Arindam Bhattacharya - CSE, IIT Bombaycs621-2011/te.pdf · Arindam Bhattacharya M.Tech, Computer...

Textual Entailment

Arindam Bhattacharya

M.Tech, Computer ScienceIndian Institute of Technology, Bombay

November 9, 2011

Arindam (IITB) Textual Entailment November 9, 2011 1 / 59

Outline

1 IntroductionDefinitionEntailment TriggersRole of KnowledgeRTE ChallengesResources

2 General Strategy

3 Lexical Approach

4 Machine Learning Approach

5 Graphical Approach

6 Deep Semantic ApproachText Entailment using UNL

Outline

2 General Strategy

3 Lexical Approach

Outline

2 General Strategy

3 Lexical Approach

Definition

Classical Definition

A text t entails a hypothesis h if h is true in every circumstance (possibleworld) in which t is true.

Strict Entailment! Doesn’t account for real world uncertainties.

Example:

T: Ram was born and brought up in Maharashtra.H: Ram can speak Marathi.

Applied Definition

t entails h (t ⇒ h) if humans reading t will infer that h is most likely true.

Definition

Classical Definition

A text t entails a hypothesis h if h is true in every circumstance (possibleworld) in which t is true.

Strict Entailment! Doesn’t account for real world uncertainties.

Example:

T: Ram was born and brought up in Maharashtra.H: Ram can speak Marathi.

Applied Definition

t entails h (t ⇒ h) if humans reading t will infer that h is most likely true.

Probabilistic Interpretation

Applied definition sounds good.

But doesn’t sound concrete of mathematical.

Probabilistic interpretation

t probabilistically entails h if:

P(h is true | t) > P(h is true)

P(h is true |t) is called Entailment Confidence.

Probabilistic Interpretation

Applied definition sounds good.

But doesn’t sound concrete of mathematical.

Probabilistic interpretation

t probabilistically entails h if:

P(h is true | t) > P(h is true)

P(h is true |t) is called Entailment Confidence.

Figure: Textual Entailment

Outline

2 General Strategy

3 Lexical Approach

Entailment Triggers

Triggers are linguistic features that affect entailment [?].

Here are some examples to show how these various factors affectentailment.

Synonymy: Very common form of entailment trigger, where a word isreplaced by its synonym.

T: World War I began in 1914.H: World War I started in 1914.

Entailment Triggers

Hypernymy/Hyponymy: Certain concept can be either generalized orspecialized, leading to entailment.

T: Reptiles have scale.H: Snakes have scale. (Specialization or

Hyponymy)

T: Beckham plays football.H: Beckham plays a game. (Generalization or

Hypernymy)

Entailment Triggers

Co-reference: One of the main sources for text entailment. Especially withlong text containing paragraphs!

T: Barrack Obama came to India. TheAmerican President had a meeting withManmahon Singh.

H: Barrack Obama had a meeting withManmahon Singh.

Entailment Triggers

Modality/Polarity/Factive: Plays critical role in entailment as they affectthe degree of reliability on the remaining sentence. Especiallytroublesome for lexical approaches.

Modality denotes possibility or necessity and sometimes may lead towrong entailment. e.g. may, can, shall, must etc. aremodality triggers.

T: The government may approve theanti-corruption bill.

H: The government approved the anti-corruptionbill.

Entailment Triggers

Polarity determines whether the fact asserted or its negation is goingto occur. e.g. not, never, deny etc. are polarity triggers.

T: The watchman denied that he was sleeping.H: The watchman was sleeping.

Factivity deals with presupposition. It states a fact assuming anotherhas occurred. e.g. realize, regret etc. are factivity triggers.

T: Martha regrets eating John’s homemade cake.H: Martha ate John’s homemade cake.

Entailment Triggers

Passivization: In some case one of the text or hypothesis was is in activewhile the other is in passive. Subject and object of the mainverb gets reversed. Can only be handled by assigningsemantic roles to each entity.

T: Yahoo bought Overture.H: Overture was bought by Yahoo.

