The document covers semantic analysis in language technology with a focus on question answering, specifically detailing named entity recognition and systems such as IBM's Watson. It discusses methods for improving accuracy in recognizing different types of named entities and the evolution of question answering systems using information retrieval and knowledge-based approaches. Additionally, it outlines the complexities of question processing, answer type detection, and the use of machine learning for generating responses.

1. Seman&c
Analysis
in
Language
Technology
http://stp.lingfil.uu.se/~santinim/sais/2016/sais_2016.htm
Question Answering
Marina
San(ni
san$nim@stp.lingfil.uu.se
Department
of
Linguis(cs
and
Philology
Uppsala
University,
Uppsala,
Sweden
Spring
2016
1

2. Previous
Lecture:
IE
–
Named
En$ty
Recogni$on
(NER)
2

3. • A
very
important
sub-­‐task:
find
and
classify
names
in
text,
for
example:
• The
decision
by
the
independent
MP
Andrew
Wilkie
to
withdraw
his
support
for
the
minority
Labor
government
sounded
drama(c
but
it
should
not
further
threaten
its
stability.
When,
aJer
the
2010
elec(on,
Wilkie,
Rob
OakeshoN,
Tony
Windsor
and
the
Greens
agreed
to
support
Labor,
they
gave
just
two
guarantees:
confidence
and
supply.
Named
En$ty
Recogni$on
(NER)
Person
Date
Loca(on
Organiza(on
Etc.

4. NER
pipeline
4
Representa(ve
documents
Human
annota(on
Annotated
documents
Feature
extrac(on
Training
data
Sequence
classifiers
NER
system

5. Encoding
classes
for
sequence
labeling
IO
encoding
IOB
encoding
Fred
PER
B-­‐PER
showed
O
O
Sue
PER
B-­‐PER
Mengqiu
PER
B-­‐PER
Huang
PER
I-­‐PER
‘s
O
O
new
O
O
pain(ng
O
O

6. Features
for
sequence
labeling
• Words
• Current
word
(essen(ally
like
a
learned
dic(onary)
• Previous/next
word
(context)
• Other
kinds
of
inferred
linguis(c
classifica(on
• Part-­‐of-­‐speech
tags
• Other
features
• Word
shapes
• etc.
6

7. Features: Word shapes
• Word Shapes
• Map words to simplified representation that encodes attributes
such as length, capitalization, numerals, Greek letters, internal
punctuation, etc.
Varicella-zoster Xx-xxx
mRNA xXXX
CPA1 XXXd
• Varicella
zoster
is
a
virus
• Messenger
RNA
(mRNA)
is
a
large
family
of
RNA
molecules
• CPA1
(Carboxypep(dase
A1
(Pancrea(c))
is
a
Protein
Coding
gene.

8. Inspira$on
figure
Task:
Develop
a
set
of
regular
expressions
to
recognize
the
character
shape
features.
• Possible
set
of
REs
matching
the
inspira(on
figure
(syntax
dpn
on
prLang):
8
No
need
to
remember
things
by
heart:
once
you
know
what
you
have
to
do,
find
the
correct
syntax
on
the
web!

9. The
gold
standard
corpus
There
are
always
many
solu(ons
to
a
research
ques(on!
You
had
to
make
your
choice…
Basic
steps:
1. Analyse
the
data
(you
must
know
your
data
well!!!);
2. Get
an
idea
of
the
paNerns
3. Choose
the
way
to
go…
4. Report
your
results
9

10. Proposed
solu$ons
• (Xx*)*
regardless
the
NE
type
• Complex
paNerns
that
could
iden(fy
approx.
900
lines
out
of
1316
en((es
(regardless
NE
type)
• etc…
10

11. Some
alterna$ves:
create
paLerns
per
NE
type…
(divide
and
conquer
approach
J
)
Ex:
person
names
(283):
most
person
names
have
the
shape:
(Xx*){2}
(presumably
you
woud
get
high
accuracy)
Miles
Sindercombe
p:person
Armand
de
Pontmar(n
p:person
Alicia
Gorey
p:person
Kim
Crosby
(singer)
p:person
Edmond
Roudnitska
p:person
Shobha
Gurtu
p:person
Bert
Greene
p:person
Danica
McKellar
p:person
11
Sheila
O'Brien
p:person
Mar(n
Day
p:person
Clive
MaNhew-­‐Wilson
p:person
Venugopal
Dhoot
p:person
Clifford
Berry
p:person
Munir
Malik
p:person
Mary
Sears
p:person
Charles
Wayne
"Chuck"
Day
p:person
Michael
Formanek
p:person
Felix
Carlebach
p:person
Alexander
Keith,
Jr.
p:person
Omer
Vanaudenhove
p:person

