Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 497–504,
Sydney, July 2006. c©2006 Association for Computational Linguistics
QuestionBank: Creating a Corpus of Parse-Annotated Questions
John Judge1, Aoife Cahill1, and Josef van Genabith1,2
1National Centre for Language Technology and School of Computing,
Dublin City University, Dublin, Ireland
2IBM Dublin Center for Advanced Studies,
IBM Dublin, Ireland
{jjudge,acahill,josef}@computing.dcu.ie
Abstract
This paper describes the development of
QuestionBank, a corpus of 4000 parse-
annotated questions for (i) use in training
parsers employed in QA, and (ii) evalua-
tion of question parsing. We present a se-
ries of experiments to investigate the ef-
fectiveness of QuestionBank as both an
exclusive and supplementary training re-
source for a state-of-the-art parser in pars-
ing both question and non-question test
sets. We introduce a new method for
recovering empty nodes and their an-
tecedents (capturing long distance depen-
dencies) from parser output in CFG trees
using LFG f-structure reentrancies. Our
main  ndings are (i) using QuestionBank
training data improves parser performance
to 89.75% labelled bracketing f-score, an
increase of almost 11% over the base-
line; (ii) back-testing experiments on non-
question data (Penn-II WSJ Section 23)
shows that the retrained parser does not
suffer a performance drop on non-question
material; (iii) ablation experiments show
that the size of training material provided
by QuestionBank is suf cient to achieve
optimal results; (iv) our method for recov-
ering empty nodes captures long distance
dependencies in questions from the ATIS
corpus with high precision (96.82%) and
low recall (39.38%). In summary, Ques-
tionBank provides a useful new resource
in parser-based QA research.
1 Introduction
Parse-annotated corpora (treebanks) are crucial for
developing machine learning and statistics-based
parsing resources for a given language or task.
Large treebanks are available for major languages,
however these are often based on a speci c text
type or genre, e.g.  nancial newspaper text (the
Penn-II Treebank (Marcus et al., 1993)). This can
limit the applicability of grammatical resources in-
duced from treebanks in that such resources un-
derperform when used on a different type of text
or for a speci c task.
In this paper we present work on creating Ques-
tionBank, a treebank of parse-annotated questions,
which can be used as a supplementary training re-
source to allow parsers to accurately parse ques-
tions (as well as other text). Alternatively, the re-
source can be used as a stand-alone training corpus
to train a parser speci cally for questions. Either
scenario will be useful in training parsers for use
in question answering (QA) tasks, and it also pro-
vides a suitable resource to evaluate the accuracy
of these parsers on questions.
We use a semi-automatic  bootstrapping 
method to create the question treebank from raw
text. We show that a parser trained on the ques-
tion treebank alone can accurately parse ques-
tions. Training on a combined corpus consisting of
the question treebank and an established training
set (Sections 02-21 of the Penn-II Treebank), the
parser gives state-of-the-art performance on both
questions and a non-question test set (Section 23
of the Penn-II Treebank).
Section 2 describes background work and mo-
tivation for the research presented in this paper.
Section 3 describes the data we used to create
the corpus. In Section 4 we describe the semi-
automatic method to  bootstrap the question cor-
pus, discuss some interesting and problematic
phenomena, and show how the manual vs. auto-
matic workload distribution changed as work pro-
gressed. Two sets of experiments using our new
question corpus are presented in Section 5. In
Section 6 we introduce a new method for recover-
ing empty nodes and their antecedents using Lex-
ical Functional Grammar (LFG) f-structure reen-
497
trancies. Section 7 concludes and outlines future
work.
2 Background and Motivation
High quality probabilistic, treebank-based parsing
resources can be rapidly induced from appropri-
ate treebank material. However, treebank- and
machine learning-based grammatical resources re-
 ect the characteristics of the training data. They
generally underperform on test data substantially
different from the training data.
Previous work on parser performance and do-
main variation by Gildea (2001) showed that by
training a parser on the Penn-II Treebank and test-
ing on the Brown corpus, parser accuracy drops by
5.7% compared to parsing the Wall Street Journal
(WSJ) based Penn-II Treebank Section 23. This
shows a negative effect on parser performance
even when the test data is not radically different
from the training data (both the Penn II and Brown
corpora consist primarily of written texts of Amer-
ican English, the main difference is the consider-
ably more varied nature of the text in the Brown
corpus). Gildea also shows how to resolve this
problem by adding appropriate data to the training
corpus, but notes that a large amount of additional
data has little impact if it is not matched to the test
material.
