Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions, pages 263–270,
Sydney, July 2006. c©2006 Association for Computational Linguistics
Discriminative Reranking for Semantic Parsing
Ruifang Ge Raymond J. Mooney
Department of Computer Sciences
University of Texas at Austin
Austin, TX 78712
{grf,mooney}@cs.utexas.edu
Abstract
Semantic parsing is the task of mapping
natural language sentences to complete
formal meaning representations. The per-
formance of semantic parsing can be po-
tentially improved by using discrimina-
tive reranking, which explores arbitrary
global features. In this paper, we investi-
gate discriminative reranking upon a base-
line semantic parser, SCISSOR, where the
composition of meaning representations is
guided by syntax. We examine if features
used for syntactic parsing can be adapted
for semantic parsing by creating similar
semantic features based on the mapping
between syntax and semantics. We re-
port experimental results on two real ap-
plications, an interpreter for coaching in-
structions in robotic soccer and a natural-
language database interface. The results
show that reranking can improve the per-
formance on the coaching interpreter, but
not on the database interface.
1 Introduction
A long-standing challenge within natural language
processing has been to understand the meaning of
natural language sentences. In comparison with
shallow semantic analysis tasks, such as word-
sense disambiguation (Ide and Jean´eronis, 1998)
and semantic role labeling (Gildea and Jurafsky,
2002; Carreras and M`arquez, 2005), which only
partially tackle this problem by identifying the
meanings of target words or finding semantic roles
of predicates, semantic parsing (Kate et al., 2005;
Ge and Mooney, 2005; Zettlemoyer and Collins,
2005) pursues a more ambitious goal – mapping
natural language sentences to complete formal
meaning representations (MRs), where the mean-
ing of each part of a sentence is analyzed, includ-
ing noun phrases, verb phrases, negation, quanti-
fiers and so on. Semantic parsing enables logic
reasoning and is critical in many practical tasks,
such as speech understanding (Zue and Glass,
2000), question answering (Lev et al., 2004) and
advice taking (Kuhlmann et al., 2004).
Ge and Mooney (2005) introduced an approach,
SCISSOR, where the composition of meaning rep-
resentations is guided by syntax. First, a statis-
tical parser is used to generate a semantically-
augmented parse tree (SAPT), where each internal
node includes both a syntactic and semantic label.
Once a SAPT is generated, an additional meaning-
composition process guided by the tree structure is
used to translate it into a final formal meaning rep-
resentation.
The performance of semantic parsing can be po-
tentially improved by using discriminative rerank-
ing, which explores arbitrary global features.
While reranking has benefited many tagging and
parsing tasks (Collins, 2000; Collins, 2002c;
Charniak and Johnson, 2005) including semantic
role labeling (Toutanova et al., 2005), it has not
yet been applied to semantic parsing. In this paper,
we investigate the effect of discriminative rerank-
ing to semantic parsing.
We examine if the features used in reranking
syntactic parses can be adapted for semantic pars-
ing, more concretely, for reranking the top SAPTs
from the baseline model SCISSOR. The syntac-
tic features introduced by Collins (2000) for syn-
tactic parsing are extended with similar semantic
features, based on the coupling of syntax and se-
mantics. We present experimental results on two
corpora: an interpreter for coaching instructions
263
in robotic soccer (CLANG) and a natural-language
database interface (GeoQuery). The best rerank-
ing model significantly improves F-measure on
CLANG from 82.3% to 85.1% (15.8% relative er-
ror reduction), however, it fails to show improve-
ments on GEOQUERY.
2 Background
2.1 Application Domains
2.1.1 CLANG: the RoboCup Coach Language
RoboCup (www.robocup.org) is an inter-
national AI research initiative using robotic soccer
as its primary domain. In the Coach Competition,
teams of agents compete on a simulated soccer
field and receive advice from a team coach in
a formal language called CLANG. In CLANG,
tactics and behaviors are expressed in terms of
if-then rules. As described in Chen et al. (2003),
its grammar consists of 37 non-terminal symbols
and 133 productions. Negation and quantifiers
like all are included in the language. Below is a
sample rule with its English gloss:
((bpos (penalty-area our))
(do (player-except our {4})
(pos (half our))))
“If the ball is in our penalty area, all our players
except player 4 should stay in our half.”
