Search Engines & Question Answering Giuseppe Attardi Dipartimento di Informatica Università di...

Search Engines & Question Search Engines & Question AnsweringAnswering

Giuseppe AttardiGiuseppe Attardi

Dipartimento di InformaticaDipartimento di Informatica

Università di PisaUniversità di Pisa

Università di Pisa

Question AnsweringQuestion Answering

IR: find documents relevant to queryIR: find documents relevant to query– query: boolean combination of

keywordsQA: find answer to questionQA: find answer to question

– Question: expressed in natural language

– Answer: short phrase (< 50 byte)

Trec-9 Q&A trackTrec-9 Q&A track

693 fact-based, short answer questions693 fact-based, short answer questions– either short (50 B) or long (250 B) answer

~3 GB newspaper/newswire text (AP, WSJ, ~3 GB newspaper/newswire text (AP, WSJ, SJMN, FT, LAT, FBIS)SJMN, FT, LAT, FBIS)

Score: MRR (penalizes second answer)Score: MRR (penalizes second answer) Resources: top 50 (no answer for 130 q)Resources: top 50 (no answer for 130 q) Questions: 186 (Encarta), 314 (seeds from Questions: 186 (Encarta), 314 (seeds from

Excite logs), 193 (syntactic variants of 54 Excite logs), 193 (syntactic variants of 54 originals)originals)

CommonalitiesCommonalities

Approaches:Approaches:– question classification– finding entailed answer type– use of WordNet

High-quality document search High-quality document search helpful (e.g. Queen College)helpful (e.g. Queen College)

Sample QuestionsSample Questions

Q: Who shot President Abraham Lincoln?Q: Who shot President Abraham Lincoln?

A: John Wilkes BoothA: John Wilkes Booth

Q: How many lives were lost in the Pan Am crash in Lockerbie?Q: How many lives were lost in the Pan Am crash in Lockerbie?

A: 270A: 270

Q: How long does it take to travel from London to Paris through the Q: How long does it take to travel from London to Paris through the Channel?Channel?

A: three hours 45 minutesA: three hours 45 minutes

Q: Which Atlantic hurricane had the highest recorded wind speed?Q: Which Atlantic hurricane had the highest recorded wind speed?

A: Gilbert (200 mph)A: Gilbert (200 mph)

Q: Which country has the largest part of the rain forest?Q: Which country has the largest part of the rain forest?

A: Brazil (60%)A: Brazil (60%)

Question TypesQuestion Types

Class 1Class 1 Answer: single datum or list of itemsAnswer: single datum or list of items

C: who, when, where, how (old, much, large)C: who, when, where, how (old, much, large)

Class 2Class 2 A: multi-sentenceA: multi-sentence

C: extract from multiple sentencesC: extract from multiple sentences

Class 3Class 3 A: across several textsA: across several texts

C: comparative/contrastiveC: comparative/contrastive

Class 4Class 4 A: an analysis of retrieved informationA: an analysis of retrieved information

C: synthesized coherently from several retrieved C: synthesized coherently from several retrieved fragmentsfragments

Class 5Class 5 A: result of reasoningA: result of reasoning

C: word/domain knowledge and common sense C: word/domain knowledge and common sense reasoningreasoning

Question subtypesQuestion subtypes

Class 1.AClass 1.A About subjects, objects, manner, time or About subjects, objects, manner, time or locationlocation

Class 1.BClass 1.B About properties or attributesAbout properties or attributes

Class 1.CClass 1.C Taxonomic natureTaxonomic nature

Results (long)Results (long)

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

SMU

Queens

Wat

erlo

oIB

MLIM

SINTT IC

Pisa

MRRUnofficial

Falcon: ArchitectureFalcon: Architecture

Question

Question Semantic Form

ExpectedAnswer

TypeAnswer

Paragraphs

Answer Semantic Form

Answer

Answer Logical Form

Paragraph Index

Question ProcessingQuestion Processing Paragraph ProcessingParagraph Processing Answer ProcessingAnswer Processing

Paragraph filtering

Paragraph filtering

Collins Parser + NE Extraction


Abduction Filter

Abduction Filter

Coreference Resolution


Question Taxonomy

Question ExpansionWordNet



Question Logical Form

Question parseQuestion parse

Who was the first Russian astronaut to walk in space

WP VBD DT JJ NNP NP TO VB IN NN

NP NP

PP

VP

S

VP

S

Question semantic formQuestion semantic form

astronaut

walk space

Russianfirst

PERSON

first(x) astronaut(x) Russian(x) space(z) walk(y, z, x) PERSON(x)

Question logic form:Question logic form:

Answer type

Expected Answer TypeExpected Answer Type

size Argentina

dimension

QUANTITYWordNet

Question: Question: What is the size of Argentina?What is the size of Argentina?

