregation, the satellite propositions are

modified.

The Maths score was 99.8% 99.8% is a record high score The Maths score was 99.8%, a record high score (apposition

modification)

The Maths score was a record high score of 99.8%

A dog used by police A police dog Rise sharply shoot Drop sharply plunge

Rhetorical Relations and HypotacticsUse of cue operatorsRR: ConcessionHe was fine He just had an accidentAlthough he had an accident he was fine

RR: EvidenceMy car is not Indian My car is a ToyotaMy car is not Indian because it is a Toyota

RR: ElaborationMy car is not Indian My car is expensiveMy expensive car is not Indian

Hypotactic Operators If propositions do not share any common entity, the

operator can simply join using cue phrase

N:Tom is feeling cold S:The window is open CauseTom is feeling cold because the window is open

If the linked propositions share common entities, the internals of the linked propositions undergo modifications

N: The child stopped hunger S: The child ate an apple [Purpose]

To stop hunger, the child ate an apple.

Two stage transformation:RR: EvidenceN: Tom was hungryS: Tom did not eat dinnerReplace Tom in N by ‘he’Apply Rule 1

Because Tom did not eat dinner, he was hungry

Corpus study to Rules [Example RR: Purpose N: Lift the cover S: Install battery]

% Example

To-infinitive 59.6 To install battery, lift the cover

For-Nominalization 7.5 Lift the cover for battery installation

For-Gerund 2.5 Lift the cover for installing battery

By-pupose 10 Install battery by lifting cover

So-Tat Purpose 8.4 Lift cover so battery can be installed

Syntactic constructions for realizing Elaboration relations

Verbosity M-direction Examples

R-Clause Short Before An apple which weighs 3 oz

Reduced R-Clause Shorter Before An apple weighing 3oz

PP Shorter Before An apple in the basket

Apposition Shortest Before An apple, a small fruit

Prenominalization Shortest After A 3 oz apple

Adjective Shortest After A dark red apple

Lexical Constraints Except for R-clause and Reduced R-clause, transforming a

proposition into an apposition, an adjective or a PP requires that the satellite proposition be of a specific syntactic type ( a noun, an adj or a PP respectively).

N: Jack is a runner.S: Jack is fast.

Jack is a fast runner

Fast and runner has a possible qualifying relationship.

Qualia Structure (Pustejovsky 91)

Constraints Linear Ordering

Paratactic Years 1998,1999 and 2000

Not Years 1999, 1998 and 2000

Hypotactic Uncommon orderings between premodifiers create

disfluencies A happy old man ---- An old happy man

Linear Ordering and Scope of ModifiersProblem when multiple modifiers modify the same noun Decide the order Avoid ambiguity

Ms. Jones is a patient of Dr. Smith, undergoing heart surgery

Old men and women should board firstWomen and old men should board first

Linear Ordering of Modifiers A simplex NP is a maximal noun phrase that includes pre-

modifiers such as determiners and possessives, but not post-nominals such as PPs and R-Cls.

A POS tagger along with FS grammar can be used to extract simples NPs.

A morphology module transforms plurals of nouns, comparative and superlative adjectives into their base form for frequency count.

Regular expression filter to remove concatenations of NPs Takeover bid last week Metformin 500 milligrams

Three stages of subsequent analysis Direct Evidence

Modifier sequences are transformed in ordered pairs Well known traditional brand name drug

Well known < traditional Well known < brand name traditional < brand name

Three possibilities A < B ; B< A; B=A (no order)

For n modifiers nC2 ordered pairs

Form a w X w matrix where w is the number of distinct modifiers.

Find Count[A,B] and Count[B,A]

For small corpus binomial distribution of one following the other is observed.

