What is the Minimal Set of Fragments that Achieves
Maximal Parse Accuracy?
Rens Bod
School of Computing
University of Leeds, Leeds LS2 9JT, &
Institute for Logic, Language and Computation
University of Amsterdam, Spuistraat 134, 1012 VB Amsterdam
rens@comp.leeds.ac.uk
Abstract
We aim at finding the minimal set of
fragments which achieves maximal parse
accuracy in Data Oriented Parsing. Expe-
riments with the Penn Wall Street
Journal treebank show that counts of
almost arbitrary fragments within parse
trees are important, leading to improved
parse accuracy over previous models
tested on this treebank (a precis -
ion of 90.8% and a recall of 90.6%). We
isolate some dependency relations which
previous models neglect but which
contribute to higher parse accuracy.
1 Introduction
One of the goals in statistical natural language
parsing is to find the minimal set of statistical
dependencies (between words and syntactic
structures) that achieves maximal parse accuracy.
Many stochastic parsing models use linguistic
intuitions to find this minimal set, for example by
restricting the statistical dependencies to the
locality of headwords of constituents (Collins
1997, 1999; Eisner 1997), leaving it as an open
question whether there exist important statistical
dependencies that go beyond linguistically
motivated dependencies. The Data Oriented
Parsing (DOP) model, on the other hand, takes a
rather extreme view on this issue: given an
annotated corpus, all fragments (i.e. subtrees)
seen in that corpus, regardless of size and
lexicalization, are in principle taken to form a
grammar (see Bod 1993, 1998; Goodman 1998;
Sima'an 1999). The set of subtrees that is used is
thus very large and extremely redundant. Both
from a theoretical and from a computational
perspective we may wonder whether it is
possible to impose constraints on the subtrees
that are used, in such a way that the accuracy of
the model does not deteriorate or perhaps even
improves. That is the main question addressed in
this paper. We report on experiments carried out
with the Penn Wall Street Journal (WSJ)
treebank to investigate several strategies for
constraining the set of subtrees. We found that
the only constraints that do not decrease the parse
accuracy consist in an upper bound of the
number of words in the subtree frontiers and an
upper bound on the depth of unlexicalized
subtrees. We also found that counts of subtrees
with several nonheadwords are important,
resulting in improved parse accuracy over
previous parsers tested on the WSJ.
2 The DOP1 Model
To-date, the Data Oriented Parsing model has
mainly been applied to corpora of trees whose
labels consist of primitive symbols (but see Bod
& Kaplan 1998; Bod 2000c, 2001). Let us illus-
trate the original DOP model presented in Bod
(1993), called DOP1, with a simple example.
Assume a corpus consisting of only two trees:
NP VP
S
NP
Mary
V
likes
John
NP VP
S
NPV
Peter
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:
NPVP
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 equal to the number of
occurrences of t, | t |, divided by the total number
of occurrences of all subtrees t' with the same
root label as t. Let r(t) return the root label of t.
Then we may write:
P(t)  =
   
| t |
Σ 
t': r(t')= r(t)
  | t' |
In most applications of DOP1, the subtree
probabilities are smoothed by the technique
described in Bod (1996) which is based on
Good-Turing. (The subtree probabilities are not
smoothed by backing off to smaller subtrees,
since these are taken into account by the parse
tree probability, as we will see.)
The probability of a derivation t
1°
...
°
t
n
 is
computed by the product of the probabilities of
its subtrees t
i
:
P(t
1°
...
°
t
n
)  =  Π
i 
P(t
i
)
As we have seen, there may be several distinct
derivations that generate the same parse tree. The
probability of a parse tree T is thus the sum of the
probabilities of its distinct derivations. Let t
id 
be
the i-th subtree in the derivation d that produces
tree T, then the probability of T is given by
P(T)  =  Σ
d
Π
i 
P(t
id
)
Thus the DOP1 model considers counts of
subtrees of a wide range of sizes in computing
the probability of a tree: everything from counts
of single-level rules to counts of entire trees. This
means that the model is sensitive to the frequency
of large subtrees while taking into account the
smoothing effects of counts of small subtrees.
