Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 865–872,
Sydney, July 2006. c©2006 Association for Computational Linguistics
An All-Subtrees Approach to Unsupervised Parsing
Rens Bod
School of Computer Science
University of St Andrews
North Haugh, St Andrews
 KY16 9SX Scotland, UK
rb@dcs.st-and.ac.uk
Abstract
We investigate generalizations of the all-
subtrees "DOP" approach to unsupervised
parsing. Unsupervised DOP models assign
all possible binary trees to a set of sentences
and next use (a large random subset of) all
subtrees from these binary trees to compute
the most probable parse trees. We will test
both a relative frequency estimator for
unsupervised DOP and a maximum
likelihood estimator which is known to be
statistically consistent. We report state-of-
the-art results on English (WSJ), German
(NEGRA) and Chinese (CTB) data. To the
best of our knowledge this is the first paper
which tests a maximum likelihood estimator
for DOP on the Wall Street Journal, leading
to the surprising result that an unsupervised
parsing model beats a widely used
supervised model (a treebank PCFG).
1   Introduction
The problem of bootstrapping syntactic structure
from unlabeled data has regained considerable
interest. While supervised parsers suffer from
shortage of hand-annotated data, unsupervised
parsers operate with unlabeled raw data of which
unlimited quantities are available. During the last
few years there has been steady progress in the field.
Where van Zaanen (2000) achieved 39.2%
unlabeled f-score on ATIS word strings, Clark
(2001) reports 42.0% on the same data, and Klein
and Manning (2002) obtain 51.2% f-score on ATIS
part-of-speech strings using a constituent-context
model called CCM. On Penn Wall Street Journal p-
o-s-strings ≤ 10 (WSJ10), Klein and Manning
(2002) report 71.1% unlabeled f-score with CCM.
And the hybrid approach of Klein and Manning
(2004), which combines constituency and
dependency models, yields 77.6% f-score.
Bod (2006) shows that a further improve-
ment on the WSJ10 can be achieved by an unsuper-
vised generalization of the all-subtrees approach
known as Data-Oriented Parsing (DOP). This
unsupervised DOP model, coined U-DOP, first
assigns all possible unlabeled binary trees to a set of
sentences and next uses all subtrees from (a large
subset of) these trees to compute the most probable
parse trees. Bod (2006) reports that U-DOP not
only outperforms previous unsupervised parsers but
that its performance is as good as a binarized super-
vised parser (i.e. a treebank PCFG) on the WSJ.
A possible drawback of U-DOP, however,
is the statistical inconsistency of its estimator
(Johnson 2002) which is inherited from the DOP1
model (Bod 1998). That is, even with unlimited
training data, U-DOP's estimator is not guaranteed
to converge to the correct weight distribution.
Johnson (2002: 76) argues in favor of a maximum
likelihood estimator for DOP which is statistically
consistent. As it happens, in Bod (2000) we already
developed such a DOP model, termed ML-DOP,
which reestimates the subtree probabilities by a
maximum likelihood procedure based on
Expectation-Maximization. Although cross-
validation is needed to avoid overlearning, ML-DOP
outperforms DOP1 on the OVIS corpus (Bod
2000).
This raises the question whether we can
create an unsupervised DOP model which is also
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statistically consistent. In this paper we will show
that an unsupervised version of ML-DOP can be
constructed along the lines of U-DOP. We will start
out by summarizing DOP, U-DOP and ML-DOP,
and next create a new unsupervised model called
UML-DOP. We report that UML-DOP not only
obtains higher parse accuracy than U-DOP on three
different domains, but that it also achieves this with
fewer subtrees than U-DOP. To the best of our
knowledge, this paper presents the first
unsupervised parser that outperforms a widely used
supervised parser on the WSJ, i.e. a treebank
PCFG. We will raise the question whether the end
of supervised parsing is in sight.
2  DOP
The key idea of DOP is this: given an annotated
corpus, use all subtrees, regardless of size, to parse
new sentences. The DOP1 model in Bod (1998)
computes the probabilities of parse trees and
sentences from the relative frequencies of the
subtrees. Although it is now known that DOP1's
relative frequency estimator is statistically
inconsistent (Johnson 2002), the model yields
excellent empirical results and has been used in
state-of-the-art systems. Let's illustrate DOP1 with a
simple example. Assume a corpus consisting of
only two trees, as given in figure 1.