12. What’s
the
mathema$cal
formalism
underlying
REs?
12

13. DFA
13

14. Conver$ng
the
regular
expression
(a|b)*
to
a
DFA
14

15. Conver$ng
the
regular
expression
(a*|b*)*
to
a
DFA
15

16. Conver$ng
the
regular
expression
ab(a|b)*
to
a
DFA
16

17. Chomsky
hierarchy
• Regular
expressions
help
solve
problems
that
are
tractable
by
”regular
grammars”.
17
For
example,
it
is
not
possible
to
write
an
FSM
(and
consequently
regular
expressions)
that
generates
the
language
an
bn,
i.e.
the
set
of
all
strings
which
consist
of
a
(possibly
empty)
block
of
as
followed
by
a
(possibly
empty)
block
of
bs
of
exactly
the
same
length).
Areas
where
finite
state
methods
have
been
shown
to
be
par(cularly
useful
in
NLP
are
phonological
and
morphological
processing.
In
our
case,
we
must
explore
and
experiment
with
the
NE
corpus
and
see
if
there
are
sequences
that
cannot
be
captured
by
a
regular
language.

18. For
some
problems,
• …
the
expressive
power
of
REs
is
exactly
what
is
needed
• For
some
other
problems,
the
expressive
power
of
REs
is
too
weak…
• Addionally,
since
REs
a
basically
hand-­‐wriNen
rules,
it
is
easy
to
get
entagled
with
rules…
at
one
point
you
do
not
know
any
more
how
the
rules
interact
with
each
other…
so
results
might
be
unpredictable
J
18

19. End
of
previous
lecture
19

20. Question
Answering
What
is
Ques(on
Answering?

21. Acknowledgements
Most
slides
borrowed
or
adapted
from:
Dan
Jurafsky
and
Christopher
Manning,
Coursera
Dan
Jurafsky
and
James
H.
Mar(n
(2015)
J&M(2015,
draJ):
hNps://web.stanford.edu/~jurafsky/slp3/

22. 22
Ques$on
Answering
What do worms eat?
worms
eat
what
worms
eat
grass
Worms eat grass
worms
eat
grass
Grass is eaten by worms
birds
eat
worms
Birds eat worms
horses
eat
grass
Horses with worms eat grass
with
worms
Ques%on: Poten%al-Answers:
One
of
the
oldest
NLP
tasks
(punched
card
systems
in
1961)
Simmons,
Klein,
McConlogue.
1964.
Indexing
and
Dependency
Logic
for
Answering
English
Ques(ons.
American
Documenta(on
15:30,
196-­‐204

23. Ques$on
Answering:
IBM’s
Watson
• Won
Jeopardy
on
February
16,
2011!
• IBM’s
Watson
is
a
Ques(on
Answering
system.
• What
is
Jeopardy?
23

24. Jeopardy!
• Jeopardy!
is
an
American
television
quiz
compe((on
in
which
contestants
are
presented
with
general
knowledge
clues
in
the
form
of
answers,
and
must
phrase
their
responses
in
the
form
of
ques/ons.
• The
original
day(me
version
debuted
on
NBC
on
March
30,
1964,
24

25. Watson’s
performance
• With
the
answer:
“You
just
need
a
nap.
You
don’t
have
this
sleep
disorder
that
can
make
sufferers
nod
off
while
standing
up,”
Watson
replied,
“What
is
narcolepsy?”
25

26. Ques$on
Answering:
IBM’s
Watson
• The
winning
reply!
26
WILLIAM WILKINSON’S
“AN ACCOUNT OF THE PRINCIPALITIES OF
WALLACHIA AND MOLDOVIA”
INSPIRED THIS AUTHOR’S
MOST FAMOUS NOVEL
Bram
Stoker

27. Apple’s
Siri
27

28. Wolfram
Alpha
28

29. 29
Types
of
Ques$ons
in
Modern
Systems
• Factoid
ques(ons
• Who
wrote
“The
Universal
Declara/on
of
Human
Rights”?
• How
many
calories
are
there
in
two
slices
of
apple
pie?
• What
is
the
average
age
of
the
onset
of
au/sm?
• Where
is
Apple
Computer
based?
• Complex
(narra(ve)
ques(ons:
• In
children
with
an
acute
febrile
illness,
what
is
the
efficacy
of
acetaminophen
in
reducing
fever?
• What
do
scholars
think
about
Jefferson’s
posi/on
on
dealing
with
pirates?