Entailment Triggers

Dropping or Inserting Adjunct: Adding or dropping adjuncts affectentailment based on which of T or H is modified, and thepolarity.

T: Bob was running quickly.H: Bob was running.T: Carl was eating.H: Carl was eating slowly. [Incorrect entailment]T: Alice was not driving.H: Alice was not driving fast.T: Derek was not writing properly.H: Derek was not writing. [Incorrect entailment]

Entailment Triggers

Protocols: Some common conventions such as mentioning birth-deathyear may trigger entailment.

T: Charles de Gaulle, 1890 – 1970, Frenchgeneral and statesman, was the first presidentof the Fifth Republic.

H: Charles de Gaulle died in 1970.

Entailment Triggers

Numerals: In some cases, certain level of numeric calculation affectsentailment.

T: 3 men and 2 women were found dead in theapartment.

H: 5 people were found dead in an apparent.

Outline

2 General Strategy

3 Lexical Approach

Role of Knowledge

Background knowledge is crucial in entailment as in any AIapplication!

Example

T: President of Russia visited Paris.

H: President of Russia visited France.

B: Paris is situated in France.

Background knowledge B alone should not entail the hypothesis Hand text T must contain necessary information (may not besufficient).

(T ∧ B) |= H

butB 2 H

Role of Knowledge

Example

(T ∧ B) |= H

butB 2 H

Role of Knowledge

Example

(T ∧ B) |= H

butB 2 H

Outline

2 General Strategy

3 Lexical Approach

Recognizing Textual Entailment Challenges

The recognizing textual entailment is an attempt to promote an abstractgeneric task that captures major semantic inference needs acrossapplications.

Held every year starting 2005.

RTE - 1,2 and 3 organized by PASCAL1.

Organized by Text Analysis Conference (TAC) since then.

Shifted focus to real world applications since RTE-5 (2009) ratherthan T-H pair entailment recognition.

1Pattern Analysis, Statistical Modeling and Computational Learning

RTE-7 2011

Main Task: Given a corpus and a set of ”candidate” sentencesretrieved by Lucene from that corpus, RTE systems are required toidentify all the sentences from among the candidate sentences thatentail a given Hypothesis.

each topic contains two sets of documents (“A” and “B”)Corpus is the set “A” and H is a sentence taken from “B”

Outline

2 General Strategy

3 Lexical Approach

Resources used for Textual Entailment

Resource Type Author Brief Description

WordNet LexicalDB

Princeton Univer-sity

DB of nouns, verbs, adjec-tives and adverbs

Verbnet LexicalDB

University of Col-orado, Boulder

Lexicon for English verbsorganized into classes ex-tending Levin (1993) classesthrough refinement and addi-tion of subclasses to achievesyntactic and semantic co-herence among members of aclass.

Roget’sThe-saurus

Thesaurus Peter Mark Roget Roget’s Thesaurus is awidely-used English the-saurus. The electronicedition (version 1.02) ismade available by Universityof Chicago.

Table: Knowledge Resources

Resources used for Textual Entailment

Resource Type Author Brief Description

DIRTPara-phraseCollec-tion

Collectionof para-phrases

University of Al-berta

DIRT (Discovery of InferenceRules from Text) knowledgecollection of paraphrasesfrom over a 1GB set ofnewspaper text.

TEASECollec-tion

Collectionof En-tailmentRules

Bar-Ilan University Output of the TEASE algo-rithm. Collection of severalentailment templates fromweb resources.

Table: Knowledge Resources

Outline

2 General Strategy

3 Lexical Approach

Sub-tasks

Recognizing textual entailment requires various sub-tasks such as:

Phrasal Verb Recognition

Named Entity Recognition

Semantic Role Labeling

An example illustrates the need for these tasks

Example

General Strategy

A general two-step strategy involves:

1 Representation of the information into a form that can be used bythe entailment algorithm

2 Entailment Recognition Algorithm that matches the text T alongwith knowledge B with hypothesis H

Representation

Raw Text T Re-representation φ(T )

Lexical

Syntactic

Semantic

Logical

Figure: Various Representations

The complexity of representation increases as we go higher.

