Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 337–344,
Sydney, July 2006. c©2006 Association for Computational Linguistics
Reranking and Self-Training for Parser Adaptation
David McClosky, Eugene Charniak, and Mark Johnson
Brown Laboratory for Linguistic Information Processing (BLLIP)
Brown University
Providence, RI 02912
{dmcc|ec|mj}@cs.brown.edu
Abstract
Statistical parsers trained and tested on the
Penn Wall Street Journal (WSJ) treebank
have shown vast improvements over the
last 10 years. Much of this improvement,
however, is based upon an ever-increasing
number of features to be trained on (typi-
cally) the WSJ treebank data. This has led
to concern that such parsers may be too
finely tuned to this corpus at the expense
of portability to other genres. Such wor-
ries have merit. The standard “Charniak
parser” checks in at a labeled precision-
recall f-measure of 89.7% on the Penn
WSJ test set, but only 82.9% on the test set
from the Brown treebank corpus.
This paper should allay these fears. In par-
ticular, we show that the reranking parser
described in Charniak and Johnson (2005)
improves performance of the parser on
Brown to 85.2%. Furthermore, use of the
self-training techniques described in (Mc-
Closky et al., 2006) raise this to 87.8%
(an error reduction of 28%) again with-
out any use of labeled Brown data. This
is remarkable since training the parser and
reranker on labeled Brown data achieves
only 88.4%.
1 Introduction
Modern statistical parsers require treebanks to
train their parameters, but their performance de-
clines when one parses genres more distant from
the training data’s domain. Furthermore, the tree-
banks required to train said parsers are expensive
and difficult to produce.
Naturally, one of the goals of statistical parsing
is to produce a broad-coverage parser which is rel-
atively insensitive to textual domain. But the lack
of corpora has led to a situation where much of
the current work on parsing is performed on a sin-
gle domain using training data from that domain
— the Wall Street Journal (WSJ) section of the
Penn Treebank (Marcus et al., 1993). Given the
aforementioned costs, it is unlikely that many sig-
nificant treebanks will be created for new genres.
Thus, parser adaptation attempts to leverage ex-
isting labeled data from one domain and create a
parser capable of parsing a different domain.
Unfortunately, the state of the art in parser
portability (i.e. using a parser trained on one do-
main to parse a different domain) is not good. The
“Charniak parser” has a labeled precision-recall
f-measure of 89.7% on WSJ but a lowly 82.9%
on the test set from the Brown corpus treebank.
Furthermore, the treebanked Brown data is mostly
general non-fiction and much closer to WSJ than,
e.g., medical corpora would be. Thus, most work
on parser adaptation resorts to using some labeled
in-domain data to fortify the larger quantity of out-
of-domain data.
In this paper, we present some encouraging re-
sults on parser adaptation without any in-domain
data. (Though we also present results with in-
domain data as a reference point.) In particular we
note the effects of two comparatively recent tech-
niques for parser improvement.
The first of these, parse-reranking (Collins,
2000; Charniak and Johnson, 2005) starts with a
“standard” generative parser, but uses it to gener-
ate the n-best parses rather than a single parse.
Then a reranking phase uses more detailed fea-
tures, features which would (mostly) be impossi-
ble to incorporate in the initial phase, to reorder
337
the list and pick a possibly different best parse.
At first blush one might think that gathering even
more fine-grained features from a WSJ treebank
would not help adaptation. However, we find that
reranking improves the parsers performance from
82.9% to 85.2%.
The second technique is self-training — pars-
ing unlabeled data and adding it to the training
corpus. Recent work, (McClosky et al., 2006),
has shown that adding many millions of words
of machine parsed and reranked LA Times arti-
cles does, in fact, improve performance of the
parser on the closely related WSJ data. Here we
show that it also helps the father-afield Brown
data. Adding it improves performance yet-again,
this time from 85.2% to 87.8%, for a net error re-
duction of 28%. It is interesting to compare this to
our results for a completely Brown trained system
(i.e. one in which the first-phase parser is trained
on just Brown training data, and the second-phase
reranker is trained on Brown 50-best lists). This
system performs at a 88.4% level — only slightly
higher than that achieved by our system with only
WSJ data.