Note that the subtree probabilities in DOP1
are directly estimated from their relative frequen-
cies. A number of alternative subtree estimators
have  been proposed for DOP1 (cf. Bonnema et
al 1999), including maximum likelihood
estimation (Bod 2000b). But since the relative
frequency estimator has so far not been outper -
formed by any other estimator for DOP1, we
will stick to this estimator in the current paper.
3 Computational Issues
Bod (1993) showed how standard chart parsing
techniques can be applied to DOP1. Each corpus-
subtree t is converted into a context-free rule r
where the lefthand side of r corresponds to the
root label of t  and the righthand side of r
corresponds to the frontier labels of t. Indices link
the rules to the original subtrees so as to maintain
the subtree's internal structure and probability.
These rules are used to create a derivation forest
for a sentence (using a CKY parser), and the
most probable parse is computed by sampling a
sufficiently large number of random derivations
from the forest ("Monte Carlo disambiguation",
see Bod 1998). While this technique has been
successfully applied to parsing the ATIS portion
in the Penn Treebank (Marcus et al. 1993), it is
extremely time consuming. This is mainly
because the number of random derivations that
should be sampled to reliably estimate the most
probable parse increases exponentially with the
sentence length (see Goodman 1998). It is
therefore questionable whether Bod's sampling
technique can be scaled to larger domains such as
the WSJ portion in the Penn Treebank.
Goodman (1996, 1998) showed how DOP1
can be reduced to a compact stochastic context-
free grammar (SCFG) which contains exactly
eight SCFG rules for each node in the training set
trees. Although Goodman's method does still not
allow for an efficient computation of the most
probable parse (in fact, the problem of computing
the most probable parse in DOP1 is NP-hard --
see Sima'an 1999), his method does allow for an
efficient computation of the "maximum constit-
uents parse", i.e. the parse tree that is most likely
to have the largest number of correct constituents.
Goodman has shown on the ATIS corpus that
the maximum constituents parse performs at
least as well as the most probable parse if all
subtrees are used. Unfortunately, Goodman's
reduction method is only beneficial if indeed all
subtrees are used. Sima'an (1999: 108) argues
that there may still be an isomorphic SCFG for
DOP1 if the corpus-subtrees are restricted in size
or lexicalization, but that the number of the rules
explodes in that case.
In this paper we will use Bod's subtree-to-
rule conversion method for studying the impact
of various subtree restrictions on the WSJ
corpus. However, we will not use Bod's Monte
Carlo sampling technique from complete
derivation forests, as this turned out to be
prohibitive for WSJ sentences. Instead, we
employ a Viterbi n-best search using a CKY
algorithm and estimate the most probable parse
from the 1,000 most probable derivations,
summing up the probabilities of derivations that
generate the same tree. Although this heuristic
does not guarantee that the most probable parse is
actually found, it is shown in Bod (2000a) to
perform at least as well as the estimation of the
most probable parse with Monte Carlo
techniques. However, in computing the 1,000
most probable derivations by means of Viterbi it
is prohibitive to keep track of all subderivations at
each edge in the chart (at least for such a large
corpus as the WSJ). As in most other statistical
parsing systems we therefore use the pruning
technique described in Goodman (1997) and
Collins (1999: 263-264) which assigns a score to
each item in the chart equal to the product of the
inside probability of the item and its prior
probability. Any item with a score less than 10
−5
times of that of the best item is pruned from the
chart.
4 What is the Minimal Subtree Set that
Achieves Maximal Parse Accuracy?
4.1 The base line
For our base line parse accuracy, we used the
now standard division of the WSJ (see Collins
1997, 1999; Charniak 1997, 2000; Ratnaparkhi
1999) with sections 2 through 21 for training
(approx. 40,000 sentences) and section 23 for
testing (2416 sentences ≤ 100 words); section 22
was used as development set. All trees were
stripped off their semantic tags, co-reference
information and quotation marks. We used all
training set subtrees of depth 1, but due to
memory limitations we used a subset of the
subtrees larger than depth 1, by taking for each
depth a random sample of 400,000 subtrees.