Entailment Recognition

Text Hypothesis

Knowledge Base

⊆e ?

φ(T) φ(H

Figure: General Strategy

⊆e checks if the degree of subsumption of φ(H) with φ(T ) and φ(B)is over a certain threshold e

Outline

2 General Strategy

3 Lexical Approach

Lexical Approaches

Shallow Approaches: operate on surface level

Carries out some basic preprocessing

Does not compute elaborate representations

Make the entailment decision solely based on the lexical evidences

Preprocessing

Surface preprocessing includes:

tokenizationstemming/lemmatizationidentifying the stop words

Some systems does a bit deeper preprocessing such as:

Phrasal Verb Recognition e.g. take off, put onIdiom processing e.g. A Picture Paints a Thousand WordsNamed Entity Recognition and Normalization

Representation

Lexical approaches use on of following representation

Bag-of-words: Both T and H are represented as a set of words.n-grams: Sequence of n tokens are grouped together. Bag of words isan extreme case of n-gram, with n=1.

Example:“The fixed routine of a bedtime story before sleeping has arelaxing effect.”

Bag-of words: The, fixed, routine, of, a, bedtime, story, before,sleeping, has, relaxing, effectBigram model (n-gram with n=2): The fixed, fixed routine, routine of,of a, a bedtime, bedtime story, story before, before sleeping, sleepinghas, has a, a relaxing, relaxing effect

Example: LLM Algorithm

Local Lexical Matching (LLM) is a lexical approach for textentailment that uses bag of words representation

INPUT: Text T and Hypothesis H.OUTPUT: The matching score.for all word in T and H do

if word in stopWordList thenremove word ;

end ifif no words left in T or H then

return 0;end if

end fornumberMatched = 0;for all word WT in T do

LemmaT = Lemmatize(WT );for all word WH in H do

LemmaH = Lemmatize(WH );if LexicalCompare(LemmaH , LemmaT ) then

numberMatched + +;end if

end forend for

Figure: LLM Algorithm

LexicalCompare

The LexicalCompare() procedure is checks similarity with help ofWordNet.

if LemmaH == LemmaT thenreturn TRUE;

end ifif HypernymDistance(WH , WT ) ≤ dHyp then

return TRUE;end ifif MeronymDistance(WH , WT ) ≤ dMer then

return TRUE;end ifif MemberOfDistance(WH , WT ) ≤ dMem then

return TRUE;end ifif SynonymOf(WH , WT ) then

return TRUE;end if

Figure: Lexical Compare Procedure

Outline

2 General Strategy

3 Lexical Approach

Textual Entailment as a Classification Task

Figure: Text Entailment as a Classification Task

Feature Space

What could be a possible feature space? Most important decision!

Distance Features Features of some distance between T and H.Entailment Triggers Features that triggers entailment (or

non-entailment)Syntactic Feature Syntax of T-H pair modeled to exploit rewrite

rules.

Distance Features

Possible Features

Number of words in common.Longest common subsequnce.Longest common syntactic subtree.

Requires representation of T and H as

Bag-of words or n-gramsSyntactic representationSemantic Representation

Distance Features

Possible Features

Number of words in common.Longest common subsequnce.Longest common syntactic subtree.

Requires representation of T and H as

Bag-of words or n-gramsSyntactic representationSemantic Representation

Distance Features

For example:

T: At the end of the year, all solid companies pay dividends.H: At the end of the year, all solid insurance companies pay

dividends.

The above example, possible 〈feature, value〉 pair could be〈WordsInCommon, 11〉 or 〈LongestSubsequence, 8〉.

Entailment Triggers [?]

Capture presence of linguistic features that triggers entailment.

Example

T: The government may approve the anti-corruption bill.H: The government approved the anti-corruption bill.

A 〈feature, value〉 pair could be 〈modal , 1〉

Exploiting Re-write rules

How the rewrite rules are exploited is illustrated by following example.Consider the the pair:

T: Loki was killed by Thor.

H: Loki died.