2 Related Work
Work in parser adaptation is premised on the as-
sumption that one wants a single parser that can
handle a wide variety of domains. While this is the
goal of the majority of parsing researchers, it is not
quite universal. Sekine (1997) observes that for
parsing a specific domain, data from that domain
is most beneficial, followed by data from the same
class, data from a different class, and data from
a different domain. He also notes that different
domains have very different structures by looking
at frequent grammar productions. For these rea-
sons he takes the position that we should, instead,
simply create treebanks for a large number of do-
mains. While this is a coherent position, it is far
from the majority view.
There are many different approaches to parser
adaptation. Steedman et al. (2003) apply co-
training to parser adaptation and find that co-
training can work across domains. The need to
parse biomedical literature inspires (Clegg and
Shepherd, 2005; Lease and Charniak, 2005).
Clegg and Shepherd (2005) provide an extensive
side-by-side performance analysis of several mod-
ern statistical parsers when faced with such data.
They find that techniques which combine differ-
Training Testing f-measureGildea Bacchiani
WSJ WSJ 86.4 87.0
WSJ Brown 80.6 81.1
Brown Brown 84.0 84.7
WSJ+Brown Brown 84.3 85.6
Table 1: Gildea and Bacchiani results on WSJ and
Brown test corpora using different WSJ and Brown
training sets. Gildea evaluates on sentences of
length ≤ 40, Bacchiani on all sentences.
ent parsers such as voting schemes and parse se-
lection can improve performance on biomedical
data. Lease and Charniak (2005) use the Charniak
parser for biomedical data and find that the use of
out-of-domain trees and in-domain vocabulary in-
formation can considerably improve performance.
However, the work which is most directly com-
parable to ours is that of (Ratnaparkhi, 1999; Hwa,
1999; Gildea, 2001; Bacchiani et al., 2006). All
of these papers look at what happens to mod-
ern WSJ-trained statistical parsers (Ratnaparkhi’s,
Collins’, Gildea’s and Roark’s, respectively) as
training data varies in size or usefulness (because
we are testing on something other than WSJ). We
concentrate particularly on the work of (Gildea,
2001; Bacchiani et al., 2006) as they provide re-
sults which are directly comparable to those pre-
sented in this paper.
Looking at Table 1, the first line shows us
the standard training and testing on WSJ — both
parsers perform in the 86-87% range. The next
line shows what happens when parsing Brown us-
ing a WSJ-trained parser. As with the Charniak
parser, both parsers take an approximately 6% hit.
It is at this point that our work deviates from
these two papers. Lacking alternatives, both
(Gildea, 2001) and (Bacchiani et al., 2006) give
up on adapting a pure WSJ trained system, instead
looking at the issue of how much of an improve-
ment one gets over a pure Brown system by adding
WSJ data (as seen in the last two lines of Table 1).
Both systems use a “model-merging” (Bacchiani
et al., 2006) approach. The different corpora are,
in effect, concatenated together. However, (Bac-
chiani et al., 2006) achieve a larger gain by weight-
ing the in-domain (Brown) data more heavily than
the out-of-domain WSJ data. One can imagine, for
instance, five copies of the Brown data concate-
nated with just one copy of WSJ data.
338
3 Corpora
We primarily use three corpora in this paper. Self-
training requires labeled and unlabeled data. We
assume that these sets of data must be in similar
domains (e.g. news articles) though the effective-
ness of self-training across domains is currently an
open question. Thus, we have labeled (WSJ) and
unlabeled (NANC) out-of-domain data and labeled
in-domain data (BROWN). Unfortunately, lacking
a corresponding corpus to NANC for BROWN, we
cannot perform the opposite scenario and adapt
BROWN to WSJ.