NP VP
S
NP
Mary
V
likes
John
NP VP
S
NPVPeter
hates Susan
Figure 1. A corpus of two trees
New sentences may be derived by combining
fragments, i.e. subtrees, from this corpus, by means
of a node-substitution operation indicated as °.
Node-substitution identifies the leftmost
nonterminal frontier node of one subtree with the
root node of a second subtree (i.e., the second
subtree is substituted on the leftmost nonterminal
frontier node of the first subtree). Thus a new
sentence such as Mary likes Susan can be derived by
combining subtrees from this corpus, shown in
figure 2.
NP VP
S
NPV
likes
NP
Mary
NP
Susan NP VP
S
NPMary V
likes Susan
=° °
Figure 2. A derivation for Mary likes Susan
Other derivations may yield the same tree, e.g.:
NP VP
S
NPV
NP
Mary NP VP
S
NPMary V
likes Susan
=
Susan
V
likes
° °
Figure 3. Another derivation yielding same tree
DOP1 computes the probability of a subtree t as the
probability of selecting t among all corpus subtrees
that can be substituted on the same node as t. This
probability is computed as the number of
occurrences of t in the corpus, | t |, divided by the
total number of occurrences of all subtrees t' with
the same root label as t.1 Let r(t) return the root label
of t. Then we may write:
P(t)  =   | t |Σ
 t': r(t')= r(t)  | t' |
The probability of a derivation t1°...°tn is computed
by the product of the probabilities of its subtrees ti:
P(t1°...°tn)  =  Πi P(ti)
As we have seen, there may be several distinct
derivations that generate the same parse tree. The
probability of a parse tree T is the sum of the
1 This subtree probability is redressed by a simple
correction factor discussed in Goodman (2003: 136)
and Bod (2003).
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probabilities of its distinct derivations. Let tid be the
i-th subtree in the derivation d that produces tree T,
then the probability of T is given by
P(T)  =  ΣdΠi P(tid)
Thus DOP1 considers counts of subtrees of a wide
range of sizes: everything from counts of single-
level rules to entire trees is taken into account to
compute the most probable parse tree of a sentence.
A disadvantage of the approach may be that an
extremely large number of subtrees (and
derivations) must be considered. Fortunately there
exists a compact isomorphic PCFG-reduction of
DOP1 whose size is linear rather than exponential in
the size of the training set (Goodman 2003).
Moreover, Collins and Duffy (2002) show how a
tree kernel can be applied to DOP1's all-subtrees
representation. The currently most successful
version of DOP1 uses a PCFG-reduction of the
model with an n-best parsing algorithm (Bod 2003).
3  U-DOP
U-DOP extends DOP1 to unsupervised parsing
(Bod 2006). Its key idea is to assign all unlabeled
binary trees to a set of sentences and to next use (in
principle) all subtrees from these binary trees to
parse new sentences. U-DOP thus proposes one of
the richest possible models in bootstrapping trees.
Previous models like Klein and Manning's (2002,
2005) CCM model limit the dependencies to
"contiguous subsequences of a sentence". This
means that CCM neglects dependencies that are
non-contiguous such as between more and than in
"BA carried more people than cargo". Instead, U-
DOP's all-subtrees  approach captures both
contiguous and non-contiguous lexical dependen-
cies.
As with most other unsupervised parsing
models, U-DOP induces trees for p-o-s strings
rather than for word strings. The extension to word
strings is straightforward as there exist highly
accurate unsupervised part-of-speech taggers (e.g.
Schütze 1995) which can be directly combined with
unsupervised parsers.
To give an illustration of U-DOP, consider
the WSJ p-o-s string NNS VBD JJ NNS which
may correspond for instance to the sentence
Investors suffered heavy losses. U-DOP starts by
assigning all possible binary trees to this string,
where each root node is labeled S and each internal
node is labeled X. Thus NNS VBD JJ NNS has a
total of five binary trees shown in figure 4 -- where
for readability we add words as well.