These random subtree samples were not selected
by first exhaustively computing the complete set
of subtrees (this was computationally prohibit -
ive). Instead, for each particular depth > 1 we
sampled subtrees by randomly selecting a node
in a random tree from the training set, after which
we selected random expansions from that node
until a subtree of the particular depth was
obtained. We repeated this procedure 400,000
times for each depth > 1 and ≤ 14. Thus no
subtrees of depth > 14 were used. This resulted
in a base line subtree set of 5,217,529 subtrees
which were smoothed by the technique described
in Bod (1996) based on Good-Turing. Since our
subtrees are allowed to be lexicalized (at their
frontiers), we did not use a separate part-of-
speech tagger: the test sentences were directly
parsed by the training set subtrees. For words
that were unknown in our subtree set, we
guessed their categories by means of the method
described in Weischedel et al. (1993) which uses
statistics on word-endings, hyphenation and
capitalization. The guessed category for each
unknown word was converted into a depth-1
subtree and assigned a probability by means of
simple Good-Turing estimation (see Bod 1998).
The most probable parse for each test sentence
was estimated from the 1,000 most probable
derivations of that sentence, as described in
section 3.
We used "evalb"
1
 to compute the standard
PARSEVAL scores for our parse results. We
focus on the Labeled Precision (LP) and Labeled
Recall (LR) scores only in this paper, as these are
commonly used to rank parsing systems.
Table 1 shows the LP and LR scores
obtained with our base line subtree set, and
compares these scores with those of previous
stochastic parsers tested on the WSJ (respectively
Charniak 1997, Collins 1999, Ratnaparkhi 1999,
and Charniak 2000).
        The table shows that by using the base line
subtree set, our parser outperforms most
previous parsers but it performs worse than the
parser in Charniak (2000). We will use our
scores of 89.5% LP and 89.3% LR (for test
sentences ≤ 40 words) as the base line result
against which the effect of various subtree
restrictions is investigated. While most subtree
restrictions diminish the accuracy scores, we will
see that there are restrictions that improve our
scores, even beyond those of Charniak (2000).
1
 http://www.cs.nyu.edu/cs/projects/proteus/evalb/
We will initially study our subtree restrictions
only for test sentences ≤ 40 words (2245
sentences), after which we will give in 4.6 our
results for all test sentences ≤ 100 words (2416
sentences). While we have tested all subtree
restrictions initially on the development set
(section 22 in the WSJ), we believe that it is
interesting and instructive to report these subtree
restrictions on the test set (section 23) rather than
reporting our best result only.
Parser LPLR
≤ 40 words
Char97 87.4 87.5
Coll99 88.7 88.5
Char00 90.1 90.1
Bod00 89.5 89.3
≤ 100 words
Char97 86.6 86.7
Coll99 88.3 88.1
Ratna99 87.5 86.3
Char00 89.5 89.6
Bod00 88.6 88.3
Table 1. Parsing results with the base line subtree
set compared to previous parsers
4.2 The impact of subtree size
Our first subtree restriction is concerned with
subtree size. We therefore performed experi-
ments with versions of DOP1 where the base
line subtree set is restricted to subtrees with a
certain maximum depth. Table 2 shows the
results of these experiments.
    depth of
    subtrees  LP  LR
  1 76.0 71.8
≤2 80.1 76.5
≤3 82.8 80.9
≤4 84.7 84.1
≤5 85.5 84.9
≤6 86.2 86.0
≤8 87.9 87.1
≤10 88.6 88.0
≤12 89.1 88.8
≤14 89.5 89.3
Table 2. Parsing results for different subtree
depths (for test sentences ≤ 40 words)
Our scores for subtree-depth 1 are comparable to
Charniak's treebank grammar if tested on word
strings (see Charniak 1997). Our scores are
slightly better, which may be due to the use of a
different unknown word model. Note that the
scores consistently improve if larger subtrees are
taken into account. The highest scores are
obtained if the full base line subtree set is used,
but they remain behind the results of Charniak
(2000). One might expect that our results further
increase if even larger subtrees are used; but due
to memory limitations we did not perform
experiments with subtrees larger than depth 14.
4.3 The impact of lexical context
The more words a subtree contains in its frontier,
the more lexical dependencies can be taken into
account. To test the impact of the lexical context
on the accuracy, we performed experiments with
different versions of the model where the base
line subtree set is restricted to subtrees whose
frontiers contain a certain maximum number of
words; the subtree depth in the base line subtree
set was not constrained (though no subtrees
deeper than 14 were in this base line set). Table 3
shows the results of our experiments.