Questions about definitionsQuestions about definitions

Special patterns:Special patterns:– What {is|are} …?– What is the definition of …?– Who {is|was|are|were} …?

Answer patterns:Answer patterns:– …{is|are}– …, {a|an|the}– … -

Question TaxonomyQuestion Taxonomy

Reason

Number

Manner

Location

Organization

Product

Language

Mammal

Currency

Nationality

Question

Game

Reptile

Country

City

Province

Continent

Speed

Degree

Dimension

Rate

Duration

Percentage

Count

Question expansionQuestion expansion

Morphological variantsMorphological variants– invented inventor

Lexical variantsLexical variants– killer assassin– far distance

Semantic variantsSemantic variants– like prefer

Indexing for Q/AIndexing for Q/A

Alternatives:Alternatives:– IR techniques– Parse texts and derive conceptual

indexesFalcon uses paragraph indexing:Falcon uses paragraph indexing:

– Vector-Space plus proximity– Returns weights used for abduction

Abduction to justify answersAbduction to justify answers

Backchaining proofs from questionsBackchaining proofs from questionsAxioms:Axioms:

– Logical form of answer– World knowledge (WordNet)– Coreference resolution in answer text

Effectiveness:Effectiveness:– 14% improvement– Filters 121 erroneous answers (of 692)– Requires 60% question processing time

TREC 13 QATREC 13 QA

Several subtasks:Several subtasks:– Factoid questions– Definition questions– List questions– Context questions

LCC still best performance, but LCC still best performance, but different architecturedifferent architecture

LCC Block ArchitectureLCC Block Architecture

PassageRetrieval

PassageRetrieval

Answer Extraction

Theorem Prover

Answer Justification

Answer Reranking

Axiomatic Knowledge Base

Answer Extraction

Theorem Prover


Answer Reranking


WordNetNER WordNetNER

DocumentRetrieval

DocumentRetrieval

Keywords Passages

Question Semantics

Captures the semantics of the questionSelects keywords for PR

Extracts and ranks passagesusing surface-text techniques

Extracts and ranks answersusing NL techniques

Q AQuestion Parse

Semantic Transformation

Recognition of

Expected Answer Type

Keyword Extraction

Question Parse


Recognition of


Keyword Extraction

Question Processing Answer Processing

Question ProcessingQuestion Processing

Two main tasksTwo main tasks– Determining the type of the answer– Extract keywords from the question and

formulate a query

Answer TypesAnswer Types

Factoid questions…Factoid questions…– Who, where, when, how many…– The answers fall into a limited and

somewhat predictable set of categories• Who questions are going to be answered

by… • Where questions…

– Generally, systems select answer types from a set of Named Entities, augmented with other types that are relatively easy to extract

Answer TypesAnswer Types

Of course, it isn’t that easy…Of course, it isn’t that easy…– Who questions can have organizations

as answers• Who sells the most hybrid cars?

– Which questions can have people as answers

• Which president went to war with Mexico?

Answer Type TaxonomyAnswer Type Taxonomy Contains ~9000 concepts reflecting expected Contains ~9000 concepts reflecting expected

answer typesanswer types Merges named entities with the WordNet hierarchyMerges named entities with the WordNet hierarchy

Answer Type DetectionAnswer Type Detection

Most systems use a combination of Most systems use a combination of hand-crafted rules and supervised hand-crafted rules and supervised machine learning to determine the machine learning to determine the right answer type for a question.right answer type for a question.

Not worthwhile to do something Not worthwhile to do something complex here if it can’t also be done complex here if it can’t also be done in candidate answer passages.in candidate answer passages.

Keyword SelectionKeyword Selection

Answer TypeAnswer Type indicates indicates whatwhat the the question is looking for:question is looking for:– It can be mapped to a NE type and used

for search in enhanced indexLexical terms (keywords) from the Lexical terms (keywords) from the

question, possibly expanded with question, possibly expanded with lexical/semantic variations provide lexical/semantic variations provide the required context.the required context.

Keyword ExtractionKeyword Extraction

Questions approximated by sets of Questions approximated by sets of unrelated keywordsunrelated keywords

Question (from TREC QA track)Question (from TREC QA track) KeywordsKeywords

Q002: Q002: What was the monetary value What was the monetary value of the Nobel Peace Prize in 1989?of the Nobel Peace Prize in 1989?

monetary, value, monetary, value, Nobel, Peace, PrizeNobel, Peace, Prize

Q003: Q003: What does the Peugeot What does the Peugeot company manufacture?company manufacture?