Work on more radical domain variance and on
adapting treebank-induced LFG resources to anal-
yse ATIS (Hemphill et al., 1990) question mate-
rial is described in Judge et al. (2005). The re-
search established that even a small amount of ad-
ditional training data can give a substantial im-
provement in question analysis in terms of both
CFG parse accuracy and LFG grammatical func-
tional analysis, with no signi cant negative effects
on non-question analysis. Judge et al. (2005) sug-
gest, however, that further improvements are pos-
sible given a larger question training corpus.
Clark et al. (2004) worked speci cally with
question parsing to generate dependencies for QA
with Penn-II treebank-based Combinatory Cate-
gorial Grammars (CCG’s). They use  what ques-
tions taken from the TREC QA datasets as the ba-
sis for a What-Question corpus with CCG annota-
tion.
3 Data Sources
The raw question data for QuestionBank comes
from two sources, the TREC 8-11 QA track
test sets1, and a question classi er training set
produced by the Cognitive Computation Group
(CCG2) at the University of Illinois at Urbana-
Champaign.3 We use equal amounts of data from
each source so as not to bias the corpus to either
data source.
3.1 TREC Questions
The TREC evaluations have become the standard
evaluation for QA systems. Their test sets con-
sist primarily of fact seeking questions with some
imperative statements which request information,
e.g.  List the names of cell phone manufactur-
ers. We included 2000 TREC questions in the
raw data from which we created the question tree-
bank. These 2000 questions consist of the test
questions for the  rst three years of the TREC QA
track (1893 questions) and 107 questions from the
2003 TREC test set.
3.2 CCG Group Questions
The CCG provide a number of resources for de-
veloping QA systems. One of these resources is
a set of 5500 questions and their answer types for
use in training question classi ers. The 5500 ques-
tions were stripped of answer type annotation, du-
plicated TREC questions were removed and 2000
questions were used for the question treebank.
The CCG 5500 questions come from a number
of sources (Li and Roth, 2002) and some of these
questions contain minor grammatical mistakes so
that, in essence, this corpus is more representa-
tive of genuine questions that would be put to a
working QA system. A number of changes in to-
kenisation were corrected (eg. separating contrac-
tions), but the minor grammatical errors were left
unchanged because we believe that it is necessary
for a parser for question analysis to be able to cope
with this sort of data if it is to be used in a working
QA system.
4 Creating the Treebank
4.1 Bootstrapping a Question Treebank
The algorithm used to generate the question tree-
bank is an iterative process of parsing, manual cor-
rection, retraining, and parsing.
1http://trec.nist.gov/data/qa.html
2Note that the acronym CCG here refers to Cognitive
Computation Group, rather than Combinatory Categorial
Grammar mentioned in Section 2.
3http://l2r.cs.uiuc.edu/ cogcomp/tools.php
498
Algorithm 1 Induce a parse-annotated treebank
from raw data
repeat
Parse a new section of raw data
Manually correct errors in the parser output
Add the corrected data to the training set
Extract a new grammar for the parser
until All the data has been processed
Algorithm 1 summarises the bootstrapping al-
gorithm. A section of raw data is parsed. The
parser output is then manually corrected, and
added to the parser’s training corpus. A new gram-
mar is then extracted, and the next section of raw
data is parsed. This process continues until all the
data has been parsed and hand corrected.
4.2 Parser
The parser used to process the raw questions prior
to manual correction was that of Bikel (2002)4,
a retrainable emulation of Collins (1999) model
2 parser. Bikel’s parser is a history-based parser
which uses a lexicalised generative model to parse
sentences. We used WSJ Sections 02-21 of the
Penn-II Treebank to train the parser for the  rst it-
eration of the algorithm. The training corpus for
subsequent iterations consisted of the WSJ ma-
terial and increasing amounts of processed ques-
tions.
4.3 Basic Corpus Development Statistics
Our question treebank was created over a period
of three months at an average annotation speed of
about 60 questions per day. This is quite rapid
for treebank development. The speed of the pro-
cess was helped by two main factors: the questions
are generally quite short (typically about 10 words
long), and, due to retraining on the continually in-
creasing training set, the quality of the parses out-
put by the parser improved dramatically during the
development of the treebank, with the effect that
corrections during the later stages were generally
quite small and not as time consuming as during
the initial phases of the bootstrapping process.