NNS VBD JJ NNS
Investors suffered heavy losses
X
X
S
  
NNS VBD JJ NNS
Investors suffered heavy losses
X
X
S
NNS VBD JJ NNS
Investors suffered heavy losses
X
X
S
  
NNS VBD JJ NNS
Investors suffered heavy losses
X
X
S
NNS VBD JJ NNS
Investors suffered heavy losses
XX
S
Figure 4. All binary trees for NNS VBD JJ NNS
(Investors suffered heavy losses)
While we can efficiently represent the set of all
binary trees of a string by means of a chart, we need
to unpack the chart if we want to extract subtrees
from this set of binary trees. And since the total
number of binary trees for the small WSJ10 is
almost 12 million, it is doubtful whether we can
apply the unrestricted U-DOP model to such a
corpus. U-DOP therefore randomly samples a large
subset from the total number of parse trees from the
chart (see Bod 2006) and next converts the subtrees
from these parse trees into a PCFG-reduction
(Goodman 2003). Since the computation of the
most probable parse tree is NP-complete (Sima'an
1996), U-DOP estimates the most probable tree
from the 100 most probable derivations using
Viterbi n-best parsing. We could also have used the
more efficient k-best hypergraph parsing technique
by Huang and Chiang (2005), but we have not yet
incorporated this into our implementation.
To give an example of the dependencies that
U-DOP can take into account, consider the
following subtrees in figure 5 from the trees in
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figure 4 (where we again add words for readability).
These subtrees show that U-DOP takes into account
both contiguous and non-contiguous substrings.
NNS VBD
Investors suffered
X
X
S
VBD
suffered
X
X
NNS NNS
Investors losses
X
X
S
JJ NNS
heavy losses
XX
S
JJ NNS
heavy losses
X
NNS VBD
Investors suffered
X
VBD JJ
suffered heavy
X
Figure 5. Some subtrees from trees in figure 4
Of course, if we only had the sentence Investors
suffered heavy losses in our corpus, there would be
no difference in probability between the five parse
trees in figure 4. However, if we also have a
different sentence where JJ NNS ( heavy losses)
appears in a different context, e.g. in Heavy losses
were reported, its covering subtree gets a relatively
higher frequency and the parse tree where heavy
losses occurs as a constituent gets a higher total
probability.
4  ML-DOP
ML-DOP (Bod 2000) extends DOP with a
maximum likelihood reestimation technique based
on the expectation-maximization (EM) algorithm
(Dempster et al. 1977) which is known to be
statistically consistent (Shao 1999). ML-DOP
reestimates DOP's subtree probabilities in an
iterative way until the changes become negligible.
The following exposition of ML-DOP is heavily
based on previous work by Bod (2000) and
Magerman (1993).
It is important to realize that there is an
implicit assumption in DOP that all possible
derivations of a parse tree contribute equally to the
total probability of the parse tree. This is equivalent
to saying that there is a hidden component to the
model, and that DOP can be trained using an EM
algorithm to determine the maximum likelihood
estimate for the training data. The EM algorithm for
this ML-DOP model is related to the Inside-Outside
algorithm for context-free grammars, but the
reestimation formula is complicated by the presence
of subtrees of depth greater than 1. To derive the
reestimation formula, it is useful to consider the
state space of all possible derivations of a tree.
The derivations of a parse tree T can be
viewed as a state trellis, where each state contains a
partially constructed tree in the course of a leftmost
derivation of T. st denotes a state containing the tree
t which is a subtree of T. The state trellis is defined
as follows.
The initial state, s0, is a tree with depth zero,
consisting of simply a root node labeled with S. The
final state, sT, is the given parse tree T.
A state st is connected forward to all states
stf such that tf = t ° t', for some t' . Here the
appropriate t' is defined to be tf − t.
A state st is connected backward to all states
stb such that t = tb ° t', for some t' . Again, t' is
defined to be t − tb.
The construction of the state lattice and
assignment of transition probabilities according to
the ML-DOP model is called the forward pass. The
probability of a given state, P(s), is referred to as
α(s). The forward probability of a state st is
computed recursively
α(st) = Σ α(st  ) P(t − tb).b
stb
The backward probability of a state, referred to as
β(s), is calculated according to the following
recursive formula:
β(st) = Σ β(st  ) P(tf − t)f
fst
where the backward probability of the goal state is
set equal to the forward probability of the goal state,
β(sT) = α(sT).