Peugeot, company, Peugeot, company, manufacturemanufacture

Q004: Q004: How much did Mercury spend How much did Mercury spend on advertising in 1993?on advertising in 1993?

Mercury, spend, Mercury, spend, advertising, 1993advertising, 1993

Q005: Q005: What is the name of the What is the name of the managing director of Apricot managing director of Apricot Computer?Computer?

name, managing, name, managing, director, Apricot, director, Apricot, ComputerComputer

Keyword Selection AlgorithmKeyword Selection Algorithm

1.1. Select all non-stopwords in quotationsSelect all non-stopwords in quotations2.2. Select all NNP words in recognized Select all NNP words in recognized

named entitiesnamed entities3.3. Select all complex nominals with their Select all complex nominals with their

adjectival modifiersadjectival modifiers4.4. Select all other complex nominalsSelect all other complex nominals5.5. Select all nouns with adjectival modifiersSelect all nouns with adjectival modifiers6.6. Select all other nounsSelect all other nouns7.7. Select all verbsSelect all verbs8.8. Select the answer type wordSelect the answer type word

Passage RetrievalPassage Retrieval

Extracts and ranks passagesusing surface-text techniques

PassageRetrieval

PassageRetrieval

Answer Extraction

Theorem Prover


Answer Reranking


Answer Extraction

Theorem Prover


Answer Reranking



DocumentRetrieval

DocumentRetrieval

Keywords Passages

Question Semantics

Q AQuestion Parse


Recognition of


Keyword Extraction

Question Parse


Recognition of


Keyword Extraction


Passage Extraction LoopPassage Extraction Loop

Passage Extraction ComponentPassage Extraction Component– Extracts passages that contain all selected keywords– Passage size dynamic– Start position dynamic

Passage quality and keyword adjustmentPassage quality and keyword adjustment– In the first iteration use the first 6 keyword selection

heuristics– If the number of passages is lower than a threshold

query is too strict drop a keyword– If the number of passages is higher than a threshold

query is too relaxed add a keyword

Passage ScoringPassage Scoring Passages are scored based on keyword windowsPassages are scored based on keyword windows

– For example, if a question has a set of keywords: {k1, k2, k3, k4}, and in a passage k1 and k2 are matched twice, k3 is matched once, and k4 is not matched, the following windows are built:

k1 k2 k3k2 k1

Window 1

k1 k2 k3k2 k1

Window 2

k1 k2 k3k2 k1

Window 3

k1 k2 k3k2 k1

Window 4

Passage ScoringPassage Scoring

Passage ordering is performed using Passage ordering is performed using a sort that involves three scores:a sort that involves three scores:– The number of words from the question

that are recognized in the same sequence in the window

– The number of words that separate the most distant keywords in the window

– The number of unmatched keywords in the window

Answer ExtractionAnswer Extraction

Extracts and ranks answersusing NL techniques

PassageRetrieval

PassageRetrieval

Answer Extraction

Theorem Prover


Answer Reranking


Answer Extraction

Theorem Prover


Answer Reranking



DocumentRetrieval

DocumentRetrieval

Keywords Passages

Question Semantics

Q AQuestion Parse


Recognition of


Keyword Extraction

Question Parse


Recognition of


Keyword Extraction


Ranking Candidate AnswersRanking Candidate Answers

Answer type: Person Text passage:

“Among them was Christa McAuliffe, the first private citizen to fly in space. Karen Allen, best known for her starring role in “Raiders of the Lost Ark”, plays McAuliffe. Brian Kerwin is featured as shuttle pilot Mike Smith...”

Q066: Name the first private citizen to fly in space.

Ranking Candidate AnswersRanking Candidate Answers

Answer type: Person Text passage:

“Among them was Christa McAuliffe, the first private citizen to fly in space. Karen Allen, best known for her starring role in “Raiders of the Lost Ark”, plays McAuliffe. Brian Kerwin is featured as shuttle pilot Mike Smith...”

Best candidate answer: Christa McAuliffe

Q066: Name the first private citizen to fly in space.