3.1 Brown
The BROWN corpus (Francis and Kuˇcera, 1979)
consists of many different genres of text, intended
to approximate a “balanced” corpus. While the
full corpus consists of fiction and nonfiction do-
mains, the sections that have been annotated in
Treebank II bracketing are primarily those con-
taining fiction. Examples of the sections annotated
include science fiction, humor, romance, mystery,
adventure, and “popular lore.” We use the same
divisions as Bacchiani et al. (2006), who base
their divisions on Gildea (2001). Each division of
the corpus consists of sentences from all available
genres. The training division consists of approx-
imately 80% of the data, while held-out develop-
ment and testing divisions each make up 10% of
the data. The treebanked sections contain approx-
imately 25,000 sentences (458,000 words).
3.2 Wall Street Journal
Our out-of-domain data is the Wall Street Journal
(WSJ) portion of the Penn Treebank (Marcus et al.,
1993) which consists of about 40,000 sentences
(one million words) annotated with syntactic in-
formation. We use the standard divisions: Sec-
tions 2 through 21 are used for training, section 24
for held-out development, and section 23 for final
testing.
3.3 North American News Corpus
In addition to labeled news data, we make use
of a large quantity of unlabeled news data. The
unlabeled data is the North American News Cor-
pus, NANC (Graff, 1995), which is approximately
24 million unlabeled sentences from various news
sources. NANC contains no syntactic information
and sentence boundaries are induced by a simple
discriminative model. We also perform some basic
cleanups on NANC to ease parsing. NANC contains
news articles from various news sources including
the Wall Street Journal, though for this paper, we
only use articles from the LA Times portion.
To use the data from NANC, we use self-training
(McClosky et al., 2006). First, we take a WSJ
trained reranking parser (i.e. both the parser and
reranker are built from WSJ training data) and
parse the sentences from NANC with the 50-best
(Charniak and Johnson, 2005) parser. Next, the
50-best parses are reordered by the reranker. Fi-
nally, the 1-best parses after reranking are com-
bined with the WSJ training set to retrain the first-
stage parser.1 McClosky et al. (2006) find that the
self-trained models help considerably when pars-
ing WSJ.
4 Experiments
We use the Charniak and Johnson (2005) rerank-
ing parser in our experiments. Unless mentioned
otherwise, we use the WSJ-trained reranker (as op-
posed to a BROWN-trained reranker). To evaluate,
we report bracketing f-scores.2 Parser f-scores
reported are for sentences up to 100 words long,
while reranking parser f-scores are over all sen-
tences. For simplicity and ease of comparison,
most of our evaluations are performed on the de-
velopment section of BROWN.
4.1 Adapting self-training
Our first experiment examines the performance
of the self-trained parsers. While the parsers are
created entirely from labeled WSJ data and unla-
beled NANC data, they perform extremely well on
BROWN development (Table 2). The trends are the
same as in (McClosky et al., 2006): Adding NANC
data improves parsing performance on BROWN
development considerably, improving the f-score
from 83.9% to 86.4%. As more NANC data is
added, the f-score appears to approach an asymp-
tote. The NANC data appears to help reduce data
sparsity and fill in some of the gaps in the WSJ
model. Additionally, the reranker provides fur-
ther benefit and adds an absolute 1-2% to the f-
score. The improvements appear to be orthogonal,
as our best performance is reached when we use
the reranker and add 2,500k self-trained sentences
from NANC.
1We trained a new reranker from this data as well, but it
does not seem to get significantly different performance.