30. Commercial
systems:
mainly
factoid
ques$ons
Where
is
the
Louvre
Museum
located?
In
Paris,
France
What’s
the
abbrevia(on
for
limited
partnership?
L.P.
What
are
the
names
of
Odin’s
ravens?
Huginn
and
Muninn
What
currency
is
used
in
China?
The
yuan
What
kind
of
nuts
are
used
in
marzipan?
almonds
What
instrument
does
Max
Roach
play?
drums
What
is
the
telephone
number
for
Stanford
University?
650-­‐723-­‐2300

31. Paradigms
for
QA
• IR-­‐based
approaches
• TREC;
IBM
Watson;
Google
• Knowledge-­‐based
• Apple
Siri;
Wolfram
Alpha;
• Hybrid
approaches
• IBM
Watson;
True
Knowledge
Evi
31

32. Many
ques$ons
can
already
be
answered
by
web
search
• a
32

33. IR-­‐based
Ques$on
Answering
• a
33

34. Things
change
all
the
$me….
J
• Google
was
a
pure
IR-­‐based
QA,
but
in
2012
Knowledge
Graph
was
added
to
Google's
search
engine.
• The
Knowledge
Graph
is
a
knowledge
base
used
by
Google
to
enhance
its
search
engine's
search
results
with
seman(c-­‐search
informa(on
gathered
from
a
wide
variety
of
sources.
• Wikipedia:
The
goal
of
KGraph
is
that
users
would
be
able
to
use
this
informa(on
to
resolve
their
query
without
having
to
navigate
to
other
sites
and
assemble
the
informa(on
themselves.
[...]
According
to
some
news
websites,
the
implementa(on
of
Google's
Knowledge
Graph
has
played
a
role
in
the
page
view
decline
of
various
language
versions
of
Wikipedia.
34

35. 35
IR-­‐based
Factoid
QA
Document
DocumentDocument
Docume
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Question
Processing
Passage
Retrieval
Query
Formulation
Answer Type
Detection
Question
Passage
Retrieval
Document
Retrieval
Answer
Processing
Answer
passages
Indexing
Relevant
Docs
DocumentDocument
Document

36. IR-­‐based
Factoid
QA
• QUESTION
PROCESSING
• Detect
ques(on
type,
answer
type,
focus,
rela(ons
• Formulate
queries
to
send
to
a
search
engine
• PASSAGE
RETRIEVAL
• Retrieve
ranked
documents
• Break
into
suitable
passages
and
rerank
• ANSWER
PROCESSING
• Extract
candidate
answers
• Rank
candidates
• using
evidence
from
the
text
and
external
sources

37. Knowledge-­‐based
approaches
(Siri)
• Build
a
seman(c
representa(on
of
the
query
• Times,
dates,
loca(ons,
en((es,
numeric
quan((es
• Map
from
this
seman(cs
to
query
structured
data
or
resources
• Geospa(al
databases
• Ontologies
(Wikipedia
infoboxes,
dbPedia,
WordNet,
Yago)
• Restaurant
review
sources
and
reserva(on
services
• Scien(fic
databases
37