   # words
  in subtrees  LP  LR
≤1 84.4 84.0
≤2 85.2 84.9
≤3 86.6 86.3
≤4 87.6 87.4
≤6 88.0 87.9
≤8 89.2 89.1
≤10 90.2 90.1
≤11 90.8 90.4
≤12 90.8 90.5
≤13 90.4 90.3
≤14 90.3 90.3
≤16 89.9 89.8
  unrestricted 89.5 89.3
Table 3. Parsing results for different subtree
lexicalizations (for test sentences ≤ 40 words)
We see that the accuracy initially increases when
the lexical context is enlarged, but that the
accuracy decreases if the number of words in the
subtree frontiers exceeds 12 words. Our highest
scores of 90.8% LP and 90.5% LR outperform
the scores of the best previously published parser
by Charniak (2000) who obtains 90.1% for both
LP and LR. Moreover, our scores also outper-
form the reranking technique of Collins (2000)
who reranks the output of the parser of Collins
(1999) using a boosting method based on
Schapire & Singer (1998), obtaining 90.4% LP
and 90.1% LR. We have thus found a subtree
restriction which does not decrease the parse
accuracy but even improves it. This restriction
consists of an upper bound of 12 words in the
subtree frontiers, for subtrees ≤ depth 14. (We
have also tested this lexical restriction in
combination with subtrees smaller than depth 14,
but this led to a decrease in accuracy.)
4.4 The impact of structural context
Instead of investigating the impact of lexical
context, we may also be interested in studying the
importance of structural context. We may raise
the question as to whether we need all unlexica-
lized subtrees, since such subtrees do not contain
any lexical information, although they may be
useful to smooth lexicalized subtrees. We accom-
plished a set of experiments where unlexicalized
subtrees of a certain minimal depth are deleted
from the base line subtree set, while all
lexicalized subtrees up to 12 words are retained.
depth of deleted  
  unlexicalized  
     subtrees  LP  LR
  ≥1 79.9 77.7
  ≥2 86.4 86.1
  ≥3 89.9 89.5
  ≥4 90.6 90.2
  ≥5 90.7 90.6
  ≥6 90.8 90.6
  ≥7 90.8 90.5
  ≥8 90.8 90.5
  ≥10 90.8 90.5
  ≥12 90.8 90.5
Table 4. Parsing results for different structural
context (for test sentences ≤ 40 words)
Table 4 shows that the accuracy increases if
unlexicalized subtrees are retained, but that
unlexicalized subtrees larger than depth 6 do not
contribute to any further increase in accuracy. On
the contrary, these larger subtrees even slightly
decrease the accuracy. The highest scores
obtained are: 90.8% labeled precision and 90.6%
labeled recall. We thus conclude that pure
structural context without any lexical information
contributes to higher parse accuracy (even if there
exists an upper bound for the size of structural
context). The importance of structural context is
consonant with Johnson (1998) who showed that
structural context from higher nodes in the tree
(i.e. grandparent nodes) contributes to higher
parse accuracy. This mirrors our result of the
importance of unlexicalized subtrees of depth 2.
But our results show that larger structural context
(up to depth 6) also contributes to the accuracy.
4.5 The impact of nonheadword dependencies
We may also raise the question as to whether we
need almost arbitrarily large lexicalized subtrees
(up to 12 words) to obtain our best results. It
could be the case that DOP's gain in parse
accuracy with increasing subtree depth is due to
the model becoming sensitive to the influence of
lexical heads higher in the tree, and that this gain
could also be achieved by a more compact model
which associates each nonterminal with its
headword, such as a head-lexicalized SCFG.
Head-lexicalized stochastic grammars have
recently become increasingly popular (see Collins
1997, 1999; Charniak 1997, 2000). These
grammars are based on Magerman's head-
percolation scheme to determine the headword of
each nonterminal (Magerman 1995). Unfortunat-
ely this means that head-lexicalized stochastic
grammars are not able to capture dependency
relations between words that according to
Magerman's head-percolation scheme are
"nonheadwords" -- e.g. between more and than
in the WSJ construction carry more people than
cargo where neither more nor than are head-
words of the NP constituent more people than
cargo. A frontier-lexicalized DOP model, on the
other hand, captures these dependencies since it
includes subtrees in which more and than are the
only frontier words. One may object that this
example is somewhat far-fetched, but Chiang
(2000) notes that head-lexicalized stochastic
grammars fall short in encoding even simple
dependency relations such as between left  and
John in the sentence John should have left . This
is because Magerman's head-percolation scheme
makes should and have the heads of their
respective VPs so that there is no dependency
relation between the verb left and its subject John.