2The harmonic mean of labeled precision (P) and labeled
recall (R), i.e. f = 2×P×RP+R
339
Sentences added Parser Reranking Parser
Baseline BROWN 86.4 87.4
Baseline WSJ 83.9 85.8
WSJ+50k 84.8 86.6
WSJ+250k 85.7 87.2
WSJ+500k 86.0 87.3
WSJ+750k 86.1 87.5
WSJ+1,000k 86.2 87.3
WSJ+1,500k 86.2 87.6
WSJ+2,000k 86.1 87.7
WSJ+2,500k 86.4 87.7
Table 2: Effects of adding NANC sentences to WSJ
training data on parsing performance. f-scores
for the parser with and without the WSJ reranker
are shown when evaluating on BROWN develop-
ment. For this experiment, we use the WSJ-trained
reranker.
The results are even more surprising when we
compare against a parser3 trained on the labeled
training section of the BROWN corpus, with pa-
rameters tuned against its held-out section. De-
spite seeing no in-domain data, the WSJ based
parser is able to match the BROWN based parser.
For the remainder of this paper, we will refer
to the model trained on WSJ+2,500k sentences of
NANC as our “best WSJ+NANC” model. We also
note that this “best” parser is different from the
“best” parser for parsing WSJ, which was trained
on WSJ with a relative weight4 of 5 and 1,750k
sentences from NANC. For parsing BROWN, the
difference between these two parsers is not large,
though.
Increasing the relative weight of WSJ sentences
versus NANC sentences when testing on BROWN
development does not appear to have a significant
effect. While (McClosky et al., 2006) showed that
this technique was effective when testing on WSJ,
the true distribution was closer to WSJ so it made
sense to emphasize it.
4.2 Incorporating In-Domain Data
Up to this point, we have only considered the sit-
uation where we have no in-domain data. We now
3In this case, only the parser is trained on BROWN. In sec-
tion 4.3, we compare against a fully BROWN-trained rerank-
ing parser as well.
4A relative weight of n is equivalent to using n copies of
the corpus, i.e. an event that occurred x times in the corpus
would occur x×n times in the weighted corpus. Thus, larger
corpora will tend to dominate smaller corpora of the same
relative weight in terms of event counts.
explore different ways of making use of labeled
and unlabeled in-domain data.
Bacchiani et al. (2006) applies self-training to
parser adaptation to utilize unlabeled in-domain
data. The authors find that it helps quite a bit when
adapting from BROWN to WSJ. They use a parser
trained from the BROWN train set to parse WSJ and
add the parsed WSJ sentences to their training set.
We perform a similar experiment, using our WSJ-
trained reranking parser to parse BROWN train and
testing on BROWN development. We achieved a
boost from 84.8% to 85.6% when we added the
parsed BROWN sentences to our training. Adding
in 1,000k sentences from NANC as well, we saw a
further increase to 86.3%. However, the technique
does not seem as effective in our case. While the
self-trained BROWN data helps the parser, it ad-
versely affects the performance of the reranking
parser. When self-trained BROWN data is added to
WSJ training, the reranking parser’s performance
drops from 86.6% to 86.1%. We see a similar
degradation as NANC data is added to the train-
ing set as well. We are not yet able to explain this
unusual behavior.
We now turn to the scenario where we have
some labeled in-domain data. The most obvious
way to incorporate labeled in-domain data is to
combine it with the labeled out-of-domain data.
We have already seen the results (Gildea, 2001)
and (Bacchiani et al., 2006) achieve in Table 1.
We explore various combinations of BROWN,
WSJ, and NANC corpora. Because we are
mainly interested in exploring techniques with
self-trained models rather than optimizing perfor-
mance, we only consider weighting each corpus
with a relative weight of one for this paper. The
models generated are tuned on section 24 from
WSJ. The results are summarized in Table 3.
While both WSJ and BROWN models bene-
fit from a small amount of NANC data, adding
more than 250k NANC sentences to the BROWN
or combined models causes their performance to
drop. This is not surprising, though, since adding
“too much” NANC overwhelms the more accurate
BROWN or WSJ counts. By weighting the counts
from each corpus appropriately, this problem can
be avoided.