Features for Answer RankingFeatures for Answer Ranking

Number of question terms matched in the answer passageNumber of question terms matched in the answer passage Number of question terms matched in the same phrase as the Number of question terms matched in the same phrase as the

candidate answercandidate answer Number of question terms matched in the same sentence as Number of question terms matched in the same sentence as

the candidate answerthe candidate answer Flag set to 1 if the candidate answer is followed by a Flag set to 1 if the candidate answer is followed by a

punctuation signpunctuation sign Number of question terms matched, separated from the Number of question terms matched, separated from the

candidate answer by at most three words and one commacandidate answer by at most three words and one comma Number of terms occurring in the same order in the answer Number of terms occurring in the same order in the answer

passage as in the questionpassage as in the question Average distance from candidate answer to question term Average distance from candidate answer to question term

matchesmatches

Lexical ChainsLexical Chains

QuestionQuestion: When was the internal combustion engine : When was the internal combustion engine invented?invented?

AnswerAnswer: The first internal combustion engine was built in : The first internal combustion engine was built in 1867.1867.

Lexical chainsLexical chains::(1)(1) invent:v#1 invent:v#1 HYPERNIM HYPERNIM create_by_mental_act:v#1 create_by_mental_act:v#1

HYPERNIM HYPERNIM create:v#1 create:v#1 HYPONIM HYPONIM build:v#1 build:v#1

QuestionQuestion: How many chromosomes does a human zygote : How many chromosomes does a human zygote have?have?

AnswerAnswer: 46 chromosomes lie in the nucleus of every normal : 46 chromosomes lie in the nucleus of every normal human cell.human cell.

Lexical chainsLexical chains::(1)(1) zygote:n#1 zygote:n#1 HYPERNIM HYPERNIM cell:n#1 cell:n#1 HAS.PART HAS.PART

nucleus:n#1nucleus:n#1

Theorem ProverTheorem Prover

Q: What is the age of the solar system?QLF: quantity_at(x2) & age_nn(x2) & of_in(x2,x3) & solar_jj(x3) &

system_nn(x3)Question Axiom: (exists x1 x2 x3 (quantity_at(x2) & age_NN(x2) &

of_in(x2,x3) & solar_jj(x3) & system_nn(x3))Answer: The solar system is 4.6 billion years old.Wordnet Gloss: old_jj(x6) live_vb(e2,x6,x2) & for_in(e2,x1) &

relatively_jj(x1) & long_jj(x1) & time_nn(x1) & or_cc(e5,e2,e3) & attain_vb(e3,x6,x2) & specific_jj(x2) & age_nn(x2)

Linguistic Axiom: all x1 (quantity_at(x1) & solar_jj(x1) & system_nn(x1) of_in(x1,x1))

Proof: ¬quantity_at(x2) | ¬age_nn(x2) | ¬of_in(x2,x3) | ¬solar_jj(x3) | ¬system_nn(x3)

Refutation assigns value to x2

Is the Web Different?Is the Web Different?

In TREC (and most commercial In TREC (and most commercial applications), retrieval is performed applications), retrieval is performed against a smallish closed collection of against a smallish closed collection of texts.texts.

The diversity/creativity in how people The diversity/creativity in how people express themselves necessitates all express themselves necessitates all that work to bring the question and the that work to bring the question and the answer texts together.answer texts together.

But…But…

The Web is DifferentThe Web is Different

On the Web popular factoids are On the Web popular factoids are likely to be expressed in a gazillion likely to be expressed in a gazillion different ways.different ways.

At least a few of which will likely At least a few of which will likely match the way the question was match the way the question was asked.asked.

So why not just grep (or agrep) the So why not just grep (or agrep) the Web using all or pieces of the Web using all or pieces of the original question.original question.

AskMSRAskMSR

Process the question by…Process the question by…– Forming a search engine query from the

original question– Detecting the answer type

Get some resultsGet some resultsExtract answers of the right type Extract answers of the right type

based onbased on– How often they occur

Step 1: Rewrite the questionsStep 1: Rewrite the questions

Intuition: The user’s question is Intuition: The user’s question is often syntactically quite close to often syntactically quite close to sentences that contain the answersentences that contain the answer

– Where is the Louvre Museum located? • The Louvre Museum is located in Paris

– Who created the character of Scrooge?• Charles Dickens created the character of

Scrooge.

Query rewritingQuery rewriting

Classify question into seven categoriesClassify question into seven categories

– Who is/was/are/were…?– When is/did/will/are/were …?– Where is/are/were …?

a. Hand-crafted category-specific transformation rulesa. Hand-crafted category-specific transformation rulese.g.: For e.g.: For wherewhere questions, move ‘is’ to all possible questions, move ‘is’ to all possible locationslocations

Look to the Look to the rightright of the query terms for the of the query terms for the answer.answer.