For example, in the  rst week of the project the
trees from the parser were of relatively poor qual-
ity and over 78% of the trees needed to be cor-
rected manually. This slowed the annotation pro-
cess considerably and parse-annotated questions
4Downloaded from http://www.cis.upenn.edu/∼dbikel
/software.html#stat-parser
were being produced at an average rate of 40 trees
per day. During the later stages of the project this
had changed dramatically. The quality of trees
from the parser was much improved with less than
20% of the trees requiring manual correction. At
this stage parse-annotated questions were being
produced at an average rate of 90 trees per day.
4.4 Corpus Development Error Analysis
Some of the more frequent errors in the parser
output pertain to the syntactic analysis of WH-
phrases (WHNP, WHPP, etc). In Sections 02-21
of the Penn-II Treebank, these are used more often
in relative clause constructions than in questions.
As a result many of the corpus questions were
given syntactic analyses corresponding to relative
clauses (SBAR with an embedded S) instead of as
questions (SBARQ with an embedded SQ). Figure
1 provides an example.
SBAR
WHNP
WP
Who
S
VP
VBD
created
NP
DT
the
NN
Muppets
(a)
SBARQ
WHNP
WP
Who
SQ
VP
VBD
created
NP
DT
the
NNPS
Muppets
(b)
Figure 1: Example tree before (a) and after correc-
tion (b)
Because the questions are typically short, an er-
ror like this has quite a large effect on the accu-
racy for the overall tree; in this case the f-score
for the parser output (Figure 1(a)) would be only
60%. Errors of this nature were quite frequent
in the  rst section of questions analysed by the
parser, but with increased training material becom-
ing available during successive iterations, this er-
ror became less frequent and towards the end of
499
the project it was only seen in rare cases.
WH-XP marking was the source of a number of
consistent (though infrequent) errors during anno-
tation. This occurred mostly in PP constructions
containing WHNPs. The parser would output a
structure like Figure 2(a), where the PP mother of
the WHNP is not correctly labelled as a WHPP as
in Figure 2(b).
PP
IN
by
WHNP
WP$
whose
NN
authority
WHPP
IN
by
WHNP
WP$
whose
NN
authority
(a) (b)
Figure 2: WH-XP assignment
The parser output often had to be rearranged
structurally to varying degrees. This was common
in the longer questions. A recurring error in the
parser output was failing to identify VPs in SQs
with a single object NP. In these cases the verb
and the object NP were left as daughters of the
SQ node. Figure 3(a) illustrates this, and Figure
3(b) shows the corrected tree with the VP node in-
serted.
SBARQ
WHNP
WP
Who
SQ
VBD
killed
NP
Ghandi
SBARQ
WHNP
WP
Who
SQ
VP
VBD
killed
NP
Ghandi
(a) (b)
Figure 3: VP missing inside SQ with a single NP
On inspection, we found that the problem was
caused by copular constructions, which, accord-
ing to the Penn-II annotation guidelines, do not
feature VP constituents. Since almost half of the
question data contain copular constructions, the
parser trained on this data would sometimes mis-
analyse non-copular constructions or, conversely,
incorrectly bracket copular constructions using a
VP constituent (Figure 4(a)).
The predictable nature of these errors meant that
they were simple to correct. This is due to the par-
ticular context in which they occur and the  nite
number of forms of the copular verb.
SBARQ
WHNP
WP
What
SQ
VP
VBZ
is
NP
a fear of shadows
SBARQ
WHNP
WP
What
SQ
VBZ
is
NP
a fear of shadows
(a) (b)
Figure 4: Erroneous VP in copular constructions
5 Experiments with QuestionBank
In order to test the effect training on the question
corpus has on parser performance, we carried out
a number of experiments. In cross-validation ex-
periments with 90%/10% splits we use all 4000
trees in the completed QuestionBank as the test
set. We performed ablation experiments to inves-
tigate the effect of varying the amount of question
and non-question training data on the parser’s per-
formance. For these experiments we divided the
4000 questions into two sets. We randomly se-
lected 400 trees to be held out as a gold standard
test set against which to evaluate, the remaining
3600 trees were then used as a training corpus.
5.1 Establishing the Baseline
The baseline we use for our experiments is pro-
vided by Bikel’s parser trained on WSJ Sections
02-21 of the Penn-II Treebank. We test on all 4000
questions in our question treebank, and also Sec-
tion 23 of the Penn-II Treebank.