Another way to incorporate labeled data is to
tune the parser back-off parameters on it. Bac-
chiani et al. (2006) report that tuning on held-out
BROWN data gives a large improvement over tun-
340
ing on WSJ data. The improvement is mostly (but
not entirely) in precision. We do not see the same
improvement (Figure 1) but this is likely due to
differences in the parsers. However, we do see
a similar improvement for parsing accuracy once
NANC data has been added. The reranking parser
generally sees an improvement, but it does not ap-
pear to be significant.
4.3 Reranker Portability
We have shown that the WSJ-trained reranker is
actually quite portable to the BROWN fiction do-
main. This is surprising given the large number
of features (over a million in the case of the WSJ
reranker) tuned to adjust for errors made in the 50-
best lists by the first-stage parser. It would seem
the corrections memorized by the reranker are not
as domain-specific as we might expect.
As further evidence, we present the results of
applying the WSJ model to the Switchboard cor-
pus — a domain much less similar to WSJ than
BROWN. In Table 4, we see that while the parser’s
performance is low, self-training and reranking
provide orthogonal benefits. The improvements
represent a 12% error reduction with no additional
in-domain data. Naturally, in-domain data and
speech-specific handling (e.g. disfluency model-
ing) would probably help dramatically as well.
Finally, to compare against a model fully
trained on BROWN data, we created a BROWN
reranker. We parsed the BROWN training set with
20-fold cross-validation, selected features that oc-
curred 5 times or more in the training set, and
fed the 50-best lists from the parser to a numeri-
cal optimizer to estimate feature weights. The re-
sulting reranker model had approximately 700,000
features, which is about half as many as the WSJ
trained reranker. This may be due to the smaller
size of the BROWN training set or because the
feature schemas for the reranker were developed
on WSJ data. As seen in Table 5, the BROWN
reranker is not a significant improvement over the
WSJ reranker for parsing BROWN data.
5 Analysis
We perform several types of analysis to measure
some of the differences and similarities between
the BROWN-trained and WSJ-trained reranking
parsers. While the two parsers agree on a large
number of parse brackets (Section 5.2), there are
categorical differences between them (as seen in
Parser model Parser f-score Reranker f-score
WSJ 74.0 75.9
WSJ+NANC 75.6 77.0
Table 4: Parser and reranking parser performance
on the SWITCHBOARD development corpus. In
this case, WSJ+NANC is a model created from WSJ
and 1,750k sentences from NANC.
Model 1-best 10-best 25-best 50-best
WSJ 82.6 88.9 90.7 91.9
WSJ+NANC 86.4 92.1 93.5 94.3
BROWN 86.3 92.0 93.3 94.2
Table 6: Oracle f-scores of top n parses pro-
duced by baseline WSJ parser, a combined WSJ and
NANC parser, and a baseline BROWN parser.
Section 5.3).
5.1 Oracle Scores
Table 6 shows the f-scores of an “oracle reranker”
— i.e. one which would always choose the parse
with the highest f-score in the n-best list. While
the WSJ parser has relatively low f-scores, adding
NANC data results in a parser with comparable ora-
cle scores as the parser trained from BROWN train-
ing. Thus, the WSJ+NANC model has better oracle
rates than the WSJ model (McClosky et al., 2006)
for both the WSJ and BROWN domains.
5.2 Parser Agreement
In this section, we compare the output of the
WSJ+NANC-trained and BROWN-trained rerank-
ing parsers. We use evalb to calculate how sim-
ilar the two sets of output are on a bracket level.
Table 7 shows various statistics. The two parsers
achieved an 88.0% f-score between them. Ad-
ditionally, the two parsers agreed on all brackets
almost half the time. The part of speech tagging
agreement is fairly high as well. Considering they
were created from different corpora, this seems
like a high level of agreement.