2.1.2 GEOQUERY: a DB Query Language
GEOQUERY is a logical query language for
a small database of U.S. geography containing
about 800 facts. The GEOQUERY language
consists of Prolog queries augmented with several
meta-predicates (Zelle and Mooney, 1996). Nega-
tion and quantifiers like all and each are included
in the language. Below is a sample query with its
English gloss:
answer(A,count(B,(city(B),loc(B,C),
const(C,countryid(usa))),A))
“How many cities are there in the US?”
2.2 SCISSOR: the Baseline Model
SCISSOR is based on a fairly standard approach
to compositional semantics (Jurafsky and Martin,
2000). First, a statistical parser is used to con-
struct a semantically-augmented parse tree that
captures the semantic interpretation of individual
NP-PLAYER
PRP$-TEAM
our
NN-PLAYER
player
CD-UNUM
2
Figure 1: A SAPT for describing a simple CLANG
concept PLAYER .
words and the basic predicate-argument structure
of a sentence. Next, a recursive deterministic pro-
cedure is used to compose the MR of a parent
node from the MR of its children following the
tree structure.
Figure 1 shows the SAPT for a simple natural
language phrase describing the concept PLAYER
in CLANG. We can see that each internal node
in the parse tree is annotated with a semantic la-
bel (shown after dashes) representing concepts in
an application domain; when a node is semanti-
cally vacuous in the application domain, it is as-
signed with the semantic label NULL. The seman-
tic labels on words and non-terminal nodes repre-
sent the meanings of these words and constituents
respectively. For example, the word our repre-
sents a TEAM concept in CLANG with the value
our, whereas the constituent OUR PLAYER 2 rep-
resents a PLAYER concept. Some type concepts
do not take arguments, like team and unum (uni-
form number), while some concepts, which we
refer to as predicates, take an ordered list of ar-
guments, like player which requires both a TEAM
and a UNUM as its arguments.
SAPTs are given to a meaning composition
process to compose meaning, guided by both
tree structures and domain predicate-argument re-
quirements. In figure 1, the MR of our and 2
would fill the arguments of PLAYER to generate
the MR of the whole constituent PLAYER(OUR,2)
using this process.
SCISSOR is implemented by augmenting
Collins’ (1997) head-driven parsing model II to
incorporate the generation of semantic labels on
internal nodes. In a head-driven parsing model,
a tree can be seen as generated by expanding
non-terminals with grammar rules recursively.
To deal with the sparse data problem, the expan-
sion of a non-terminal (parent) is decomposed
into primitive steps: a child is chosen as the
head and is generated first, and then the other
children (modifiers) are generated independently
264
BACK-OFFLEVEL PL1(Li|...)
1 P,H,w,t,∆,LC
2 P,H,t,∆,LC
3 P,H,∆,LC
4 P,H
5 P
Table 1: Extended back-off levels for the semantic
parameter PL1(Li|...), using the same notation as
in Ge and Mooney (2005). The symbols P, H and
Li are the semantic label of the parent , head, and
the ith left child, w is the head word of the parent,
t is the semantic label of the head word, δ is the
distance between the head and the modifier, and
LC is the left semantic subcat.
constrained by the head. Here, we only describe
changes made to SCISSOR for reranking, for a
full description of SCISSOR see Ge and Mooney
(2005).
In SCISSOR, the generation of semantic labels
on modifiers are constrained by semantic subcat-
egorization frames, for which data can be very
sparse. An example of a semantic subcat in Fig-
ure 1 is that the head PLAYER associated with NN
requires a TEAM as its modifier. Although this
constraint improves SCISSOR’s precision, which
is important for semantic parsing, it also limits
its recall. To generate plenty of candidate SAPTs
for reranking, we extended the back-off levels for
the parameters generating semantic labels of mod-
ifiers. The new set is shown in Table 1 using the
parameters for the generation of the left-side mod-
ifiers as an example. The back-off levels 4 and 5
are newly added by removing the constraints from
the semantic subcat. Although the best SAPTs
found by the model may not be as precise as be-
fore, we expect that reranking can improve the re-
sults and rank correct SAPTs higher.