QuestionBank
Coverage 100
F-Score 78.77
WSJ Section 23
Coverage 100
F-Score 82.97
Table 1: Baseline parsing results
Table 1 shows the results for our baseline eval-
uations on question and non-question test sets.
While the coverage for both tests is high, the
parser underperforms signi cantly on the question
test set with a labelled bracketing f-score of 78.77
compared to 82.97 on Section 23 of the Penn-II
Treebank. Note that unlike the published results
for Bikel’s parser in our evaluations we test on
Section 23 and include punctuation.
5.2 Cross-Validation Experiments
We carried out two cross-validation experiments.
In the  rst experiment we perform a 10-fold cross-
validation experiment using our 4000 question
500
treebank. In each case a randomly selected set of
10% of the questions in QuestionBank was held
out during training and used as a test set. In this
way parses from unseen data were generated for
all 4000 questions and evaluated against the Ques-
tionBank trees.
The second cross-validation experiment was
similar to the  rst, but in each of the 10 folds we
train on 90% of the 4000 questions in Question-
Bank and on all of Sections 02-21 of the Penn-II
Treebank.
In both experiments we also backtest each of the
ten grammars on Section 23 of the Penn-II Tree-
bank and report the average scores.
QuestionBank
Coverage 100
F-Score 88.82
Backtest on Sect 23
Coverage 98.79
F-Score 59.79
Table 2: Cross-validation experiment using the
4000 question treebank
Table 2 shows the results for the  rst cross-
validation experiment, using only the 4000 sen-
tence QuestionBank. Compared to Table 1, the re-
sults show a signi cant improvement of over 10%
on the baseline f-score for questions. However, the
tests on the non-question Section 23 data show not
only a signi cant drop in accuracy but also a drop
in coverage.
Questions
Coverage 100
F-Score 89.75
Backtest on Sect 23
Coverage 100
F-Score 82.39
Table 3: Cross-validation experiment using Penn-
II Treebank Sections 02-21 and 4000 questions
Table 3 shows the results for the second cross-
validation experiment using Sections 02-21 of the
Penn-II Treebank and the 4000 questions in Ques-
tionBank. The results show an even greater in-
crease on the baseline f-score than the experiments
using only the question training set (Table 2). The
non-question results are also better and are com-
parable to the baseline (Table 1).
5.3 Ablation Runs
In a further set of experiments we investigated the
effect of varying the amount of data in the parser’s
training corpus. We experiment with varying both
the amount of QuestionBank and Penn-II Tree-
bank data that the parser is trained on. In each
experiment we use the 400 question test set and
Section 23 of the Penn-II Treebank to evaluate
against, and the 3600 question training set de-
scribed above and Sections 02-21 of the Penn-II
Treebank as the basis for the parser’s training cor-
pus. We report on three experiments:
In the  rst experiment we train the parser using
only the 3600 question training set. We performed
ten training and parsing runs in this experiment,
incrementally reducing the size of the Question-
Bank training corpus by 10% of the whole on each
run.
The second experiment is similar to the  rst but
in each run we add Sections 02-21 of the Penn-II
Treebank to the (shrinking) training set of ques-
tions.
The third experiment is the converse of the sec-
ond, the amount of questions in the training set
remains  xed (all 3600) and the amount of Penn-
II Treebank material is incrementally reduced by
10% on each run.
 50
 60
 70
 80
 90
 100
 10 20 30 40 50 60 70 80 90 100
Coverage/F-Score
Percentage of 3600 questions in the training corpus
FScore Questions
FScore Section 23
Coverage Questions
Coverage Section 23
Figure 5: Results for ablation experiment reducing
3600 training questions in steps of 10%
Figure 5 graphs the coverage and f-score for
the parser in tests on the 400 question test set,
and Section 23 of the Penn-II Treebank in ten
parsing runs with the amount of data in the 3600
question training corpus reducing incrementally
on each run. The results show that training on only
a small amount of questions, the parser can parse
questions with high accuracy. For example when
trained on only 10% of the 3600 questions used
in this experiment, the parser successfully parses
all of the 400 question test set and achieves an f-
score of 85.59. However the results for the tests
on WSJ Section 23 are considerably worse. The
parser never manages to parse the full test set, and
the best score at 59.61 is very low.
