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QA in Discussion Boards QA in Discussion Boards

QA in Discussion Boards - PowerPoint Presentation

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QA in Discussion Boards - PPT Presentation

Companies eg Dell IBM use discussion boards as ways for customers to get answers to their questions 90 of 40 analyzed discussion boards contain questions and answers Online QA services could benefit Yahoo Answers Answerscom etc ID: 600352

posts mahatma answers gandhi mahatma posts gandhi answers question bill gates questions answer relevance college features discussion query authorship boards data detection

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Slide1

QA in Discussion Boards

Companies (e.g., Dell, IBM) use discussion boards as ways for customers to get answers to their questions

90% of 40 analyzed discussion boards contain questions and answers

Online QA services could benefit (Yahoo! Answers, Answers.com, etc)

But…

finding questions and their answers is hard

Post may not be in question format

Answers are provided asynchronously

Messages in a single thread may response to different questionsSlide2

Research questions

Can we detect question threads in an efficient and effective manner?

What features should be used (content/non)?

Can we effectively discover answers without analyzing content of replied posts?

Who posts these answers and where do they appear?

Can this task be treated as a traditional IR problem suitable for relevance detection?Slide3

Question Post from UbuntuForums.org

There are a number of threads

on Firefox

crashes, so it’s

nothing new

. I upgraded from U8.04

to U8.10

, but it’s no better. Then

I tried

Seamonkey

, and it worked

fine for

a couple of days. Now it too

is crashing

. I’m baffled. Anyone

have any

ideas what I can do?Slide4

Method: Classification

Features for question classification

Question mark

5 W1H words (who, what, when, where, why)

Total # of posts within 1 thread: long posts problematic

Authorship: a new poster is more likely a questioner, vice versa for the answerer

N-gramsSlide5

Answer detection features

Position of post: answer usually not near bottom

Authorship

N-grams

Stop words: an answer probably contains less

Query likelihood model score: tests relevance to questionSlide6

Experiments

Baseline:

previously published system (Cong) using syntactic patterns (Q) and query relevance (A)

Data:

Photography on next (700K posts)

Ubuntu

Forum (555K posts)

Training data: manually labeled all first posts and answers from 2580

Ubuntu

posts and 3962 photo posts

Balanced the data set (50% positive, 50% negative)

SVM classifier and 10-fold cross validationSlide7
Slide8

Answer Detection

Relevance model did not do well

Perhaps ranking difficult since all posts more or less relevant to the question

N-gram does not outperform other features

Stop word similar performance to n-gram

Simple heuristics (position, authorship) best. Combination outperforms all others.Slide9
Slide10
Slide11

Proposed Solution

Paraphrase Templates

How did Mahatma Gandhi die?

Mahatma

Gandhi died

<how>

Mahatma

Gandhi died of

<what>

Mahatma

Gandhi died from

<what>

Mahatma

Gandhi’s death from

<what>

Mahatma

Gandhi drowned

Mahatma

Gandhi suffocated

Mahatma

Gandhi froze to death

<who>

killed

Mahatma Gandhi

<who>

assassinated

Mahatma Gandhi

Mahatma

Gandhi was killedSlide12

Use

In IR to find documents more likely to contain answer

To rank sentences within documents that are returned by IRSlide13

Reformulations

Hand-built or manual generalizations of automatically produced paraphrases

Specify type of relation to original

Number of reformulations: 1-30,

ave

: 3.24Slide14

Other Reformulation Types

Lexical:

Buy/sell:

John sold the laptop to Mary

=

Mary bought the laptop from John

Syntactic:

How deep is Crater Lake?

Crater Lake has a depth of <what distance>

Inference

Reformulation Chains

Where did Bill Gates go to college?

Bill Gates was a student at <which college>?

Bill Gates dropped out of <which college>?

Bill Gates was a <which college> dropout?

Text: Bill Gates was a Harvard dropout.Slide15

But….

Their Web IR System

Preserve quoted terms and quote the smallest NPs:

“What is the longest river in the United States?” -> “longest river” and “United States”

Expand the query using

Wordnet

Synonyms

“What is the length of the border between Ukraine and Russia?” -> (“length” or “distance”) and (“border” or “surround”) and (“Ukraine” or “

Ukrainia

”) and (“Russia” or “Soviet Union”) and (“between” or “betwixt”)

Using

Contex

refomulations

, add quoted reformulations of the question’s declarative form

“What is an atom?” -> “is an atom”, “an atom is” “an atom is one of”Slide16

Evaluation