2.3 The Averaged Perceptron Reranking
Model
Averaged perceptron (Collins, 2002a) has been
successfully applied to several tagging and parsing
reranking tasks (Collins, 2002c; Collins, 2002a),
and in this paper, we employed it in reranking
semantic parses generated by the base semantic
parser SCISSOR. The model is composed of three
parts (Collins, 2002a): a set of candidate SAPTs
GEN, which is the top n SAPTs of a sentence
from SCISSOR; a function Φ that maps a sentence
Inputs: A set of training examples (xi,y∗i ), i = 1...n, where xi
is a sentence, and y∗i is a candidate SAPT that has the highest
similarity score with the gold-standard SAPT
Initialization: Set ¯W = 0
Algorithm:
For t = 1...T,i = 1...n
Calculate yi = argmaxy∈GEN(xi) Φ(xi,y) · ¯W
If (yi negationslash= y∗i ) then ¯W = ¯W +Φ(xi,y∗i ) − Φ(xi,yi)
Output: The parameter vector ¯W
Figure 2: The perceptron training algorithm.
x and its SAPT y into a feature vector Φ(x,y) ∈
Rd; and a weight vector ¯W associated with the set
of features. Each feature in a feature vector is a
function on a SAPT that maps the SAPT to a real
value. The SAPT with the highest score under a
parameter vector ¯W is outputted, where the score
is calculated as:
score(x,y) = Φ(x,y)· ¯W (1)
The perceptron training algorithm for estimat-
ing the parameter vector ¯W is shown in Fig-
ure 2. For a full description of the algorithm,
see (Collins, 2002a). The averaged perceptron, a
variant of the perceptron algorithm is often used in
testing to decrease generalization errors on unseen
test examples, where the parameter vectors used
in testing is the average of each parameter vector
generated during the training process.
3 Features for Reranking SAPTs
In our setting, reranking models discriminate be-
tween SAPTs that can lead to correct MRs and
those that can not. Intuitively, both syntactic and
semantic features describing the syntactic and se-
mantic substructures of a SAPT would be good in-
dicators of the SAPT’s correctness.
The syntactic features introduced by Collins
(2000) for reranking syntactic parse trees have
been proven successfully in both English and
Spanish (Cowan and Collins, 2005). We exam-
ine if these syntactic features can be adapted for
semantic parsing by creating similar semantic fea-
tures. In the following section, we first briefly de-
scribe the syntactic features introduced by Collins
(2000), and then introduce two adapted semantic
feature sets. A SAPT in CLANG is shown in Fig-
ure 3 for illustrating the features throughout this
section.
265
VP-ACTION.PASS
VB
be
VP-ACTION.PASS
VBN-ACTION.PASS
passed
PP-POINT
TO
to
NP-POINT
PRN-POINT
-LRB–POINT
(
NP-NUM1
CD-NUM
36
COMMA
,
NP-NUM2
CD-NUM
10
-RRB-
)
Figure 3: A SAPT for illustrating the reranking features, where the syntactic label “,” is replaced by
COMMA for a clearer description of features, and the NULL semantic labels are not shown. The head
of the rule “PRN-POINT→ -LRB–POINT NP-NUM1 COMMA NP-NUM2 -RRB-” is -LRB–POINT. The
semantic labels NUM1 and NUM2 are meta concepts in CLANG specifying the semantic role filled since
NUM can fill multiple semantic roles in the predicate POINT.
3.1 Syntactic Features
All syntactic features introduced by Collins (2000)
are included for reranking SAPTs. While the full
description of all the features is beyond the scope
of this paper, we still introduce several feature
types here for the convenience of introducing se-
mantic features later.
1. Rules. These are the counts of unique syntac-
tic context-free rules in a SAPT. The example
in Figure 3 has the feature f(PRN→ -LRB- NP
COMMA NP -RRB-)=1.
2. Bigrams. These are the counts of unique
bigrams of syntactic labels in a constituent.
They are also featured with the syntactic la-
bel of the constituent, and the bigram’s rel-
ative direction (left, right) to the head of the
constituent. The example in Figure 3 has the
feature f(NP COMMA, right, PRN)=1.
3. Grandparent Rules. These are the same as
Rules, but also include the syntactic label
above a rule. The example in Figure 3 has
the feature f([PRN→ -LRB- NP COMMA NP
-RRB-], NP)=1, where NP is the syntactic la-
bel above the rule “PRN→ -LRB- NP COMMA
NP -RRB-”.
4. Grandparent Bigrams. These are the same
as Bigrams, but also include the syntactic
label above the constituent containing a bi-
gram. The example in Figure 3 has the
feature f([NP COMMA, right, PRN], NP)=1,
where NP is the syntactic label above the con-
stituent PRN.