Using the syntactic pair features we can learn rules such as:

Figure: Exploiting rewrite rules

Outline

2 General Strategy

3 Lexical Approach

Textual Entailment as Graph Matching

Convert hypothesis and text into graphs.

Either syntactic or semantic.

Measure similarity.

Similarity score gives entailment.

Different from Classical Graph Matching!

Scoring is not symmetric.

Node similarity can not be reduced to label level (i.e token level).

Consideration of linguistically motivated graph transformation(nominalization, passivization).

Text to Graph

Generation of dependency graph using a dependency parser

Graph edges are labeled by hand made rules (e.g. subj, amod)

Applying certain enhancements

Enhancements to Dependency Graph

Collapse Collocations and Named-Entities.Collocations: [blow] [off] → [blow off]Named entities: [Micheal] [Jackson] → [Micheal Jackson]

Dependency Folding so that certain dependencies (such asmodifying prepositions become labels).

Skeleton -[in]-> cupboard.

Enhancements to Dependency Graph

Semantic Role Labeling.

Arcs are labeled with Propbank style semantic roles.This helps to create links between words which share a deep semanticrelation not evident in the surface syntax.e.g. Pakistan got independence in [1947]Temporal .

Co-reference LinksUsing a co-reference resolution tagger, coref links are added throughoutthe graph.In the case of multiple sentence texts, it is our only “link” in the graphbetween entities in the two sentences.

Entailment Model

Entailment model determines the matching cost between graphs of Tand H.

The final cost is a linear combination of cost of matching vertices andedges.

Cost = α ∗ VertexCost + (1− α) ∗ EdgeCost

Additional Checks

Certain additional checks can be applied to the system to improve itsperformance [?]. They are listed below.

Negation Check: Check if there is a negation in a sentence.Example,

T:Clinton’s book is not a bestseller.H:Clinton’s book is a bestseller.

Factive Check: Non-factive verbs (claim, think, charged, etc.) incontrast to factive verbs (know, regret, etc.) have sententialcomplements which do not represent true propositions.

T:Clonaid claims to have cloned 13 babies worldwide.H:Clonaid has cloned 13 babies.

Additional Checks

Superlative Check: invert the typical monotonicity of entailment.Example,

T: The Osaka World Trade Center is the tallest building in WesternJapan.H: The Osaka World Trade Center is the tallest building in Japan.

Antonym Check: It is observed that the WordNet::Similaritymeasures gave high similarity to antonyms. Explicit check of whethera matching involved antonyms is done and unless one of the verticeshad a negation modifier, its rejected.

An Example

T: In 1994, Amazon.com was founded by Jeff Bezos.H: Bezos established a company.

VC = (0 + 0.4 + 0)/3 = 0.13

EC = 0 (isomorphic edges)

Cost = (0.55) ∗ (0.13) + (0.45) ∗ (0) = 0.0715 (let α = 0.55)

Outline

2 General Strategy

3 Lexical Approach

What is UNL?

tool for representing text in terms of semantic relation betweendifferent entities

consist of Universal Words (UW), Relations and Attributes

Example

Google goes public.

Google(icl>organization)

go(icl>do).@present.@entry

public(aoj>thing, ant>private)

agt obj

agt(go(icl>do, equ>travel, obj>thing).@present.@entry , Google(icl>organization))

obj(go(icl>do, equ>travel, obj>thing).@present.@entry , public(aoj>thing, ant>private))

Outline

2 General Strategy

3 Lexical Approach

The Approach [CS626-449: Lecture 29, Prasad Pradip Joshi]

Represent both text and hypothesis in their UNL form and do analysison the UNL expressions

List of atomic facts (predicates) emerging from the UNL graph of thehypothesis statement must be a subset (either explicitly or implicitly)of the atomic facts emerging from the UNL graph of the textstatement

The algorithm has two main parts:

Extending the set of atomic truths of the text graph based on thosewhich are present. (referred to as growth-rules)Carrying out the matching of the atomic facts in the hypothesis andthe text graph (referred to as matching-rules)

Illustration [CS626-449: Lecture 29, Prasad Pradip Joshi]

Text: Manmohan Singh along with president George Bush signed a letter in 2006.