““Where Where isis the Louvre Museum located?” the Louvre Museum located?” ““isis the Louvre Museum located” the Louvre Museum located” ““the the isis Louvre Museum located” Louvre Museum located” ““the Louvre the Louvre isis Museum located” Museum located” ““the Louvre Museum the Louvre Museum isis located” located” ““the Louvre Museum located the Louvre Museum located isis””

Step 2: Query search engineStep 2: Query search engine

Send all rewrites to a Web search Send all rewrites to a Web search engineengine

Retrieve top N answers (100-200)Retrieve top N answers (100-200)For speed, rely just on search For speed, rely just on search

engine’s “snippets”, not the full text engine’s “snippets”, not the full text of the actual documentof the actual document

Step 3: Gathering N-GramsStep 3: Gathering N-Grams

Enumerate all N-grams (N=1,2,3) in all Enumerate all N-grams (N=1,2,3) in all retrieved snippetsretrieved snippets

Weight of an n-gram: occurrence count, Weight of an n-gram: occurrence count, each weighted by “reliability” (weight) of each weighted by “reliability” (weight) of rewrite rule that fetched the documentrewrite rule that fetched the document– Example: “Who created the character of

Scrooge?”Dickens 117Christmas Carol 78Charles Dickens 75Disney 72Carl Banks 54A Christmas 41Christmas Carol 45Uncle 31

Step 4: Filtering N-GramsStep 4: Filtering N-Grams

Each question type is associated Each question type is associated with one or more “data-type filters” = with one or more “data-type filters” = regular expressions for answer typesregular expressions for answer types

Boost score of n-grams that match the Boost score of n-grams that match the expected answer type.expected answer type.

Lower score of n-grams that don’t Lower score of n-grams that don’t match.match.

Step 5: Tiling the AnswersStep 5: Tiling the Answers

Dickens

Charles Dickens

Mr Charles

Scores

20

15

10

merged, discardold n-grams

Mr Charles DickensScore 45

ResultsResults

Standard TREC contest test-bed Standard TREC contest test-bed (TREC 2001): 1M documents; 900 (TREC 2001): 1M documents; 900 questionsquestions– Technique does ok, not great (would have

placed in top 9 of ~30 participants)– But with access to the Web… they do

much better, would have come in second on TREC 2001

Harder QuestionsHarder Questions

Factoid question answering is really Factoid question answering is really pretty silly.pretty silly.

A more interesting task is one where A more interesting task is one where the answers are fluid and depend on the answers are fluid and depend on the fusion of material from disparate the fusion of material from disparate texts over time.texts over time.– Who is Condoleezza Rice?– Who is Mahmoud Abbas?– Why was Arafat flown to Paris?

IXE ComponentsIXE Components

IXE FrameworkIXE Framework

Object Store

Indexer

OS Abstraction Text

MaxEntropy

Sent. Splitter

Readers

POS Tagger

NE Tagger

Passage Index

Clustering

Crawler Search

Web Service WrappersUnicodeRegExp

TokenizerSuffix Trees

FilesMem Mapping

ThreadsSynchronization

PythonPerlJava

EventStreamContextStream

GIS

Language Processing ToolsLanguage Processing Tools

Maximum Entropy classifierMaximum Entropy classifierSentence SplitterSentence SplitterMulti-language POS TaggerMulti-language POS TaggerMulti-language NE TaggerMulti-language NE TaggerConceptual clusteringConceptual clustering

Maximum EntropyMaximum Entropy

Machine Learning approach to classification:Machine Learning approach to classification:– System trained on test cases– Learned model used for predictions

Classification problem described as a number of Classification problem described as a number of featuresfeatures

Each feature corresponds to a constraint on the Each feature corresponds to a constraint on the modelmodel

Maximum entropy model: the model with the Maximum entropy model: the model with the maximum entropy of all the models that satisfy maximum entropy of all the models that satisfy the constraintsthe constraints

Choosing a model with less entropy, would add Choosing a model with less entropy, would add ‘information’ constraints not justified by the ‘information’ constraints not justified by the empirical evidence availableempirical evidence available