38. SIRI's
main
tasks,
at
a
high
level,
involve:
• Using
ASR
(Automa(c
speech
recogni(on)
to
transcribe
human
speech
(in
this
case,
short
uNerances
of
commands,
ques(ons,
or
dicta(ons)
into
text.
• Using
natural
language
processing
(part
of
speech
tagging,
noun-­‐phrase
chunking,
dependency
&
cons(tuent
parsing)
to
translate
transcribed
text
into
"parsed
text".
• Using
ques(on
&
intent
analysis
to
analyze
parsed
text,
detec(ng
user
commands
and
ac(ons.
("Schedule
a
mee(ng",
"Set
my
alarm",
...)
• Using
data
technologies
to
interface
with
3rd-­‐party
web
services
such
as
OpenTable,
WolframAlpha,
to
perform
ac(ons,
search
opera(ons,
and
ques(on
answering.
• ULerances
SIRI
has
iden$fied
as
a
ques$on,
that
it
cannot
directly
answer,
it
will
forward
to
more
general
ques$on-­‐answering
services
such
as
WolframAlpha
• Transforming
output
of
3rd
party
web
services
back
into
natural
language
text
(eg,
Today's
weather
report
-­‐>
"The
weather
will
be
sunny")
• Using
TTS
(text-­‐to-­‐speech)
technologies
to
transform
the
natural
language
text
from
step
5
above
into
synthesized
speech.
38

39. Hybrid
approaches
(IBM
Watson)
• Build
a
shallow
seman(c
representa(on
of
the
query
• Generate
answer
candidates
using
IR
methods
• Augmented
with
ontologies
and
semi-­‐structured
data
• Score
each
candidate
using
richer
knowledge
sources
• Geospa(al
databases
• Temporal
reasoning
• Taxonomical
classifica(on
39

40. Question
Answering
Answer
Types
and
Query
Formula(on

41. Factoid
Q/A
41
Document
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Question
Processing
Passage
Retrieval
Query
Formulation
Answer Type
Detection
Question
Passage
Retrieval
Document
Retrieval
Answer
Processing
Answer
passages
Indexing
Relevant
Docs
DocumentDocument
Document

42. Ques$on
Processing
Things
to
extract
from
the
ques$on
• Answer
Type
Detec(on
• Decide
the
named
en$ty
type
(person,
place)
of
the
answer
• Query
Formula(on
• Choose
query
keywords
for
the
IR
system
• Ques(on
Type
classifica(on
• Is
this
a
defini(on
ques(on,
a
math
ques(on,
a
list
ques(on?
• Focus
Detec(on
• Find
the
ques(on
words
that
are
replaced
by
the
answer
• Rela(on
Extrac(on
• Find
rela(ons
between
en((es
in
the
ques(on
42

43. Question Processing
They’re the two states you could be reentering if you’re crossing
Florida’s northern border
• Answer
Type:
US
state
• Query:
two
states,
border,
Florida,
north
• Focus:
the
two
states
• Rela(ons:
borders(Florida,
?x,
north)
43

44. Answer
Type
Detec$on:
Named
En$$es
• Who
founded
Virgin
Airlines?
•
PERSON
• What
Canadian
city
has
the
largest
popula/on?
•
CITY.

45. Answer
Type
Taxonomy
• 6
coarse
classes
• ABBEVIATION,
ENTITY,
DESCRIPTION,
HUMAN,
LOCATION,
NUMERIC
• 50
finer
classes
• LOCATION:
city,
country,
mountain…
• HUMAN:
group,
individual,
(tle,
descrip(on
• ENTITY:
animal,
body,
color,
currency…
45
Xin
Li,
Dan
Roth.
2002.
Learning
Ques(on
Classifiers.
COLING'02

46. 46
Part
of
Li
&
Roth’s
Answer
Type
Taxonomy
LOCATION
NUMERIC
ENTITY HUMAN
ABBREVIATION
DESCRIPTION
country city state
date
percent
money
sizedistance
individual
title
group
food
currency
animal
definition
reason expression
abbreviation

47. 47
Answer
Types

48. 48
More
Answer
Types

49. Answer
types
in
Jeopardy
• 2500
answer
types
in
20,000
Jeopardy
ques(on
sample
• The
most
frequent
200
answer
types
cover
<
50%
of
data
• The
40
most
frequent
Jeopardy
answer
types
he,
country,
city,
man,
film,
state,
she,
author,
group,
here,
company,
president,
capital,
star,
novel,
character,
woman,
river,
island,
king,
song,
part,
series,
sport,
singer,
actor,
play,
team,
show,
actress,
animal,
presiden(al,
composer,
musical,
na(on,
book,
(tle,
leader,
game
49
Ferrucci
et
al.
2010.
Building
Watson:
An
Overview
of
the
DeepQA
Project.
AI
Magazine.
Fall
2010.
59-­‐79.