Figure 6 graphs the results for the second abla-
501
 50
 60
 70
 80
 90
 100
 10 20 30 40 50 60 70 80 90 100
Coverage/F-Score
Percentage of 3600 questions in the training corpus
FScore Questions
FScore Section 23
Coverage Questions
Coverage Section 23
Figure 6: Results for ablation experiment using
PTB Sections 02-21 ( xed) and reducing 3600
questions in steps of 10%
 50
 60
 70
 80
 90
 100
 10 20 30 40 50 60 70 80 90 100
Coverage/F-Score
Percentage of PTB Stetcions 2-21 in the training corpus
FScore Questions
FScore Section 23
Coverage Questions
Coverage Section 23
Figure 7: Results for ablation experiment using
3600 questions ( xed) and reducing PTB Sections
02-21 in steps of 10%
tion experiment. The training set for the parser
consists of a  xed amount of Penn-II Treebank
data (Sections 02-21) and a reducing amount of
question data from the 3600 question training set.
Each grammar is tested on both the 400 question
test set, and WSJ Section 23. The results here
are signi cantly better than in the previous exper-
iment. In all of the runs the coverage for both test
sets is 100%, f-scores for the question test set de-
crease as the amount of question data in the train-
ing set is reduced (though they are still quite high.)
There is little change in the f-scores for the tests on
Section 23, the results all fall in the range 82.36 to
82.46, which is comparable to the baseline score.
Figure 7 graphs the results for the third abla-
tion experiment. In this case the training set is a
 xed amount of the question training set described
above (all 3600 questions) and a reducing amount
of data from Sections 02-21 of the Penn Treebank.
The graph shows that the parser performs consis-
tently well on the question test set in terms of both
coverage and accuracy. The tests on Section 23,
however, show that as the amount of Penn-II Tree-
bank material in the training set decreases, the f-
score also decreases.
6 Long Distance Dependencies
Long distance dependencies are crucial in the
proper analysis of question material. In English
wh-questions, the fronted wh-constituent refers to
an argument position of a verb inside the interrog-
ative construction. Compare the super cially sim-
ilar
1. Who1 [t1] killed Harvey Oswald?
2. Who1 did Harvey Oswald kill [t1]?
(1) queries the agent (syntactic subject) of the de-
scribed eventuality, while (2) queries the patient
(syntactic object). In the Penn-II and ATIS tree-
banks, dependencies such as these are represented
in terms of empty productions, traces and coindex-
ation in CFG tree representations (Figure 8).
SBARQ
WHNP-1
WP
Who
SQ
NP
*T*-1
VP
VBD
killed
NP
Harvey Oswald
(a)
SBARQ
WHNP-1
WP
Who
SQ
AUX
did
NP
Harvey Oswald
VP
VB
kill
NP
*T*-1
(b)
Figure 8: LDD resolved treebank style trees
With few exceptions5 the trees produced by cur-
rent treebank-based probabilistic parsers do not
represent long distance dependencies (Figure 9).
Johnson (2002) presents a tree-based method
for reconstructing LDD dependencies in Penn-
II trained parser output trees. Cahill et al.
(2004) present a method for resolving LDDs
5Collins’ Model 3 computes a limited number of wh-
dependencies in relative clause constructions.
502
SBARQ
WHNP
WP
Who
SQ
VP
VBD
killed
NP
Harvey Oswald
(a)
SBARQ
WHNP
WP
Who
SQ
AUX
did
NP
Harvey Oswald
VP
VB
kill
(b)
Figure 9: Parser output trees
at the level of Lexical-Functional Grammar f-
structure (attribute-value structure encodings of
basic predicate-argument structure or dependency
relations) without the need for empty productions
and coindexation in parse trees. Their method is
based on learning  nite approximations of func-
tional uncertainty equations (regular expressions
over paths in f-structure) from an automatically f-
structure annotated version of the Penn-II treebank
and resolves LDDs at f-structure. In our work we
use the f-structure-based method of Cahill et al.
(2004) to  reverse engineer empty productions,
traces and coindexation in parser output trees. We
explain the process by way of a worked example.
We use the parser output tree in Figure 9(a)
(without empty productions and coindexation) and
automatically annotate the tree with f-structure
information and compute LDD-resolution at the
level of f-structure using the resources of Cahill
et al. (2004). This generates the f-structure an-
notated tree6 and the LDD resolved f-structure in
Figure 10.
Note that the LDD is indicated in terms of a
reentrancy 1 between the question FOCUS and the
SUBJ function in the resolved f-structure. Given
the correspondence between the f-structure and f-
structure annotated nodes in the parse tree, we
compute that the SUBJ function newly introduced
and reentrant with the FOCUS function is an argu-
ment of the PRED ‘kill’ and the verb form ‘killed’
in the tree. In order to reconstruct the correspond-
ing empty subject NP node in the parser output
tree, we need to determine candidate anchor sites
6Lexical annotations are suppressed to aid readability.