Chiang observes that almost a quarter of all
nonempty subjects in the WSJ appear in such a
configuration.
In order to isolate the contribution of
nonheadword dependencies to the parse accuracy,
we eliminated all subtrees containing a certain
maximum number of nonheadwords, where a
nonheadword of a subtree is a word which
according to Magerman's scheme is not a
headword of the subtree's root nonterminal
(although such a nonheadword may of course be
a headword of one of the subtree's internal
nodes). In the following experiments we used the
subtree set for which maximum accuracy was
obtained in our previous experiments, i.e.
containing all lexicalized subtrees with maximally
12 frontier words and all unlexicalized subtrees
up to depth 6.
# nonheadwords  
    in subtrees  LP  LR
    0 89.6 89.6
   ≤1 90.2 90.1
   ≤2 90.4 90.2
   ≤3 90.3 90.2
   ≤4 90.6 90.4
   ≤5 90.6 90.6
   ≤6 90.6 90.5
   ≤7 90.7 90.7
   ≤8 90.8 90.6
    unrestricted 90.8 90.6
Table 5. Parsing results for different number of
nonheadwords (for test sentences ≤ 40 words)
Table 5 shows that nonheadwords contribute to
higher parse accuracy: the difference between
using no and all nonheadwords is 1.2% in LP
and 1.0% in LR. Although this difference is
relatively small, it does indicate that nonhead-
word dependencies should preferably not be
discarded in the WSJ. We should note, however,
that most other stochastic parsers do include
counts of single nonheadwords: they appear in
the backed-off statistics of these parsers (see
Collins 1997, 1999; Charniak 1997; Goodman
1998). But our parser is the first parser that also
includes counts between two or more non-
headwords, to the best of our knowledge, and
these counts lead to improved performance, as
can be seen in table 5.
4.6 Results for all sentences
We have seen that for test sentences ≤ 40 words,
maximal parse accuracy was obtained by a
subtree set which is restricted to subtrees with not
more than 12 words and which does not contain
unlexicalized subtrees deeper than 6.
2
 We used
2
 It may be noteworthy that for the development
set (section 22 of WSJ), maximal parse accuracy
was obtained with exactly the same subtree
restrictions. As explained in 4.1, we initially tested
all restrictions on the development set, but we
preferred to report the effects of these restrictions
for the test set.
these restrictions to test our model on all
sentences ≤ 100 words from the WSJ test set.
This resulted in an LP of 89.7% and an LR of
89.7%. These scores slightly outperform the best
previously published parser by Charniak (2000),
who obtained 89.5% LP and 89.6% LR for test
sentences ≤ 100 words. Only the reranking
technique proposed by Collins (2000) slightly
outperforms our precision score, but not our
recall score: 89.9% LP and 89.6% LR.
5   Discussion: Converging Approaches
The main goal of this paper was to find the
minimal set of fragments which achieves
maximal parse accuracy in Data Oriented
Parsing. We have found that this minimal set of
fragments is very large and extremely redundant:
highest parse accuracy is obtained by employing
only two constraints on the fragment set: a
restriction of the number of words in the
fragment frontiers to 12 and a restriction of the
depth of unlexicalized fragments to 6. No other
constraints were warranted.
There is an important question why
maximal parse accuracy occurs with exactly these
constraints. Although we do not know the
answer to this question, we surmise that these
constraints differ from corpus to corpus and are
related to general data sparseness effects. In
previous experiments with DOP1 on smaller and
more restricted domains we found that the parse
accuracy decreases also after a certain maximum
subtree depth (see Bod 1998; Sima'an 1999). We
expect that also for the WSJ the parse accuracy
will decrease after a certain depth, although we
have not been able to find this depth so far.
A major difference between our approach
and most other models tested on the WSJ is that
the DOP model uses frontier lexicalization while
most other models use constituent lexicalization
(in that they associate each constituent non -
terminal with its lexical head -- see Collins 1996,
1999; Charniak 1997; Eisner 1997). The results
in this paper indicate that frontier lexicalization is
a promising alternative to constituent lexicaliza-
tion. Our results also show that the linguistically
motivated constraint which limits the statistical
dependencies to the locality of headwords of
constituents is too narrow. Not only are counts of
subtrees with nonheadwords important, also
counts of unlexicalized subtrees up to depth 6
increase the parse accuracy.