3.2 Semantic Features
3.2.1 Semantic Feature Set I
A similar semantic feature type is introduced for
each syntactic feature type used by Collins (2000)
by replacing syntactic labels with semantic ones
(with the semantic label NULL not included). The
corresponding semantic feature types for the fea-
tures in Section 3.1 are:
1. Rules. The example in Figure 3 has the fea-
ture f(POINT→ POINT NUM1 NUM2)=1.
2. Bigrams. The example in Figure 3 has the
feature f(NUM1 NUM2, right, POINT)=1,
where the bigram “NUM1 NUM2”appears to
the right of the head POINT.
3. Grandparent Rules. The example in Figure 3
has the feature f([POINT→ POINT NUM1
NUM2], POINT)=1, where the last POINT is
266
ACTION.PASS
ACTION.PASS
passed
POINT
POINT
(
NUM1
NUM
36
NUM2
NUM
10
Figure 4: The tree generated by removing purely-
syntactic nodes from the SAPT in Figure 3 (with
syntactic labels omitted.)
the semantic label above the semantic rule
“POINT→ POINT NUM1 NUM2”.
4. Grandparent Bigrams. The example in Fig-
ure 3 has the feature f([NUM1 NUM2, right,
POINT], POINT)=1, where the last POINT is
the semantic label above the POINT associ-
ated with PRN.
3.2.2 Semantic Feature Set II
Purely-syntactic structures in SAPTs exist with
no meaning composition involved, such as the ex-
pansions from NP to PRN, and from PP to “TO NP”
in Figure 3. One possible drawback of the seman-
tic features derived directly from SAPTs as in Sec-
tion 3.2.1 is that they could include features with
no meaning composition involved, which are in-
tuitively not very useful. For example, the nodes
with purely-syntactic expansions mentioned above
would trigger a semantic rule feature with mean-
ing unchanged (from POINT to POINT). Another
possible drawback of these features is that the fea-
tures covering broader context could potentially
fail to capture the real high-level meaning compo-
sition information. For example, the Grandparent
Rule example in Section 3.2.1 has POINT as the
semantic grandparent of a POINT composition, but
not the real one ACTION.PASS.
To address these problems, another semantic
feature set is introduced by deriving semantic fea-
tures from trees where purely-syntactic nodes of
SAPTs are removed (the resulting tree for the
SAPT in Figure 3 is shown in Figure 4). In this
tree representation, the example in Figure 4 would
have the Grandparent Rule feature f([POINT→
POINT NUM1 NUM2], ACTION.PASS)=1, with the
correct semantic grandparent ACTION.PASS in-
cluded.
4 Experimental Evaluation
4.1 Experimental Methodology
Two corpora of natural language sentences paired
with MRs were used in the reranking experiments.
For CLANG, 300 pieces of coaching advice were
randomly selected from the log files of the 2003
RoboCup Coach Competition. Each formal in-
struction was translated into English by one of
four annotators (Kate et al., 2005). The average
length of an natural language sentence in this cor-
pus is 22.52 words. For GEOQUERY, 250 ques-
tions were collected by asking undergraduate stu-
dents to generate English queries for the given
database. Queries were then manually translated
into logical form (Zelle and Mooney, 1996). The
average length of a natural language sentence in
this corpus is 6.87 words.
We adopted standard 10-fold cross validation
for evaluation: 9/10 of the whole dataset was used
for training (training set), and 1/10 for testing (test
set). To train a reranking model on a training set,
a separate “internal” 10-fold cross validation over
the training set was employed to generate n-best
SAPTs for each training example using a base-
line learner, where each training set was again
separated into 10 folds with 9/10 for training the
baseline learner, and 1/10 for producing the n-
best SAPTs for training the reranker. Reranking
models trained in this way ensure that the n-best
SAPTs for each training example are not gener-
ated by a baseline model that has already seen that
example. To test a reranking model on a test set, a
baseline model trained on a whole training set was
used to generate n-best SAPTs for each test ex-
ample, and then the reranking model trained with
the above method was used to choose a best SAPT
from the candidate SAPTs.
The performance of semantic parsing was mea-
sured in terms of precision (the percentage of com-
pleted MRs that were correct), recall (the percent-
age of all sentences whose MRs were correctly
generated) and F-measure (the harmonic mean of
precision and recall). Since even a single mistake
in an MR could totally change the meaning of an
example (e.g. having OUR in an MR instead of OP-
PONENT in CLANG), no partial credit was given
for examples with partially-correct SAPTs.