5.3 Statistical Analysis
We conducted randomization tests for the signifi-
cance of the difference in corpus f-score, based on
the randomization version of the paired sample t-
test described by Cohen (1995). The null hypoth-
esis is that the two parsers being compared are in
fact behaving identically, so permuting or swap-
ping the parse trees produced by the parsers for
341
WSJ tuned parser
BROWN tuned parser
WSJ tuned reranking parser
BROWN tuned reranking parser
NANC sentences added
f-
sco
re
2000k1750k1500k1250k1000k750k500k250k0k
87.8
87.0
86.0
85.0
83.8
Figure 1: Precision and recall f-scores when testing on BROWN development as a function of the number
of NANC sentences added under four test conditions. “BROWN tuned” indicates that BROWN training data
was used to tune the parameters (since the normal held-out section was being used for testing). For “WSJ
tuned,” we tuned the parameters from section 24 of WSJ. Tuning on BROWN helps the parser, but not for
the reranking parser.
Parser model Parser alone Reranking parser
WSJ alone 83.9 85.8
WSJ+2,500k NANC 86.4 87.7
BROWN alone 86.3 87.4
BROWN+50k NANC 86.8 88.0
BROWN+250k NANC 86.8 88.1
BROWN+500k NANC 86.7 87.8
WSJ+BROWN 86.5 88.1
WSJ+BROWN+50k NANC 86.8 88.1
WSJ+BROWN+250k NANC 86.8 88.1
WSJ+BROWN+500k NANC 86.6 87.7
Table 3: f-scores from various combinations of WSJ, NANC, and BROWN corpora on BROWN develop-
ment. The reranking parser used the WSJ-trained reranker model. The BROWN parsing model is naturally
better than the WSJ model for this task, but combining the two training corpora results in a better model
(as in Gildea (2001)). Adding small amounts of NANC further improves the models.
Parser model Parser alone WSJ-reranker BROWN-reranker
WSJ 82.9 85.2 85.2
WSJ+NANC 87.1 87.8 87.9
BROWN 86.7 88.2 88.4
Table 5: Performance of various combinations of parser and reranker models when evaluated on BROWN
test. The WSJ+NANC parser with the WSJ reranker comes close to the BROWN-trained reranking parser.
The BROWN reranker provides only a small improvement over its WSJ counterpart, which is not statisti-
cally significant.
342
Bracketing agreement f-score 88.03%
Complete match 44.92%
Average crossing brackets 0.94
POS Tagging agreement 94.85%
Table 7: Agreement between the WSJ+NANC
parser with the WSJ reranker and the BROWN
parser with the BROWN reranker. Complete match
is how often the two reranking parsers returned the
exact same parse.
the same test sentence should not affect the cor-
pus f-scores. By estimating the proportion of per-
mutations that result in an absolute difference in
corpus f-scores at least as great as that observed
in the actual output, we obtain a distribution-
free estimate of significance that is robust against
parser and evaluator failures. The results of this
test are shown in Table 8. The table shows that
the BROWN reranker is not significantly different
from the WSJ reranker.
In order to better understand the difference be-
tween the reranking parser trained on Brown and
the WSJ+NANC/WSJ reranking parser (a reranking
parser with the first-stage trained on WSJ+NANC
and the second-stage trained on WSJ) on Brown
data, we constructed a logistic regression model
of the difference between the two parsers’ f-
scores on the development data using the R sta-
tistical package5. Of the 2,078 sentences in the
development data, 29 sentences were discarded
because evalb failed to evaluate at least one of
the parses.6 A Wilcoxon signed rank test on the
remaining 2,049 paired sentence level f-scores
was significant at p = 0.0003. Of these 2,049
sentences, there were 983 parse pairs with the
same sentence-level f-score. Of the 1,066 sen-
tences for which the parsers produced parses with
different f-scores, there were 580 sentences for
which the BROWN/BROWN parser produced a
parse with a higher sentence-level f-score and 486
sentences for which the WSJ+NANC/WSJ parser
produce a parse with a higher f-score. We
constructed a generalized linear model with a
binomial link with BROWN/BROWN f-score >
WSJ+NANC/WSJ f-score as the predicted variable,
and sentence length, the number of prepositions
(IN), the number of conjunctions (CC) and Brown
5http://www.r-project.org
6This occurs when an apostrophe is analyzed as a posses-
sive marker in the gold tree and a punctuation symbol in the
parse tree, or vice versa.
