question answering gideon mann johns hopkins university gsm@cs.jhu.edu

Question Answering

Gideon Mann

Johns Hopkins University

gsm@cs.jhu.edu

Information Retrieval Tasks

Retired General Wesley Clark

How old is General Clark?

How long did Clark serve in the military?

Will Clark run for President?

Ad-Hoc Queries Prior work has been concerned mainly

with answering ad-hoc queries :

General Clark Typically a few words long, not an

entire question What is desired is general information

about the subject in question

Answering Ad-Hoc Queries Main focus of Information Retrieval past 2-3

decades Solution(s) :

– Vector-based methods– SVD, query expansion, language modeling– Return a page as an answer

Resulting systems Extremely Useful– Google, Altavista

Traditional IR

Query

Document Collection

Document Ranking

Document retrieval

But not all queries are Ad-Hoc!

How old is General Clark?

Does not fit well into an Ad-hoc paradigm– “How” and “is” are not relevant for appropriate

retrieval– Potentially useful cues in the question are

ignored in traditional ad-hoc retrieval system

Documents are not Facts Traditional IR systems return Pages

– Useful when only a vague information need has been identified

Insufficient when a fact is desired:– How old is General Clark? 58– How long did Clark serve in the

mililary? 36 years– Will Clark run for president? Maybe

Question Answering as Retrieval

Given a document collection and a question:

A question answering system should retrieve a short snippet of text which exactly answer the question asked.

Question Answering

Query

Document Collection

Document Ranking

Document retrieval

Ranked Answers

Answer Extraction(Sentence ranking)

QA as a Comprehension Task

For perfect recall, the answer only has to appear once in the collection.

In essence, this forces the QA system to function as a text understanding system

Thus QA may be interesting, not only for retrieval, but also to test understanding

QA as a stepping stone Current QA focused on Fact extraction

– Answers appear verbatim in text

How old is General Clark? How can we answer questions which don’t

appear exactly in the text?How long has Clark been in the military?

Will Clark run for President? Maybe build on low-level QA extracted facts

QA Methods

Two Main Categories of Methods for Question Answering

– Answer Preference Matching– Answer Context Matching

Lecture Outline1. Answer Preferences

Question Analysis Type identification Learning Answering Typing

2. Answer ContextLearning Context Similarity Alignment Surface Text Patterns

Answer Type Identification

From the question itself infer the likely type of the answer• How old is General Clark?

How Old

• When did Clark retire? When

• Who is the NBC war correspondent? Who

NASSLI!

April 12 Deadline –– Could be extended….– Mail hale@jhu.edu to ask for more time

Answer Type Identification

From the question itself infer the likely type of the answer• How old is General Clark?

How Old Age

• When did Clark retire? When Date

• Who is the NBC war correspondent? Correspondent Person

Wh-Words

Who Person, Organization, Location

When Date, Year

Where Location

In What Location

What ??

Difficult to Enumerate All Possibilities Though

What is the service ceiling for a PAC750?

WordNet

wingspan

length

diameter radius altitude

ceiling

WordNet For Answer Typing

wingspan

length

diameter radius altitude

ceiling

NUMBER

What is the service ceiling for a PAC750?

Lecture Outline1. Answer Preferences

Question Analysis Type identification Learning Answering Typing

2. Answer ContextLearning Context Similarity Alignment Surface Text Patterns

Answer Typing gives the Preference…

From Answer Typing, we have the preferences imposed by the question

But in order to use those preferences, we must have a way to detect potential candidate answers

Some are Simple…

Number [0-9]+ Date ($month) ($day) ($year) Age 0 – 100

… Others Complicated Who shot Martin Luther King?

– Person preferenceRequires a Named Entity Identifier

Who saved Chrysler from bankruptcy?– Not just confined to people…– Need a Tagger to identify appropriate candidates

Use WordNet for Type Identification

“What 20th century poet wrote Howl?”

writer

poet

Ginsburg FrostRilke

Candidate Set

communicator

Simple Answer Extraction

How old is General Clark?

Age

General Clark, from Little Rock, Arkansas, turns 58 after serving

36 years in the service, this December 23, 2002.

General Clark, from Little Rock, Arkansas, turns 58 after serving

36 years in the service, this December 23, 2002.