50. Answer
Type
Detec$on
• Hand-­‐wriNen
rules
• Machine
Learning
• Hybrids

51. Answer
Type
Detec$on
• Regular
expression-­‐based
rules
can
get
some
cases:
• Who
{is|was|are|were}
PERSON
• PERSON
(YEAR
–
YEAR)
• Other
rules
use
the
ques$on
headword:
(the
headword
of
the
first
noun
phrase
aJer
the
wh-­‐word)
• Which
city
in
China
has
the
largest
number
of
foreign
financial
companies?
• What
is
the
state
flower
of
California?

52. Answer
Type
Detec$on
• Most
oJen,
we
treat
the
problem
as
machine
learning
classifica(on
• Define
a
taxonomy
of
ques(on
types
• Annotate
training
data
for
each
ques(on
type
• Train
classifiers
for
each
ques(on
class
using
a
rich
set
of
features.
• features
include
those
hand-­‐wriNen
rules!
52

53. Features
for
Answer
Type
Detec$on
• Ques(on
words
and
phrases
• Part-­‐of-­‐speech
tags
• Parse
features
(headwords)
• Named
En((es
• Seman(cally
related
words
53

54. Factoid
Q/A
54
Document
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Question
Processing
Passage
Retrieval
Query
Formulation
Answer Type
Detection
Question
Passage
Retrieval
Document
Retrieval
Answer
Processing
Answer
passages
Indexing
Relevant
Docs
DocumentDocument
Document

55. Keyword
Selec$on
Algorithm
1.
Select
all
non-­‐stop
words
in
quota(ons
2.
Select
all
NNP
words
in
recognized
named
en((es
3.
Select
all
complex
nominals
with
their
adjec(val
modifiers
4.
Select
all
other
complex
nominals
5.
Select
all
nouns
with
their
adjec(val
modifiers
6.
Select
all
other
nouns
7.
Select
all
verbs
8.
Select
all
adverbs
9.
Select
the
QFW
word
(skipped
in
all
previous
steps)
10.
Select
all
other
words
Dan
Moldovan,
Sanda
Harabagiu,
Marius
Paca,
Rada
Mihalcea,
Richard
Goodrum,
Roxana
Girju
and
Vasile
Rus.
1999.
Proceedings
of
TREC-­‐8.

56. Choosing keywords from the query
56
Who coined the term “cyberspace” in his novel “Neuromancer”?
1 1
4 4
7
cyberspace/1 Neuromancer/1 term/4 novel/4 coined/7
Slide
from
Mihai
Surdeanu

57. Question
Answering
Passage
Retrieval
and
Answer
Extrac(on

58. Factoid
Q/A
58
Document
DocumentDocument
Docume
ntDocume
ntDocume
ntDocume
ntDocume
nt
Question
Processing
Passage
Retrieval
Query
Formulation
Answer Type
Detection
Question
Passage
Retrieval
Document
Retrieval
Answer
Processing
Answer
passages
Indexing
Relevant
Docs
DocumentDocument
Document

59. 59
Passage
Retrieval
• Step
1:
IR
engine
retrieves
documents
using
query
terms
• Step
2:
Segment
the
documents
into
shorter
units
• something
like
paragraphs
• Step
3:
Passage
ranking
• Use
answer
type
to
help
rerank
passages

60. Features
for
Passage
Ranking
• Number
of
Named
En((es
of
the
right
type
in
passage
• Number
of
query
words
in
passage
• Number
of
ques(on
N-­‐grams
also
in
passage
• Proximity
of
query
keywords
to
each
other
in
passage
• Longest
sequence
of
ques(on
words
• Rank
of
the
document
containing
passage
Either
in
rule-­‐based
classifiers
or
with
supervised
machine
learning

61. Factoid
Q/A
61
Document
DocumentDocument
Docume
ntDocume
ntDocume
ntDocume
ntDocume
nt
Question
Processing
Passage
Retrieval
Query
Formulation
Answer Type
Detection
Question
Passage
Retrieval
Document
Retrieval
Answer
Processing
Answer
passages
Indexing
Relevant
Docs
DocumentDocument
Document

62. Answer
Extrac$on
• Run
an
answer-­‐type
named-­‐en(ty
tagger
on
the
passages
• Each
answer
type
requires
a
named-­‐en(ty
tagger
that
detects
it
• If
answer
type
is
CITY,
tagger
has
to
tag
CITY
• Can
be
full
NER,
simple
regular
expressions,
or
hybrid
• Return
the
string
with
the
right
type:
• Who is the prime minister of India (PERSON)
Manmohan Singh, Prime Minister of India, had told
left leaders that the deal would not be renegotiated.!
• How tall is Mt. Everest? (LENGTH)
The official height of Mount Everest is 29035 feet!