Averaged perceptron (Collins, 2002a), which
has been successfully applied to several tag-
ging and parsing reranking tasks (Collins, 2002c;
Collins, 2002a), was employed for training rerank-
267
CLANG GEOQUERY
P R F P R F
SCISSOR 89.5 73.7 80.8 98.5 74.4 84.8
SCISSOR+ 87.0 78.0 82.3 95.5 77.2 85.4
Table 2: The performance of the baseline model SCISSOR+ compared with SCISSOR (with the best result in
bold), where P = precision, R = recall, and F = F-measure.
n 1 2 5 10 20 50
CLANG 78.0 81.3 83.0 84.0 85.0 85.3
GEOQUERY 77.2 77.6 80.0 81.2 81.6 81.6
Table 3: Oracle recalls on CLANG and GEOQUERY as a function of number n of n-best SAPTs.
ing models. To choose the correct SAPT of a
training example required for training the aver-
aged perceptron, we selected a SAPT that results
in the correct MR; if multiple such SAPTs exist,
the one with the highest baseline score was cho-
sen. Since no partial credit was awarded in evalua-
tion, a training example was discarded if it had no
correct SAPT. Rerankers were trained on the 50-
best SAPTs provided by SCISSOR, and the num-
ber of perceptron iterations over the training exam-
ples was limited to 10. Typically, in order to avoid
over-fitting, reranking features are filtered by re-
moving those occurring in less than some mini-
mal number of training examples. We only re-
moved features that never occurred in the training
data since experiments with higher cut-offs failed
to show any improvements.
4.2 Results
4.2.1 Baseline Results
Table 2 shows the results comparing the base-
line learner SCISSOR using both the back-off pa-
rameters in Ge and Mooney (2005) (SCISSOR) and
the revised parameters in Section 2.2 (SCISSOR+).
As we expected, SCISSOR+ has better recall and
worse precision than SCISSOR on both corpora
due to the additional levels of back-off. SCISSOR+
is used as the baseline model for all reranking ex-
periments in the next section.
Table 3 gives oracle recalls for CLANG and
GEOQUERY where an oracle picks the correct
parse from the n-best SAPTs if any of them are
correct. Results are shown for increasing values
of n. The trends for CLANG and GEOQUERY are
different: small values of n show significant im-
provements for CLANG, while a larger n is needed
to improve results for GEOQUERY.
4.2.2 Reranking Results
In this section, we describe the experiments
with reranking models utilizing different feature
sets. All models include the score assigned to a
SAPT by the baseline model as a special feature.
Table 4 shows results using different feature sets
derived directly from SAPTs. In general, rerank-
ing improves the performance of semantic parsing
on CLANG, but not on GEOQUERY. This could
be explained by the different oracle recall trends of
CLANG and GEOQUERY. We can see that in Ta-
ble 3, even a small n can increase the oracle score
on CLANG significantly, but not on GEOQUERY.
With the baseline score included as a feature, cor-
rect SAPTs closer to the top are more likely to
be reranked to the top than the ones in the back,
thus CLANG is more likely to have more sentences
reranked correct than GEOQUERY. On CLANG,
using the semantic feature set alone achieves the
best improvements over the baseline with 2.8%
absolute improvement in F-measure (15.8% rel-
ative error reduction), which is significant at the
95% confidence level using a paired Student’s t-
test. Nevertheless, the difference between SEM1
and SYN+SEM1 is very small (only one example).
Using syntactic features alone only slightly im-
proves the results because the syntactic features
do not directly discriminate between correct and
incorrect meaning representations. To put this
in perspective, Charniak and Johnson (2005) re-
ported that reranking improves the F-measure of
syntactic parsing from 89.7% to 91.0% with a 50-
best oracle F-measure score of 96.8%.
Table 5 compares results using semantic fea-
tures directly derived from SAPTs (SEM1), and
from trees with purely-syntactic nodes removed
(SEM2). It compares reranking models using these
268
CLANG GEOQUERY
P R F P R F
SCISSOR+ 87.0 78.0 82.3 95.5 77.2 85.4
SYN 87.7 78.7 83.0 95.5 77.2 85.4
SEM1 90.0(23.1) 80.7(12.3) 85.1(15.8) 95.5 76.8 85.1
SYN+SEM1 89.6 80.3 84.7 95.5 76.4 84.9
Table 4: Reranking results on CLANG and GEOQUERY using different feature sets derived directly from
SAPTs (with the best results in bold and relative error reduction in parentheses). The reranking model
SYN uses the syntactic feature set in Section 3.1, SEM1 uses the semantic feature set in Section 3.2.1, and
SYN+SEM1 uses both.