The update formula for the count of a
subtree t is (where r(t) is the root label of t):
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ct(t) =      Σ
      β(st  )α(st  )P(t | r(t))
f b
α(sgoal)st  :∃st ,tb°t=tf
b f
The updated probability distribution, P'(t | r(t)), is
defined to be
P'(t | r(t)) =  ct(t)ct(r(t))
where ct(r(t)) is defined as
ct(r(t)) = Σ ct(t')
t': r(t')=r(t)
In practice, ML-DOP starts out by assigning the
same relative frequencies to the subtrees as DOP1,
which are next reestimated by the formulas above.
We may in principle start out with any initial
parameters, including random initializations, but
since ML estimation is known to be very sensitive
to the initialization of the parameters, it is convenient
to start with parameters that are known to perform
well.
To avoid overtraining, ML-DOP uses the
subtrees from one half of the training set to be
trained on the other half, and vice versa. This cross-
training is important since otherwise UML-DOP
would assign the training set trees their empirical
frequencies and assign zero weight to all other
subtrees (cf. Prescher et al. 2004). The updated
probabilities are iteratively reestimated until the
decrease in cross-entropy becomes negligible.
Unfortunately, no compact PCFG-reduction of ML-
DOP is known. As a consequence, parsing with
ML-DOP is very costly and the model has hitherto
never been tested on corpora larger than OVIS
(Bonnema et al. 1997). Yet, we will show that by
clever pruning we can extend our experiments not
only to the WSJ, but also to the German NEGRA
and the Chinese CTB. (Zollmann and Sima'an 2005
propose a different consistent estimator for DOP,
which we cannot go into here).
5  UML-DOP
Analogous to U-DOP, UML-DOP is an
unsupervised generalization of ML-DOP: it first
assigns all unlabeled binary trees to a set of
sentences and next extracts a large (random) set of
subtrees from this tree set. It then reestimates the
initial probabilities of these subtrees by ML-DOP on
the sentences from a held-out part of the tree set.
The training is carried out by dividing the tree set
into two equal parts, and reestimating the subtrees
from one part on the other. As initial probabilities
we use the subtrees' relative frequencies as described
in section 2 (smoothed by Good-Turing -- see Bod
1998), though it would also be interesting to see
how the model works with other initial parameters,
in particular with the usage frequencies proposed by
Zuidema (2006).
As with U-DOP, the total number of
subtrees that can be extracted from the binary tree
set is too large to be fully taken into account.
Together with the high computational cost of
reestimation we propose even more drastic pruning
than we did in Bod (2006) for U-DOP. That is,
while for sentences ≤ 7 words we use all binary
trees, for each sentence ≥ 8 words we randomly
sample a fixed number of 128 trees (which
effectively favors more frequent trees). The resulting
set of trees is referred to as the binary tree set. Next,
we randomly extract for each subtree-depth a fixed
number of subtrees, where the depth of subtree is
the longest path from root to any leaf. This has
roughly the same effect as the correction factor used
in Bod (2003, 2006). That is, for each particular
depth we sample subtrees by first randomly
selecting a node in a random tree from the binary
tree set after which we select random expansions
from that node until a subtree of the particular depth
is obtained. For our experiments in section 6, we
repeated this procedure 200,000 times for each
depth. The resulting subtrees are then given to ML-
DOP's reestimation procedure. Finally, the
reestimated subtrees are used to compute the most
probable parse trees for all sentences using Viterbi
n-best, as described in section 3, where the most
probable parse is estimated from the 100 most
probable derivations.
A potential criticism of (U)ML-DOP is that
since we use DOP1's relative frequencies as initial
parameters, ML-DOP may only find a local
maximum nearest to DOP1's estimator. But this is
of course a criticism against any iterative ML
approach: it is not guaranteed that the global
maximum is found (cf. Manning and Schütze 1999:
401). Nevertheless we will see that our reestimation
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procedure leads to significantly better accuracy
compared to U-DOP (the latter would be equal to
UML-DOP under 0 iterations). Moreover, in
contrast to U-DOP, UML-DOP can be theoretically
motivated: it maximizes the likelihood of the data
using the statistically consistent EM algorithm.
6  Experiments: Can we beat supervised by
unsupervised parsing?