63. Ranking
Candidate
Answers
• But
what
if
there
are
mul(ple
candidate
answers!
Q: Who was Queen Victoria’s second son?!
• Answer
Type:
Person
• Passage:
The
Marie
biscuit
is
named
aJer
Marie
Alexandrovna,
the
daughter
of
Czar
Alexander
II
of
Russia
and
wife
of
Alfred,
the
second
son
of
Queen
Victoria
and
Prince
Albert
Apposi(on
is
a
gramma(cal
construc(on
in
which
two
elements,
normally
noun
phrases,
are
placed
side
by
side,
with
one
element
serving
to
iden(fy
the
other
in
a
different
way.

64. Use
machine
learning:
Features
for
ranking
candidate
answers
Answer
type
match:
Candidate
contains
a
phrase
with
the
correct
answer
type.
PaLern
match:
Regular
expression
paNern
matches
the
candidate.
Ques$on
keywords:
#
of
ques(on
keywords
in
the
candidate.
Keyword
distance:
Distance
in
words
between
the
candidate
and
query
keywords
Novelty
factor:
A
word
in
the
candidate
is
not
in
the
query.
Apposi$on
features:
The
candidate
is
an
apposi(ve
to
ques(on
terms
Punctua$on
loca$on:
The
candidate
is
immediately
followed
by
a
comma,
period,
quota(on
marks,
semicolon,
or
exclama(on
mark.
Sequences
of
ques$on
terms:
The
length
of
the
longest
sequence
of
ques(on
terms
that
occurs
in
the
candidate
answer.

65. Candidate
Answer
scoring
in
IBM
Watson
• Each
candidate
answer
gets
scores
from
>50
components
• (from
unstructured
text,
semi-­‐structured
text,
triple
stores)
• logical
form
(parse)
match
between
ques(on
and
candidate
• passage
source
reliability
• geospa(al
loca(on
• California
is
”southwest
of
Montana”
• temporal
rela(onships
• taxonomic
classifica(on
65

66. 66
Common
Evalua$on
Metrics
1. Accuracy
(does
answer
match
gold-­‐labeled
answer?)
2. Mean
Reciprocal
Rank
• For
each
query
return
a
ranked
list
of
M
candidate
answers.
• Its
score
is
1/Rank
of
the
first
right
answer.
• Take
the
mean
over
all
N
queries
MRR =
1
rankii=1
N
∑
N

67. 67
Common
Evalua$on
Metrics
1. Accuracy
(does
answer
match
gold-­‐labeled
answer?)
2. Mean
Reciprocal
Rank:
• The
reciprocal
rank
of
a
query
response
is
the
inverse
of
the
rank
of
the
first
correct
answer.
• The
mean
reciprocal
rank
is
the
average
of
the
reciprocal
ranks
of
results
for
a
sample
of
queries
Q
MRR =
1
rankii=1
N
∑
N
=

68. Common
Evalua$on
Metrics:
MRR
• The
mean
reciprocal
rank
is
the
average
of
the
reciprocal
ranks
of
results
for
a
sample
of
queries
Q.
• (ex
adapted
from
Wikipedia)
• 3
ranked
answers
for
a
query,
with
the
first
one
being
the
one
it
thinks
is
most
likely
correct
• Given
those
3
samples,
we
could
calculate
the
mean
reciprocal
rank
as
(1/3
+
1/2
+
1)/3
=
11/18
or
about
0.61.
68

69. 69
Common
Evalua$on
Metrics
1. Mean
Reciprocal
Rank
• For
each
query
return
a
ranked
list
of
M
candidate
answers.
• Query
score
is
1/Rank
of
the
first
correct
answer
• If
first
answer
is
correct:
1
• else
if
second
answer
is
correct:
½
• else
if
third
answer
is
correct:
⅓,
etc.
• Score
is
0
if
none
of
the
M
answers
are
correct
• Take
the
mean
over
all
N
queries
MRR =
1
rankii=1
N
∑
N