SBARQ
WHNP
↑ FOCUS =↓
WP
↑=↓
Who
SQ
↑=↓
VP
↑=↓
VBD
↑=↓
killed
NP
↑ OBJ =↓
Harvey Oswald
(a)

FOCUS
bracketleftbig
PRED who
bracketrightbig
1
PRED ’kill〈SUBJ OBJ〉’
OBJ
bracketleftbig
PRED ’Harvey Oswald’
bracketrightbig
SUBJ
bracketleftbig
PRED ’who’
bracketrightbig
1


(b)
Figure 10: Annotated tree and f-structure
for the empty node. These anchor sites can only be
realised along the path up to the maximal projec-
tion of the governing verb indicated by "=# anno-
tations in LFG. This establishes three anchor sites:
VP, SQ and the top level SBARQ. From the auto-
matically f-structure annotated Penn-II treebank,
we extract f-structure annotated PCFG rules for
each of the three anchor sites whose RHSs contain
exactly the information (daughter categories plus
LFG annotations) in the tree in Figure 10 (in the
same order) plus an additional node (of whatever
CFG category) annotated "SUBJ=#, located any-
where within the RHSs. This will retrieve rules of
the form
VP → NP[↑ SUBJ =↓] V BD[↑=↓] NP[↑ OBJ =↓]
V P → . . .
. . .
SQ → NP[↑ SUBJ =↓] V P[↑=↓]
SQ → . . .
. . .
SBARQ → . . .
. . .
each with their associated probabilities. We select
the rule with the highest probability and cut the
rule into the tree in Figure 10 at the appropriate
anchor site (as determined by the rule LHS). In our
case this selects SQ ! NP[" SUBJ=#]V P["=#]
and the resulting tree is given in Figure 11. From
this tree, it is now easy to compute the tree with
the coindexed trace in Figure 8 (a).
In order to evaluate our empty node and coin-
dexation recovery method, we conducted two ex-
periments, one using 146 gold-standard ATIS
question trees and one using parser output on the
corresponding strings for the 146 ATIS question
trees.
503
SBARQ
WHNP-1
↑ FOCUS =↓
WP
↑=↓
Who
SQ
↑=↓
NP
↑ SUBJ =↓
-NONE-
*T*-1
VP
↑=↓
VBD
↑=↓
killed
NP
↑ OBJ =↓
Harvey Oswald
Figure 11: Resolved tree
In the  rst experiment, we delete empty nodes
and coindexation from the ATIS gold standard
trees and and reconstruct them using our method
and the preprocessed ATIS trees. In the second
experiment, we parse the strings corresponding to
the ATIS trees with Bikel’s parser and reconstruct
the empty productions and coindexation. In both
cases we evaluate against the original (unreduced)
ATIS trees and score if and only if all of inser-
tion site, inserted CFG category and coindexation
match.
Parser Output Gold Standard Trees
Precision 96.77 96.82
Recall 38.75 39.38
Table 4: Scores for LDD recovery (empty nodes
and antecedents)
Table 4 shows that currently the recall of our
method is quite low at 39.38% while the accu-
racy is very high with precision at 96.82% on the
ATIS trees. Encouragingly, evaluating parser out-
put for the same sentences shows little change in
the scores with recall at 38.75% and precision at
96.77%.
7 Conclusions
The data represented in Figure 5 show that train-
ing a parser on 50% of QuestionBank achieves an
f-score of 88.56% as against 89.24% for training
on all of QuestionBank. This implies that while
we have not reached an absolute upper bound, the
question corpus is suf ciently large that the gain
in accuracy from adding more data is so small that
it does not justify the effort.
We will evaluate grammars learned from
QuestionBank as part of a working QA sys-
tem. A beta-release of the non-LDD-resolved
QuestionBank is available for download at
http://www.computing.dcu.ie/ 
jjudge/qtreebank/4000qs.txt. The  -
nal, hand-corrected, LDD-resolved version will be
available in October 2006.
Acknowledgments
We are grateful to the anonymous reviewers for
their comments and suggestions. This research
was supported by Science Foundation Ireland
(SFI) grant 04/BR/CS0370 and an Irish Research
Council for Science Engineering and Technology
(IRCSET) PhD scholarship 2002-05.
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