Feature Estimate z-value Pr(> |z|)
(Intercept) 0.054 0.3 0.77
IN -0.134 -4.4 8.4e-06 ***
ID=G 0.584 2.5 0.011 *
ID=K 0.697 2.9 0.003 **
ID=L 0.552 2.3 0.021 *
ID=M 0.376 0.9 0.33
ID=N 0.642 2.7 0.0055 **
ID=P 0.624 2.7 0.0069 **
ID=R 0.040 0.1 0.90
Table 9: The logistic model of BROWN/BROWN
f-score > WSJ+NANC/WSJ f-score identified by
model selection. The feature IN is the num-
ber prepositions in the sentence, while ID identi-
fies the Brown subcorpus that the sentence comes
from. Stars indicate significance level.
subcorpus ID as explanatory variables. Model
selection (using the “step” procedure) discarded
all but the IN and Brown ID explanatory vari-
ables. The final estimated model is shown in Ta-
ble 9. It shows that the WSJ+NANC/WSJ parser
becomes more likely to have a higher f-score
than the BROWN/BROWN parser as the number
of prepositions in the sentence increases, and that
the BROWN/BROWN parser is more likely to have
a higher f-score on Brown sections K, N, P, G
and L (these are the general fiction, adventure and
western fiction, romance and love story, letters and
memories, and mystery sections of the Brown cor-
pus, respectively). The three sections of BROWN
not in this list are F, M, and R (popular lore, sci-
ence fiction, and humor).
6 Conclusions and Future Work
We have demonstrated that rerankers and self-
trained models can work well across domains.
Models self-trained on WSJ appear to be better
parsing models in general, the benefits of which
are not limited to the WSJ domain. The WSJ-
trained reranker using out-of-domain LA Times
parses (produced by the WSJ-trained reranker)
achieves a labeled precision-recall f-measure of
87.8% on Brown data, nearly equal to the per-
formance one achieves by using a purely Brown
trained parser-reranker. The 87.8% f-score on
Brown represents a 24% error reduction on the
corpus.
Of course, as corpora differences go, Brown is
relatively close to WSJ. While we also find that our
343
WSJ+NANC/WSJ BROWN/WSJ BROWN/BROWN
WSJ/WSJ 0.025 (0) 0.030 (0) 0.031 (0)
WSJ+NANC/WSJ 0.004 (0.1) 0.006 (0.025)
BROWN/WSJ 0.002 (0.27)
Table 8: The difference in corpus f-score between the various reranking parsers, and the significance of
the difference in parentheses as estimated by a randomization test with 106 samples. “x/y” indicates that
the first-stage parser was trained on data set x and the second-stage reranker was trained on data set y.
“best” WSJ-parser-reranker improves performance
on the Switchboard corpus, it starts from a much
lower base (74.0%), and achieves a much less sig-
nificant improvement (3% absolute, 11% error re-
duction). Bridging these larger gaps is still for the
future.
One intriguing idea is what we call “self-trained
bridging-corpora.” We have not yet experimented
with medical text but we expect that the “best”
WSJ+NANC parser will not perform very well.
However, suppose one does self-training on a bi-
ology textbook instead of the LA Times. One
might hope that such a text will split the differ-
ence between more “normal” newspaper articles
and the specialized medical text. Thus, a self-
trained parser based upon such text might do much
better than our standard “best.” This is, of course,
highly speculative.
Acknowledgments
This work was supported by NSF grants LIS9720368, and
IIS0095940, and DARPA GALE contract HR0011-06-2-
0001. We would like to thank the BLLIP team for their com-
ments.
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