MaxEntropy: example dataMaxEntropy: example data

Features Outcome

Sunny, Happy Outdoor

Sunny, Happy, Dry Outdoor

Sunny, Happy, Humid Outdoor

Sunny, Sad, Dry Outdoor

Sunny, Sad, Humid Outdoor

Cloudy, Happy, Humid Outdoor

Cloudy, Happy, Humid Outdoor

Cloudy, Sad, Humid Outdoor

Cloudy, Sad, Humid Outdoor

Rainy, Happy, Humid Indoor

Rainy, Happy, Dry Indoor

Rainy, Sad, Dry Indoor

Rainy, Sad, Humid Indoor

Cloudy, Sad, Humid Indoor

Cloudy, Sad, Humid Indoor

MaxEnt: example predictionsMaxEnt: example predictions

ContextContext OutdoorOutdoor IndoorIndoor

Cloudy, Happy, HumidCloudy, Happy, Humid 0.7710.771 0.2280.228

Rainy, Sad, HumidRainy, Sad, Humid 0.0010.001 0.9980.998

MaxEntropy: applicationMaxEntropy: application

Sentence SplittingSentence Splitting Not all punctuations are sentence Not all punctuations are sentence

boundaries:boundaries:– U.S.A.– St. Helen– 3.14

Use features like:Use features like:– Capitalization (previous, next word)– Present in abbreviation list– Suffix/prefix digits– Suffix/prefix long

Precision: > 95%Precision: > 95%

Part of Speech TaggingPart of Speech Tagging

TreeTagger: statistic package based TreeTagger: statistic package based on HMM and decision treeson HMM and decision trees

Trained on manually tagged textTrained on manually tagged textFull language lexicon (with all Full language lexicon (with all

inflections: 140.000 words for Italian)inflections: 140.000 words for Italian)

Training CorpusTraining Corpus

IlIl DET:def:*:*:masc:sgDET:def:*:*:masc:sg _il_ilpresidentepresidente NOM:*:*:*:masc:sgNOM:*:*:*:masc:sg _presidente_presidentedelladella PRE:det:*:*:femi:sgPRE:det:*:*:femi:sg _del_delRepubblicaRepubblica NOM:*:*:*:femi:sgNOM:*:*:*:femi:sg _repubblica_repubblicafrancesefrancese ADJ:*:*:*:femi:sgADJ:*:*:*:femi:sg _francese_franceseFrancoisFrancois NPR:*:*:*:*:*NPR:*:*:*:*:* _Francois_FrancoisMitterrandMitterrand NPR:*:*:*:*:*NPR:*:*:*:*:* _Mitterrand_Mitterrandhaha VER:aux:pres:3:*:sgVER:aux:pres:3:*:sg _avere_averepropostoproposto VER:*:pper:*:masc:sgVER:*:pper:*:masc:sg _proporre_proporre……

Named Entity TaggerNamed Entity Tagger

Uses MaxEntropyUses MaxEntropy NE categories:NE categories:

– Top level: NAME, ORGANIZATION, LOCATION, QUANTITY, TIME, EVENT, PRODUCT

– Second level: 30-100. E.g. QUANTITY:• MONEY, CARDINAL, PERCENT,

MEASURE, VOLUME, AGE, WEIGHT, SPEED, TEMPERATURE, ETC.

See resources at CoNLL See resources at CoNLL (cnts.uia.ac.be/connl2004)(cnts.uia.ac.be/connl2004)

NE FeaturesNE Features

Feature types:Feature types:– word-level (es. capitalization, digits, etc.)– punctuation– POS tag– Category designator (Mr, Av.)– Category suffix (center, museum, street, etc.) – Lowercase intermediate terms (of, de, in)– presence in controlled dictionaries (locations,

people, organizations)

Context: words in position -1, 0, +1Context: words in position -1, 0, +1

Sample training documentSample training document

<TEXT> Today the <ENAMEX TYPE='ORGANIZATION'>Dow Jones</ENAMEX>

industrial average gained <NUMEX TYPE='MONEY'>thirtyeight and three quarter points</NUMEX>.

When the first American style burger joint opened in <ENAMEX TYPE='LOCATION'>London</ENAMEX>'s fashionable <ENAMEX TYPE='LOCATION'>Regent street</ENAMEX> some <TIMEX TYPE='DURATION'>twenty years</TIMEX> ago, it was mobbed.

Now it's <ENAMEX TYPE='LOCATION'>Asia</ENAMEX>'s turn.</TEXT><TEXT> The temperatures hover in the <NUMEX

TYPE='MEASURE'>nineties</NUMEX>, the heat index climbs into the <NUMEX TYPE='MEASURE'>hundreds</NUMEX>.

And that's continued bad news for <ENAMEX TYPE='LOCATION'>Florida</ENAMEX> where wildfires have charred nearly <NUMEX TYPE='MEASURE'>three hundred square miles</NUMEX> in the last <TIMEX TYPE='DURATION'>month</TIMEX> and destroyed more than a <NUMEX TYPE='CARDINAL'>hundred</NUMEX> homes.