Age Tagger

Lecture Outline1. Answer Preferences

Question Analysis Type identification Learning Answering Typing

2. Answer ContextLearning Context Similarity Alignment Surface Text Patterns

Learning Answer Typing What is desired is a model which

predicts P(type|question) Usually a variety of possible types

– Who Person (“Who shot Kennedy?” Oswald) Organization (“Who rescued Chrysler from

bankruptcy?” The Government) Location (“Who won the Superbowl?” New

England)

What training data? Annotated Questions

– “Who shot Kennedy” [PERSON] Problems :

– Expensive to annotate– Must be redone, every time the tag set is

devised

Trivia Questions! Alternatively, use unannotated Trivia

Questions– Q: “Who shot Kennedy”– A: Lee Harvey Oswald

Run your Type-Tagger over the answers, to get tags– A: Lee Harvey Oswald [ PERSON]

MI Model From tags, you can build a MI model

– Predict from the question head-word MI(Question Head Word, Type Tag)

= P(Type Tag | QuestionHeadWord)

---------------------------------------------

P(Type Tag)

– From this you can judge the fit of a question/word pair

– (Mann 2001)

MaxEnt Model Rather than just use head word alone train

on the entire set of words, and build a Maximum Entropy model to combine features suggested by the entire phrase

“What was the year in which Castro was born?”

(Ittycheriah et al. 2001)

Maybe you don’t even need training data!

Looking at occurrences of words in text, look at what types occur next to them

Use these co-occurrence statistics to determine appropriate type of answer

(Prager et al. 2002)

Lecture Outline1. Answer Preferences

Question Analysis Type identification Learning Answering Typing

2. Answer ContextLearning Context Similarity Alignment Surface Text Patterns

Is Answer Typing Enough?

Even when you’ve found the correct sentence, and know the type of the answer a lot of ambiguity in the answer still remains

Some experiments show that in every sentence, around 2/3 choices of appropriate type for a sentence which answers a question

For high precision systems, this is unacceptable

Answer Context

Who shot Martin Luther King?

Answer Preference Answer Context

Using Context Many systems simply look for an

answer of the correct type in a context which seems appropriate– Many matching keywords– Perhaps using query expansion

Another alternative If the question is “Who shot Kennedy”

Search for all exact phrases matches “X shot Kennedy”

And simple alternations “Kennedy was shot by X”

(Brill et al. 2001)

Beyond… The first step beyond simple keyword

matching, is to use relative position information

One way of doing this is to use alignment information

Lecture Outline1. Answer Preferences

Question Analysis Type identification Learning Answering Typing

2. Answer ContextLearning Context Similarity Alignment Surface Text Patterns

Local Alignment

Who shot Kennedy?

Jack assassinated Oswald, the man who shot Kennedy, and was Mrs. Ruby’s Husband.

Three Potential Candidates by type

Local Alignment

Who shot Kennedy?

Jack assassinated Oswald, the man who shot Kennedy, and was Mrs. Ruby’s Husband.

Matching Context

Question Head word

Local Alignment

Who shot Kennedy?

Jack assassinated Oswald, the man who shot Kennedy, and was Mrs. Ruby’s Husband.

Anchor word

Local Alignment

Who shot Kennedy?

Jack assassinated Oswald, the man who shot Kennedy, and was Mrs. Ruby’s Husband.

Potential alignments

Local Alignment

Who shot Kennedy?

Jack assassinated Oswald, the man who shot Kennedy, and was Mrs. Ruby’s Husband.

One Alignment

Three Alignment Features :

Local Alignment

Who shot Kennedy?

Jack assassinated Oswald, the man who shot Kennedy, and was Mrs. Ruby’s Husband.

One Alignment

Three Alignment Features :

1. Dws : Distance between Question Head word and Anchorin the sentence

2

Local Alignment

Who shot Kennedy?

Jack assassinated Oswald, the man who shot Kennedy, and was Mrs. Ruby’s Husband.

Three Alignment Features :

2. Dwq Distance between Question Head word and AnchorIn the question

1

Local Alignment

Who shot Kennedy?

Jack assassinated Oswald, the man who shot Kennedy, and was Mrs. Ruby’s Husband.

Three Alignment Features :

3. R Has the Head Word changed position?

Headword position flipped

Build a Statistical Model

Pr (answer | question, sentence) = Pr ( Dws | answer, question, sentence)

*Pr(Dwq | answer, question, sentence)

*Pr(R | answer, question, sentence)

• and if unsure about type preference, can add in a term there

In essence, this local alignment model gives a robust method for using the context of the question to pick out the correct answer from a given sentence containing an answer

Surface text Patterns Categorize question into what kind of

data it is looking for Use templates to build specialized

models Use resulting “surface text patterns”

for searching

Birthday Templates

W. A. Mozart 1756

I. Newton 1642

M. Gandhi 1869

V. S. Naipaul 1932

Bill Gates 1951

Web Search to generate patterns

Web pages w/“Mozart” “1756”

Sentences with “Mozart” “1756”

Substrings with “Mozart” “1756”

How can we pick good patterns?

Frequent ones may be too general

Infrequent ones not that useful

Want precise, specific ones

Use held out templates to evaluate patterns