Hypothesis: Bush signed a document.

Text representationagt(sign@entry@past,Manmohan Singh)

cag(sign@entry@past,President)

nam(President,George Bush)

obj(sign@entry@past,letter@indef)

tim(sign@entry@past,2006)

aoj(President,George Bush)

cag(sign@entry@past,George Bush)

Hypothesis Representationagt(sign@entry@past,Bush)

obj(sign@entry@past,document@indef)

Results

On the training set, (200 pairs of gold standard UNL from RTE andFRACAS) the precision value stands at 96.55% and the recall standsat 95.72%

Using UNL enconvertor (70.1% accurate), on phenomenon studiedFRACAS (100 pairs), precision is 63.04% and recall is 60.1%

On complete FRACAS dataset, precision 60.1% and recall 46%

Growth Rule [CS626-449: Lecture 29, Prasad Pradip Joshi]

pos-mod rule:

Presence of pos(A,B) add mod(A,B)Navy of India → Indian Navy

plc closure:

Presence of plc(A,B) and plc(B,C) leads to the addition of plc(A,C)Paris is capital of France. France is in Europe. → Paris is in Europe.

Introduction of words based on UNL relations and attributes:Attributes:

@end → ‘finish’ or ‘over’

Relations:

‘plc’ → ‘located’‘pos’ → ‘belongs to’ or ‘owned by’

Growth Rules [Maheshwari, 2009]

Figure: Growth Rules

Matching Rules [CS626-449: Lecture 29, Prasad Pradip Joshi]

Two types

Matching the UNL relations (predicate names)

Look up whether a relation belongs to the same family as othere.g. agt(agent),cag(co-agent),aoj(attribute of object)

Matching the argument part.

A narrowing edit of thing pointed to by ’aoj’A broadening edit of thing pointed to by ’obj’

Universal Words

Representation

<UW> := < integer > (<POS><WORDNETID>)

Natural Language:

<UW> := < root > [< suffix > ]

Example

Concept: a piece of furniture with tableware for a meal laid out on it

UNL Representation: 104379964

NL Representation : table(icl>furniture)

Relations

labeled arcs connecting a node to another node in a UNL graph

correspond to two-place semantic predicates holding between twoUniversal Words

used to describe semantic dependencies between syntacticconstituents

organized in a hierarchy where lower nodes subsume upper nodes

Relations

Example

Bob slept = agt(slept,Bob)

Alice died = obj(died,Alice)

John believes in Mary = aoj(believes,John)

John worked while Peter talked = coo(worked,talked)

Attributes

arcs linking a node to itself

In opposition to relations, they correspond to one-place predicates

used to represent information conveyed by natural languagegrammatical categories (such as tense, mood, aspect, number, etc)

Syntax

<attribute-name> := <character>+

Pair Features

Example

T: At the end of the year, all solid companies pay dividends.H: At the end of the year, all solid insurance companies pay dividends.

Possible feature pairs:Bag of words:

solidT

Hypothesis

solidH

We can learn:T implies H as when T contains “end”T does not imply H when H contains “end”

Totally useless???

Pair Features

Example

solidT

Hypothesis

solidH

Totally useless???

Pair Features

Example

solidT

Hypothesis

solidH

Totally useless???

Effectively using Pair Feature Space [?]

Example

T : At the end of the year, all solid companies pay dividends.H1: At the end of the year, all solid insurance companies pay dividends.H2: At the end of the year, all solid companies pay cash dividends.

Distance feature will plot < T ,H1 > and < T ,H2 > to be samepoints.

We need a space that considers the content and the structure oftextual entailment examples.

Example

Kernel Trick

Syntactic pair feature space.

Cross Pair Similarity

K (< T ′,H ′ >,< T ′′,H ′′ >) = K (< T ′,T ′′ >) + K (< H ′,H ′′ >)

defining the distance K (P1,P2) instead of features as

maxc∈C

(KT (t(H ′, c), t(H ′′, i)) + KT (t(T ′, c), t(T ′′, i)))

Makes Pair Feature look useful.

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