70. Use
of
this
metric
• Mean
reciprocal
rank
is
a
sta(s(c
measure
for
evalua(ng
any
process
that
produces
a
list
of
possible
responses
to
a
sample
of
queries,
ordered
by
probability
of
correctness.
• Machine
transla(on
• Ques(on
answering
• Etc.
70

71. Question
Answering
Advanced:
Answering
Complex
Ques(ons

72. Answering
harder
ques$ons
Q:
What
is
water
spinach?
A:
Water
spinach
(ipomoea
aqua(ca)
is
a
semi-­‐aqua(c
leafy
green
plant
with
long
hollow
stems
and
spear-­‐
or
heart-­‐shaped
leaves,
widely
grown
throughout
Asia
as
a
leaf
vegetable.
The
leaves
and
stems
are
oJen
eaten
s(r-­‐fried
flavored
with
salt
or
in
soups.
Other
common
names
include
morning
glory
vegetable,
kangkong
(Malay),
rau
muong
(Viet.),
ong
choi
(Cant.),
and
kong
xin
cai
(Mand.).
It
is
not
related
to
spinach,
but
is
closely
related
to
sweet
potato
and
convolvulus.

73. Answering
harder
ques$on
Q:
In
children
with
an
acute
febrile
illness,
what
is
the
efficacy
of
single
medica(on
therapy
with
acetaminophen
or
ibuprofen
in
reducing
fever?
A:
Ibuprofen
provided
greater
temperature
decrement
and
longer
dura(on
of
an(pyresis
than
acetaminophen
when
the
two
drugs
were
administered
in
approximately
equal
doses.
(PubMedID:
1621668,
Evidence
Strength:
A)

74. Answering
harder
ques$ons
via
query-­‐focused
summariza$on
• The
(boNom-­‐up)
snippet
method
• Find
a
set
of
relevant
documents
• Extract
informa(ve
sentences
from
the
documents
(using
…-­‐idf,
MMR)
• Order
and
modify
the
sentences
into
an
answer
• The
(top-­‐down)
informa(on
extrac(on
method
• build
specific
answerers
for
different
ques(on
types:
• defini(on
ques(ons,
• biography
ques(ons,
• certain
medical
ques(ons

75. The
Informa$on
Extrac$on
method
• a
good
biography
of
a
person
contains:
• a
person’s
birth/death,
fame
factor,
educa$on,
na$onality
and
so
on
• a
good
defini$on
contains:
• genus
or
hypernym
• The
Hajj
is
a
type
of
ritual
• a
medical
answer
about
a
drug’s
use
contains:
• the
problem
(the
medical
condi(on),
• the
interven$on
(the
drug
or
procedure),
and
• the
outcome
(the
result
of
the
study).

76. Informa$on
that
should
be
in
the
answer
for
3
kinds
of
ques$ons

77. Document
Retrieval
11 Web documents
1127 total
sentences
Predicate
Identification
Data-Driven
Analysis
383 Non-Specific Definitional sentences
Sentence clusters,
Importance ordering
Definition
Creation
9 Genus-Species Sentences
The Hajj, or pilgrimage to Makkah (Mecca), is the central duty of Islam.
The Hajj is a milestone event in a Muslim's life.
The hajj is one of five pillars that make up the foundation of Islam.
...
The Hajj, or pilgrimage to Makkah [Mecca], is the central duty of Islam. More than
two million Muslims are expected to take the Hajj this year. Muslims must perform
the hajj at least once in their lifetime if physically and financially able. The Hajj is a
milestone event in a Muslim's life. The annual hajj begins in the twelfth month of
the Islamic year (which is lunar, not solar, so that hajj and Ramadan fall sometimes
in summer, sometimes in winter). The Hajj is a week-long pilgrimage that begins in
the 12th month of the Islamic lunar calendar. Another ceremony, which was not
connected with the rites of the Ka'ba before the rise of Islam, is the Hajj, the
annual pilgrimage to 'Arafat, about two miles east of Mecca, toward Mina…
"What is the Hajj?"
(Ndocs=20, Len=8)
Architecture
for
complex
ques$on
answering:
defini$on
ques$ons
S.
Blair-­‐Goldensohn,
K.
McKeown
and
A.
Schlaikjer.
2004.
Answering
Defini(on
Ques(ons:
A
Hyrbid
Approach.

78. The end