To compare UML-DOP to U-DOP, we started out
with the WSJ10 corpus, which contains 7422
sentences ≤ 10 words after removing empty
elements and punctuation. We used the same
evaluation metrics for unlabeled precision (UP) and
unlabeled recall (UR) as defined in Klein (2005: 21-
22). Klein's definitions differ slightly from the
standard PARSEVAL metrics: multiplicity of
brackets is ignored, brackets of span one are ignored
and the bracket labels are ignored. The two metrics
of UP and UR are combined by the unlabeled f -
score F1 which is defined as the harmonic mean of
UP and UR: F1 = 2*UP*UR/(UP+UR).
For the WSJ10, we obtained a binary tree
set of 5.68 * 105 trees, by extracting the binary trees
as described in section 5. From this binary tree set
we sampled 200,000 subtrees for each subtree-
depth. This resulted in a total set of roughly 1.7 *
106 subtrees that were reestimated by our
maximum-likelihood procedure. The decrease in
cross-entropy became negligible after 14 iterations
(for both halfs of WSJ10). After computing the
most probable parse trees, UML-DOP achieved an
f-score of 82.9% which is a 20.5% error reduction
compared to U-DOP's f-score of 78.5% on the
same data (Bod 2006).
We next tested UML-DOP on two
additional domains which were also used in Klein
and Manning (2004) and Bod (2006): the German
NEGRA10 (Skut et al. 1997) and the Chinese
CTB10 (Xue et al. 2002) both containing 2200+
sentences ≤ 10 words after removing punctuation.
Table 1 shows the results of UML-DOP compared
to U-DOP, the CCM model by Klein and Manning
(2002), the DMV dependency learning model by
Klein and Manning (2004) as well as their
combined model DMV+CCM.
Table 1 shows that UML-DOP scores better
than U-DOP and Klein and Manning's models in all
cases. It thus pays off to not only use subtrees rather
than substrings (as in CCM) but to also reestimate
the subtrees' probabilities by a maximum-likelihood
procedure rather than using their (smoothed) relative
frequencies (as in U-DOP). Note that UML-DOP
achieves these improved results with fewer subtrees
than U-DOP, due to UML-DOP's more drastic
pruning of subtrees. It is also noteworthy that UML-
DOP, like U-DOP, does not employ a separate class
for non-constituents, so-called distituents, while
CCM and CCM+DMV do. (Interestingly, the top
10 most frequently learned constituents by UML-
DOP were exactly the same as by U-DOP -- see the
relevant table in Bod 2006).
Model English German Chinese(WSJ10) (NEGRA10) (CTB10)
CCM 71.9 61.6 45.0
DMV 52.1 49.5 46.7
DMV+CCM 77.6 63.9 43.3
U-DOP 78.5 65.4 46.6
UML-DOP 82.9 67.0 47.2
Table 1. F-scores of UML-DOP compared to
previous models on the same data
We were also interested in testing UML-DOP on
longer sentences. We therefore included all WSJ
sentences up to 40 words after removing empty
elements and punctuation (WSJ40) and again
sampled 200,000 subtrees for each depth, using the
same method as before. Furthermore, we compared
UML-DOP against a supervised binarized PCFG,
i.e. a treebank PCFG whose simple relative
frequency estimator corresponds to maximum
likelihood (Chi and Geman 1998), and which we
shall refer to as "ML-PCFG". To this end, we used
a random 90%/10% division of WSJ40 into a
training set and a test set. The ML-PCFG had thus
access to the Penn WSJ trees in the training set,
while UML-DOP had to bootstrap all structure from
the flat strings from the training set to next parse the
10% test set -- clearly a much more challenging
task. Table 2 gives the results in terms of f-scores.
The table shows that UML-DOP scores
better than U-DOP, also for WSJ40. Our results on
WSJ10 are somewhat lower than in table 1 due to
the use of a smaller training set of 90% of the data.