</TEXT>

ClusteringClustering

Classification: assign an item to one Classification: assign an item to one among a among a givengiven set of classesset of classes

Clustering: find groupings of similar Clustering: find groupings of similar items (i.e. items (i.e. generate the classesgenerate the classes))

Conceptual Clustering of resultsConceptual Clustering of results

Similar to VivisimoSimilar to Vivisimo– Built on the fly rather than from– Predefined categories (Northern Light)

Generalized suffix tree of snippetsGeneralized suffix tree of snippetsStemmingStemmingStop words (articulated, essential)Stop words (articulated, essential)Demo: Demo: pythonpython, , upnpupnp

PiQASso: Pisa Question Answering PiQASso: Pisa Question Answering SystemSystem

““Computers are useless, they can Computers are useless, they can only give answers”only give answers”

Pablo PicassoPablo Picasso

PiQASso ArchitecturePiQASso Architecture

SentenceSplitter

SentenceSplitter

IndexerIndexer

QueryFormulation/Expansion

QueryFormulation/Expansion

WordNet

MiniParMiniPar

?

Documentcollection

MiniParMiniPar

TypeMatching

TypeMatching

RelationMatchingRelationMatching

Answer Pars

AnswerScoringAnswerScoring

PopularityRanking

PopularityRanking

Answer

found?

Answer

found?

Answer

Questionanalysis

Answer analysis

WNSenseWNSense

QuestionClassification

QuestionClassification

Linguistic toolsLinguistic tools

• extracts lexical knowledge from WordNet

• classifies words according to WordNet top-level categories, weighting its senses

• computes distance between words based on is-a links

• suggests word alternatives for query expansion

What metal has the highest melting point?

subj lex-mod

obj

mod

WNSense Minipar [D. Lin]

Example: Theatre

Categorization: artifact 0.60, communication 0.40

Synonyms: dramaturgy, theater, house, dramatics

• Identifies dependency relations between words (e.g. subject, object, modifiers)

• Provides POS tagging

• Detects semantic types of words (e.g. location, person, organization)

• Extensible: we integrated a Maximum Entropy based Named Entity Tagger

Question AnalysisQuestion AnalysisWhat metal has the highest melting point?

metal, highest, melting, point

2. Keyword extraction

1. Parsing

3. Answer type detection

SUBSTANCE

4. Relation extraction

<SUBSTANCE, has, subj><point, has, obj><melting, point, lex-mod><highest, point, mod>

1. NL question is parsed

2. POS tags are used to select search keywords

3. Expected answer type is determined applying heuristic rules to the dependency tree

4. Additional relations are inferred and the answer entity is identified

What metal has the highest melting point?

subj lex-mod

obj

mod

Answer AnalysisAnswer Analysis

Tungsten is a very dense material and has the highest melting point of any metal.

1 Parsing

………….

2 Answer type check 3 Relation extraction

SUBSTANCE<tungsten, material, pred><tungsten, has, subj><point, has, obj>…

4 Matching Distance

Tungsten

6 Popularity Ranking

ANSWER

1. Parse retrieved paragraphs

2. Paragraphs not containing an entity of the expected type are discarded

3. Dependency relations are extracted from Minipar output

4. Matching distance between word relations in question and answer is computed

5. Too distant paragraphs are filtered out

6. Popularity rank used to weight distances

5 Distance Filtering

Match Distance between Question and AnswerMatch Distance between Question and Answer

Analyze relations between corresponding words considering:

number of matching words in question and in number of matching words in question and in answeranswer

distance between words. Ex: distance between words. Ex: moonmoon matching matching with with satellitesatellite

relation types. Ex: words in the question relation types. Ex: words in the question related by related by subjsubj while the matching words in while the matching words in the answer related by the answer related by predpred

http://medialab.di.unipi.it/askpiqasso.htmlhttp://medialab.di.unipi.it/askpiqasso.html

Improving PIQASsoImproving PIQASso

More NLPMore NLP

NLP techniques largely NLP techniques largely unsuccessful at information retrievalunsuccessful at information retrieval– Document retrieval as primary measure

of information retrieval success• Document retrieval reduces the need for

NLP techniques– Discourse factors can be ignored– Query words perform word-sense

disambiguation

– Lack of robustness:• NLP techniques are typically not as robust

as word indexing

How these technologies help?How these technologies help?

Question AnalysisQuestion Analysis– The tag of the predicted category is added to

the query Named-Entity Detection:Named-Entity Detection:

– The NE categories found in text are included as tags in the index

What party is John Kerry in? (ORGANIZATION)

John Kerry defeated John Edwards in the primaries for the Democratic Party.