CLANG GEOQUERY
P R F P R F
SEM1 90.0 80.7 85.1 95.5 76.8 85.1
SEM2 88.1 79.0 83.3 96.0 77.2 85.6
SEM1+SEM2 88.5 79.3 83.7 95.5 76.4 84.9
SYN+SEM1 89.6 80.3 84.7 95.5 76.4 84.9
SYN+SEM2 88.1 79.0 83.3 95.5 76.8 85.1
SYN+SEM1+SEM2 88.9 79.7 84.0 95.5 76.4 84.9
Table 5: Reranking results on CLANG and GEOQUERY comparing semantic features derived directly from
SAPTs, and semantic features from trees with purely-syntactic nodes removed. The symbol SEM1 and SEM2
refer to the semantic feature sets in Section 3.2.1 and 3.2.1 respectively, and SYN refers to the syntactic
feature set in Section 3.1.
feature sets alone and together, and using them
along with the syntactic feature set (SYN) alone
and together. Overall, SEM1 provides better results
than SEM2 on CLANG and slightly worse results
on GEOQUERY (only in one sentence), regard-
less of whether or not syntactic features are in-
cluded. Using both semantic feature sets does not
improve the results over just using SEM1. On one
hand, the better performance of SEM1 on CLANG
contradicts our expectation because of the reasons
discussed in Section 3.2.2; the reason behind this
needs to be investigated. On the other hand, how-
ever, it also suggests that the semantic features de-
rived directly from SAPTs can provide good evi-
dence for semantic correctness, even with redun-
dant purely syntactically motivated features.
We have also informally experimented with
smoothed semantic features utilizing domain on-
tology given by CLANG, which did not show im-
provements over reranking models not using these
features.
5 Conclusion
We have applied discriminative reranking to se-
mantic parsing, where reranking features are de-
veloped from features for reranking syntactic
parses based on the coupling of syntax and se-
mantics. The best reranking model significantly
improves F-measure on a Robocup coaching task
(CLANG) from 82.3% to 85.1%, while it fails to
improve the performance on a geography database
query task (GEOQUERY).
Future work includes further investigation of
the reasons behind the different utility of rerank-
ing for the CLANG and GEOQUERY tasks. We
also plan to explore other types of reranking
features, such as the features used in semantic
role labeling (SRL) (Gildea and Jurafsky, 2002;
Carreras and M`arquez, 2005), like the path be-
tween a target predicate and its argument, and
kernel methods (Collins, 2002b). Experimenting
with other effective reranking algorithms, such as
SVMs (Joachims, 2002) and MaxEnt (Charniak
and Johnson, 2005), is also a direction of our fu-
ture research.
6 Acknowledgements
We would like to thank Rohit J. Kate and anony-
mous reviewers for their insightful comments.
This research was supported by Defense Ad-
269
vanced Research Projects Agency under grant
HR0011-04-1-0007.
References
Xavier Carreras and Lu´ıs M`arquez. 2005. Introduc-
tion to the CoNLL-2005 shared task: Semantic role
labeling. In Proc. of 9th Conf. on Computational
Natural Language Learning (CoNLL-2005), pages
152–164, Ann Arbor, MI, June.
Eugene Charniak and Mark Johnson. 2005. Coarse-
to-fine n-best parsing and MaxEnt discriminative
reranking. In Proc. of the 43nd Annual Meeting
of the Association for Computational Linguistics
(ACL-05), pages 173–180, Ann Arbor, MI, June.
Mao Chen, Ehsan Foroughi, Fredrik Heintz, Spiros
Kapetanakis, Kostas Kostiadis, Johan Kummeneje,
Itsuki Noda, Oliver Obst, Patrick Riley, Timo Stef-
fens, Yi Wang, and Xiang Yin. 2003. Users
manual: RoboCup soccer server manual for soccer
server version 7.07 and later. Available athttp://
sourceforge.net/projects/sserver/.
Michael J. Collins. 1997. Three generative, lexicalised
models for statistical parsing. In Proc. of the 35th
Annual Meeting of the Association for Computa-
tional Linguistics (ACL-97), pages 16–23.