Transitivity

Again from corpus A < B and B< C ? A < CLong, boring and strenuous stretchLong strenuous lecture

Clustering: Formation of equivalence classes of words with same ordering with respect tp other premodifiers

John is a 74 year old hypertensive diabetic white male patient with a swollen mass in the left groin

John is a diabetic male white 74 year old hypertensive patient with a red swollen mass in the left groin

Other Constraints For conjunctions

John ate an apple and an orange (NP and NP) John ate in the morning and in the evening (PP and PP) X John ate an apple and in the evening (NP and PP)

Moral: Same syntactic category? John and a hammer broke the window ??? He is Nobel Prize winner and at the peak of his career.

Others: Adj phrase attachment, PP attachment etc.

Summer School on Natural Language Processing and Text Mining 2008

Conjunctions

Three interesting types John ate fish on Monday and rice on

Tuesday (non-constituent coordination)

John ate fish and Bill rice (gapping) Right node raising

John caught and Mary killed the spider

A Naïve Algorithm

1. Group propositions and order them according to similarities

1.I sold English books on Monday2.I sold Hindi books on Wednesday3.I sold onion on Monday4.I sold Bengali books on Monday((1,3,4),2) OR ((1,4),3,2) OR…..

2. Identify recurring elements

3. Determine sentence boundary

4. Delete redundant elements

Still Funny Scenarios The baker baked. The bread baked. The baker and the bread baked.

I don’t drink. I don’t chew tobacco. I don’t drink and chew tobacco.

==What should the constraints be?

Morphological Synthesis

Inflections depending on tense, aspect, mood, case, gender, number, person and familiarity.

A typical Bengali verb has 63 different inflected forms (120 if we consider the causative derivations)

Exceptions

Synthesis Approach

Classification of words based on Syllable structure [19 classes for Bengali verbs]

Paradigm tables for each of the classes

Table-driven modification of the words

Exceptions treated separately.

Different rules are used to inflect qualifiers and headwords

The rule to inflect proper noun as a headword in a particular SSU

IF (headword type = proper noun AND the SSU to which the headword belongs = kAke AND the last character of root word = ‘a’),

THENRule1: headword = headword + “ke”Rule1: headword = headword + “ke”rAma rAma rAmake rAmake

IF (Verb1==verb2 AND the Conjunction = Ebong AND SSU2 to which the headword belongs = kakhana AND the last character of root word = ‘a’)

THENRule1: headword = headword –’a’.Rule1: headword = headword –’a’.Rule2: headword = headword +’o’.Rule2: headword = headword +’o’.

Aaem gfkal bl /K/leClam ybL Aajo /Klb.Aaem gfkal bl /K/leClam ybL Aajo /Klb.Headword : Headword : Aaj + oAaj + o

Noun Morphology Synthesis

• Depends upon TAM option. Category Identification +Table lookup

Category Identification: Structure of root verb: X * VC * $. where: X= Any Character, V= vowel, C=constant and $ € { Ø, a, A, oYA }.

Verb Morphology Synthesis

ghumA [ghumAno]

(to sleep) u/au

so;oYA [so;oYAno]

(lie, causative)

tolA [tolAno]

(pick, causative)

tola [tolA]

(to pick)

so [so;oYA]

(to lie down) o

deoYA [deoYAno]

(give, causative)

dekhA

[dekhAno]

(to show)

dekha [dekhA]

(to see) e

ni~NrA [ni~NrAno] likha [lekhA]

(to write)

di [deoYA]

(to give) i

khAoYA [khAoYAno]

(to feed)

jAnA [jAnAno]

(to inform)

jAna [jAnA]

(to know)

khA [khAoYA]

(to eat) A

saoYA [saoYAno]

(undergo, causative)

karA[karAno]

(do, causative)

kara [karA]

(to do)

ha [haoYA]

(to happen) a

oYA A a* $

V

Table Look Up

The Table Lookup Stage:

i) Pr Present ii) Pa Pastiii) Sim Simpleiv) Per Perfectv) Co Continuousvi) Ind Indicative vii) Neg Negation.

Summer School on Natural Language Processing and Text Mining 2008

?Questions?