The only other model that uses frontier
lexicalization and that was tested on the standard
WSJ split is Chiang (2000) who extracts a
stochastic tree-insertion grammar or STIG
(Schabes & Waters 1996) from the WSJ,
obtaining 86.6% LP and 86.9% LR for sentences
≤ 40 words. However, Chiang's approach is
limited in at least two respects. First, each
elementary tree in his STIG is lexicalized with
exactly one lexical item, while our results show
that there is an increase in parse accuracy if more
lexical items and also if unlexicalized trees are
included (in his conclusion Chiang acknowledges
that "multiply anchored trees" may be important).
Second, Chiang computes the probability of a
tree by taking into account only one derivation,
while in STIG, like in DOP1, there can be several
derivations that generate the same tree.
Another difference between our approach
and most other models is that the underlying
grammar of DOP is based on a treebank
grammar (cf. Charniak 1996, 1997), while most
current stochastic parsing models use a "markov
grammar" (e.g. Collins 1999; Charniak 2000).
While a treebank grammar only assigns
probabilities to rules or subtrees that are seen in a
treebank, a markov grammar assigns proba-
bilities to any possible rule, resulting in a more
robust model. We expect that the application of
the markov grammar approach to DOP will
further improve our results. Research in this
direction is already ongoing, though it has been
tested for rather limited subtree depths only (see
Sima'an 2000).
Although we believe that our main result is
to have shown that almost arbitrary fragments
within parse trees are important, it is surprising
that a relatively simple model like DOP1
outperforms most other stochastic parsers on the
WSJ. Yet, to the best of our knowledge, DOP is
the only model which does not a priori restrict
the fragments that are used to compute the most
probable parse. Instead, it starts out by taking into
account all fragments seen in a treebank and then
investigates fragment restrictions to discover the
set of relevant fragments. From this perspective,
the DOP approach can be seen as striving for the
same goal as other approaches but from a dif-
ferent direction. While other approaches usually
limit the statistical dependencies beforehand (for
example to headword dependencies) and then try
to improve parse accuracy by gradually letting in
more dependencies, the DOP approach starts out
by taking into account as many dependencies as
possible and then tries to constrain them without
losing parse accuracy. It is not unlikely that these
two opposite directions will finally converge to
the same, true set of statistical dependencies for
natural language parsing.
As it happens, quite some convergence has
already taken place. The history of stochastic
parsing models shows a consistent increase in the
scope of statistical dependencies that are captured
by these models. Figure 4 gives a (very)
schematic overview of this increase (see Carroll
& Weir 2000, for a more detailed account of a
subsumption lattice where SCFG is at the bottom
and DOP at the top).
  
    
context-free rules
Charniak (1996)
Collins (1996),
Eisner (1996) 
context-free rules,   
headwords
Charniak (1997) context-free rules,
headwords,
grandparent nodes
Collins (2000) context-free rules,   
headwords,
grandparent nodes/rules,   
bigrams, two-level rules,   
two-level bigrams,   
nonheadwords
Bod (1992)
all fragments within   
parse trees
Scope of Statistical
Dependencies 
Model
Figure 4. Schematic overview of the increase of
statistical dependencies by stochastic parsers
Thus there seems to be a convergence towards a
maximalist model which "takes all fragments [...]
and lets the statistics decide" (Bod 1998: 5).
While early head-lexicalized grammars restricted
the fragments to the locality of headwords (e.g.
Collins 1996; Eisner 1996), later models showed
the importance of including context from higher
nodes in the tree (Charniak 1997; Johnson 1998).
This mirrors our result of the utility of
(unlexicalized) fragments of depth 2 and larger.
The importance of including single nonhead-
words is now also uncontroversial (e.g. Collins
1997, 1999; Charniak 2000), and the current
paper has shown the importance of including two
and more nonheadwords. Recently, Collins
(2000) observed that "In an ideal situation we
would be able to encode arbitrary features h
s
,
thereby keeping track of counts of arbitrary
fragments within parse trees". This is in perfect
correspondence with the DOP philosophy.
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