Michael Collins. 2000. Discriminative reranking for
natural language parsing. In Proc. of 17th Intl. Conf.
on Machine Learning (ICML-2000), pages 175–182,
Stanford, CA, June.
Michael Collins. 2002a. Discriminative training meth-
ods for hidden Markov models: Theory and exper-
iments with perceptron algorithms. In Proc. of the
2002 Conf. on Empirical Methods in Natural Lan-
guage Processing (EMNLP-02), Philadelphia, PA,
July.
Michael Collins. 2002b. New ranking algorithms for
parsing and tagging: Kernels over discrete struc-
tures, and the voted perceptron. In Proc. of the
40th Annual Meeting of the Association for Com-
putational Linguistics (ACL-2002), pages 263–270,
Philadelphia, PA, July.
Michael Collins. 2002c. Ranking algorithms for
named-entity extraction: Boosting and the voted
perceptron. In Proc. of the 40th Annual Meet-
ing of the Association for Computational Linguistics
(ACL-2002), pages 489–496, Philadelphia, PA.
Brooke Cowan and Michael Collins. 2005. Mor-
phology and reranking for the statistical parsing of
Spanish. In Proc. of the Human Language Technol-
ogy Conf. and Conf. on Empirical Methods in Nat-
ural Language Processing (HLT/EMNLP-05), Van-
couver, B.C., Canada, October.
Ruifang Ge and Raymond J. Mooney. 2005. A statis-
tical semantic parser that integrates syntax and se-
mantics. In Proc. of 9th Conf. on Computational
Natural Language Learning (CoNLL-2005), pages
9–16, Ann Arbor, MI, July.
Daniel Gildea and Daniel Jurafsky. 2002. Automated
labeling of semantic roles. Computational Linguis-
tics, 28(3):245–288.
Nancy A. Ide and Jean´eronis. 1998. Introduction to
the special issue on word sense disambiguation: The
state of the art. Computational Linguistics, 24(1):1–
40.
Thorsten Joachims. 2002. Optimizing search en-
gines using clickthrough data. In Proc. of 8th ACM
SIGKDD Intl. Conf. on Knowledge Discovery and
Data Mining (KDD-2002), Edmonton, Canada.
Daniel Jurafsky and James H. Martin. 2000. Speech
and Language Processing: An Introduction to Nat-
ural Language Processing, Computational Linguis-
tics, and Speech Recognition. Prentice Hall, Upper
Saddle River, NJ.
R. J. Kate, Y. W. Wong, and R. J. Mooney. 2005.
Learning to transform natural to formal languages.
In Proc. of 20th Natl. Conf. on Artificial Intelli-
gence (AAAI-2005), pages 1062–1068, Pittsburgh,
PA, July.
Gregory Kuhlmann, Peter Stone, Raymond J. Mooney,
and Jude W. Shavlik. 2004. Guiding a reinforce-
ment learner with natural language advice: Initial
results in RoboCup soccer. In Proc. of the AAAI-04
Workshop on Supervisory Control of Learning and
Adaptive Systems, San Jose, CA, July.
Iddo Lev, Bill MacCartney, Christopher D. Manning,
and Roger Levy. 2004. Solving logic puzzles: From
robust processing to precise semantics. In Proc. of
2nd Workshop on Text Meaning and Interpretation,
ACL-04, Barcelona, Spain.
Kristina Toutanova, Aria Haghighi, and Christopher D.
Manning. 2005. Joint learning improves semantic
role labeling. In Proc. of the 43nd Annual Meet-
ing of the Association for Computational Linguistics
(ACL-05), Ann Arbor, MI, June.
John M. Zelle and Raymond J. Mooney. 1996. Learn-
ing to parse database queries using inductive logic
programming. In Proc. of 13th Natl. Conf. on Artifi-
cial Intelligence (AAAI-96), pages 1050–1055, Port-
land, OR, August.
Luke S. Zettlemoyer and Michael Collins. 2005.
Learning to map sentences to logical form: Struc-
tured classification with probabilistic categorial
grammars. In Proc. of 21th Conf. on Uncertainty in
Artificial Intelligence (UAI-2005), Edinburgh, Scot-
land, July.
Victor W. Zue and James R. Glass. 2000. Conversa-
tional interfaces: Advances and challenges. In Proc.
of the IEEE, volume 88(8), pages 1166–1180.
270