Tags: PERSON, ORGANIZATION

NLP TechnologiesNLP Technologies

Coreference Relations:Coreference Relations:– Interpretation of a paragraph may

depend on the context in which it occurs

Description Extraction:Description Extraction:– Appositive and predicate nominative

constructions provide descriptive terms about entities

Represented as annotations Represented as annotations associated to words, i.e. words in the associated to words, i.e. words in the same position as the referencesame position as the reference

Coreference RelationsCoreference Relations

How long was Margaret Thatcher the prime minister? (DURATION)

The truth, which has been added to over each of her 11 1/2 years in power, is that they don't make many like her anymore.Tags: DURATIONColocated: her, MARGARET THATCHER

Description ExtractionDescription Extraction

Identifies Identifies DESCRIPTIONDESCRIPTION category categoryAllows descriptive terms to be used Allows descriptive terms to be used

in term expansionin term expansion

Famed architect Frank Gary…

Tags: DESCRIPTION, PERSON, LOCATION

Buildings he designed include the Guggenheim Museum in Bilbao.

Colocation: he, FRANK GARY

Who is Frank Gary? (DESCRIPTION) What architect designed the Guggenheim Museum in Bilbao? (PERSON)

NLP TechnologiesNLP Technologies

Question Analysis:Question Analysis:– identify the semantic type of the

expected answer implicit in the queryNamed-Entity Detection:Named-Entity Detection:

– determine the semantic type of proper nouns and numeric amounts in text

Will it work?Will it work?

Will these semantic relations Will these semantic relations improve paragraph retrieval?improve paragraph retrieval?– Are the implementations robust enough

to see a benefit across large document collections and question sets?

– Are there enough questions where these relationships are required to find an answer?

Hopefully yes!Hopefully yes!

PreprocessingPreprocessing

Paragraph DetectionParagraph DetectionSentence DetectionSentence DetectionTokenizationTokenizationPOS TaggingPOS TaggingNP-ChunkingNP-Chunking

Queries to a NE enhanced indexQueries to a NE enhanced index

text matches bushtext matches bush

text matches PERSON:bushtext matches PERSON:bush

text matches LOCATION:* & PERSON: text matches LOCATION:* & PERSON: bin-ladenbin-laden

text matches DURATION:* text matches DURATION:* PERSON:margaret-thatcher prime-PERSON:margaret-thatcher prime-ministerminister

CoreferenceCoreference

Task:Task:– Determine space of entity extents:

• Basal noun phrases:– Named entities consisting of multiple basal

noun phrases are treated as a single entity

• Pre-nominal proper nouns• Possessive pronouns

– Determine which extents refer to the same entity in the world

Paragraph RetrievalParagraph Retrieval

Indexing:Indexing:– add NE tags for each NE category

present in the text– add coreference relationships– Use syntactically-based categorical

relations to create a DESCRIPTION category for term expansion

– Use IXE passage indexer

High ComposabilityHigh Composability

DocInfoDocInfo

PassageDocPassageDoc

Collection<DocInfo>Collection<DocInfo>

Collection<PassageDoc>Collection<PassageDoc>namedatesize

namedatesize

textboundaries

textboundaries QueryCursorQueryCursor

PassageQueryCursorPassageQueryCursor

next()next()

next()next()

CursorCursor

next()next()

Tagged DocumentsTagged Documents

QueryCursorQueryCursor

QueryCursorTaggedWordQueryCursorTaggedWord

QueryCursorWordQueryCursorWord

select documents where select documents where – text matches bush– text matches PERSON:bush– text matches osama & LOCATION:*

CombinationCombination

Searching passages on a collection Searching passages on a collection of tagged documentsof tagged documents

PassageQueryCursor<Collection<TaggedDoc>>PassageQueryCursor<Collection<TaggedDoc>>

QueryCursor<Collection>QueryCursor<Collection>

Paragraph RetrievalParagraph Retrieval

Retrieval:Retrieval:– Use question analysis component to

predict answer category and append it to the question

– Evaluate using TREC questions and answer patterns

• 500 questions

System OverviewSystem Overview

NE Recognizer


Documents

IXE Search

Question Analysis

Question

Paragraphs

Description Extraction

Paragraphs+

Sent. Splitter

POS tagger

Paragr. Splitter

Tokenization

IXE indexer

Indexing Retrieval

ConclusionConclusion

QA is a challenging taskQA is a challenging task Involves state of the art techniques Involves state of the art techniques

in various fields:in various fields:– IR– NLP– AI– Managing large data sets– Advanced Software Technologies

Date post:	16-Dec-2015
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