But the most surprising result is that UML-DOP's f-
870
score is higher than the supervised binarized tree-
bank PCFG (ML-PCFG) for both WSJ10 and
WSJ40. In order to check whether this difference is
statistically significant, we additionally tested on 10
different 90/10 divisions of the WSJ40 (which were
the same splits as in Bod 2006). For these splits,
UML-DOP achieved an average f-score of 66.9%,
while ML-PCFG obtained an average f-score of
64.7%. The difference in accuracy between UML-
DOP and ML-PCFG was statistically significant
according to paired t-testing (p≤0.05). To the best of
our knowledge this means that we have shown for
the first time that an unsupervised parsing model
(UML-DOP) outperforms a widely used supervised
parsing model (a treebank PCFG) on the WSJ40.
Model       WSJ10  WSJ40
U-DOP 78.1 63.9
UML-DOP 82.5 66.4
ML-PCFG 81.5 64.6
Table 2. F-scores of U-DOP, UML-DOP and a
supervised treebank PCFG (ML-PCFG) for a
random 90/10 split of WSJ10 and WSJ40.
We should keep in mind that (1) a treebank PCFG
is not state-of-the-art: its performance is mediocre
compared to e.g. Bod (2003) or McClosky et al.
(2006), and (2) that our treebank PCFG is binarized
as in Klein and Manning (2005) to make results
comparable. To be sure, the unbinarized version of
the treebank PCFG obtains 89.0% average f-score
on WSJ10 and 72.3% average f-score on WSJ40.
Remember that the Penn Treebank annotations are
often exceedingly flat, and many branches have arity
larger than two. It would be interesting to see how
UML-DOP performs if we also accept ternary (and
wider) branches -- though the total number of
possible trees that can be assigned to strings would
then further explode.
UML-DOP's performance still remains
behind that of supervised (binarized) DOP parsers,
such as DOP1, which achieved 81.9% average f-
score on the 10 WSJ40 splits, and ML-DOP, which
performed slightly better with 82.1% average f-
score. And if DOP1 and ML-DOP are not
binarized, their average f-scores are respectively
90.1% and 90.5% on WSJ40. However, DOP1 and
ML-DOP heavily depend on annotated data whereas
UML-DOP only needs unannotated data. It would
thus be interesting to see how close UML-DOP can
get to ML-DOP's performance if we enlarge the
amount of training data.
7  Conclusion: Is the end of supervised
parsing in sight?
Now that we have outperformed a well-known
supervised parser by an unsupervised one, we may
raise the question as to whether the end of
supervised NLP comes in sight. All supervised
parsers are reaching an asymptote and further
improvement does not seem to come from more
hand-annotated data but by adding unsupervised or
semi-unsupervised techniques (cf. McClosky et al.
2006). Thus if we modify our question as: does the
exclusively supervised approach to parsing come to
an end, we believe that the answer is certainly yes.
Yet we should neither rule out the
possibility that entirely unsupervised methods will
in fact surpass semi-supervised methods. The main
problem is how to quantitatively compare these
different parsers, as any evaluation on hand-
annotated data (like the Penn treebank) will
unreasonably favor semi-supervised parsers. There
is thus is a quest for designing an annotation-
independent evaluation scheme. Since parsers are
becoming increasingly important in applications like
syntax-based machine translation and structural
language models for speech recognition, one way to
go would be to compare these different parsing
methods by isolating their contribution in improving
a concrete NLP system, rather than by testing them
against gold standard annotations which are
inherently theory-dependent.
The initially disappointing results of
inducing trees entirely from raw text was not so
much due to the difficulty of the bootstrapping
problem per se, but to (1) the poverty of the initial
models and (2) the difficulty of finding theory-
independent evaluation criteria. The time has come
to fully reappraise unsupervised parsing models
which should be trained on massive amounts of
data, and be evaluated in a concrete application.
There is a final question as to how far the
DOP approach to unsupervised parsing can be
stretched. In principle we can assign all possible
syntactic categories, semantic roles, argument
871
structures etc. to a set of given sentences and let the
statistics decide which assignments are most useful
in parsing new sentences. Whether such a massively
maximalist approach is feasible can only be
answered by empirical investigation in due time.
Acknowledgements
Thanks to Willem Zuidema, David Tugwell and
especially to three anonymous reviewers whose
unanymous suggestions on DOP and EM
considerably improved the original paper. A
substantial part of this research was carried out in
the context of the NWO Exact project
"Unsupervised Stochastic Grammar Induction from
Unlabeled Data", project number 612.066.405.
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