Deep Syntactic Processing by Combining Shallow Methods
P´eter Dienes and Amit Dubey
Department of Computational Linguistics
Saarland University
PO Box 15 11 50
66041 Saarbr¨ucken, Germany
{dienes,adubey}@coli.uni-sb.de
Abstract
We present a novel approach for find-
ing discontinuities that outperforms pre-
viously published results on this task.
Rather than using a deeper grammar for-
malism, our system combines a simple un-
lexicalized PCFG parser with a shallow
pre-processor. This pre-processor, which
we call a trace tagger, does surprisingly
well on detecting where discontinuities
can occur without using phase structure
information.
1 Introduction
In this paper, we explore a novel approach for find-
ing long-distance dependencies. In particular, we
detect such dependencies, or discontinuities, in a
two-step process: (i) a conceptually simple shal-
low tagger looks for sites of discontinuties as a pre-
processing step, before parsing; (ii) the parser then
finds the dependent constituent (antecedent).
Clearly, information about long-distance relation-
ships is vital for semantic interpretation. However,
such constructions prove to be difficult for stochas-
tic parsers (Collins et al., 1999) and they either avoid
tackling the problem (Charniak, 2000; Bod, 2003)
or only deal with a subset of the problematic cases
(Collins, 1997).
Johnson (2002) proposes an algorithm that is
able to find long-distance dependencies, as a post-
processing step, after parsing. Although this algo-
rithm fares well, it faces the problem that stochastic
parsers not designed to capture non-local dependen-
cies may get confused when parsing a sentence with
discontinuities. However, the approach presented
here is not susceptible to this shortcoming as it finds
discontinuties before parsing.
Overall, we present three primary contributions.
First, we extend the mechanism of adding gap vari-
ables for nodes dominating a site of discontinu-
ity (Collins, 1997). This approach allows even a
context-free parser to reliably recover antecedents,
given prior information about where discontinuities
occur. Second, we introduce a simple yet novel
finite-state tagger that gives exactly this information
to the parser. Finally, we show that the combina-
tion of the finite-state mechanism, the parser, and
our new method for antecedent recovery can com-
petently analyze discontinuities.
The overall organization of the paper is as fol-
lows. First, Section 2 sketches the material we use
for the experiments in the paper. In Section 3, we
propose a modification to a simple PCFG parser that
allows it to reliably find antecedents if it knows the
sites of long-distance dependencies. Then, in Sec-
tion 4, we develop a finite-state system that gives the
parser exactly that information with fairly high accu-
racy. We combine the models in Section 5 to recover
antecedents. Section 6 discusses related work.
2 Annotation of empty elements
Different linguistic theories offer various treatments
of non-local head–dependent relations (referred to
by several other terms such as extraction, discon-
tinuity, movement or long-distance dependencies).
The underlying idea, however, is the same: extrac-
tion sites are marked in the syntactic structure and
this mark is connected (co-indexed) to the control-
Type Freq. Example
NP–NP 987 Sam was seen *
WH–NP 438 the woman who you saw *T*
PRO–NP 426 * to sleep is nice
COMP–SBAR 338 Sam said 0 Sasha snores
UNIT 332 $ 25 *U*
WH–S 228 Sam had to go, Sasha said *T*
WH–ADVP 120 Sam told us how he did it *T*
CLAUSE 118 Sam had to go, Sasha said 0
COMP–WHNP 98 the woman 0 we saw *T*
ALL 3310
Table 1: Most frequent types of EEs in Section 0.
ling constituent.
The experiments reported here rely on a train-
ing corpus annotated with non-local dependencies
as well as phrase-structure information. We used
the Wall Street Journal (WSJ) part of the Penn Tree-
bank (Marcus et al., 1993), where extraction is rep-
resented by co-indexing an empty terminal element
(henceforth EE) to its antecedent. Without commit-
ting ourselves to any syntactic theory, we adopt this
representation.
Following the annotation guidelines (Bies et
al., 1995), we distinguish seven basic types of
EEs: controlled NP-traces (NP), PROs (PRO),
traces of Aa0 -movement (mostly wh-movement:
WH), empty complementizers (COMP), empty units
(UNIT), and traces representing pseudo-attachments
(shared constituents, discontinuous dependencies,
etc.: PSEUDO) and ellipsis (ELLIPSIS). These la-
bels, however, do not identify the EEs uniquely: for
instance, the label WH may represent an extracted
NP object as well as an adverb moved out of the
verb phrase. In order to facilitate antecedent re-
covery and to disambiguate the EEs, we also anno-
tate them with their parent nodes. Furthermore, to
ease straightforward comparison with previous work
(Johnson, 2002), a new label CLAUSE is introduced
for COMP-SBAR whenever it is followed by a moved
clause WH–S. Table 1 summarizes the most frequent
types occurring in the development data, Section 0
of the WSJ corpus, and gives an example for each,
following Johnson (2002).
For the parsing and antecedent recovery exper-
iments, in the case of WH-traces (WH–a1a2a1a2a1 ) and
SBAR
NP
who
Sa3a5a4a7a6a9a8a11a10a2a12a14a13a11a15a2a16
NP
you
VPa3a5a4a7a6a9a8a11a10a2a12a14a13a17a15a2a16
V
saw
NPa3a5a4a7a6a9a8a11a10a2a12a14a13a17a15a2a16
*WH-NP*
Figure 1: Threading gap+WH-NP.
controlled NP-traces (NP–NP), we follow the stan-
dard technique of marking nodes dominating the
empty element up to but not including the par-
ent of the antecedent as defective (missing an ar-
gument) with a gap feature (Gazdar et al., 1985;
Collins, 1997).1 Furthermore, to make antecedent
co-indexation possible with many types of EEs, we
generalize Collins’ approach by enriching the anno-
tation of non-terminals with the type of the EE in
question (eg. WH–NP) by using different gap+ fea-
tures (gap+WH-NP; cf. Figure 1). The original non-
terminals augmented with gap+ features serve as
new non-terminal labels.
In the experiments, Sections 2–21 were used to
train the models, Section 0 served as a develop-
ment set for testing and improving models, whereas
we present the results on the standard test set, Sec-
tion 23.
3 Parsing with empty elements
The present section explores whether an unlexical-
ized PCFG parser can handle non-local dependen-
cies: first, is it able to detect EEs and, second, can
it find their antecedents? The answer to the first
question turns out to be negative: due to efficiency
reasons and the inappropriateness of the model, de-
tecting all types of EEs is not feasible within the
parser. Antecedents, however, can be reliably recov-
ered provided a parser has perfect knowledge about
EEs occurring in the input. This shows that the main
bottleneck is detecting the EEs and not finding their
antecedents. In the following section, therefore, we
explore how we can provide the parser with infor-
mation about EE sites in the current sentence without
1This technique fails for 82 sentences of the treebank where
the antecedent does not c-command the corresponding EE.
relying on phrase structure information.
3.1 Method
There are three modifications required to allow a
parser to detect EEs and resolve antecedents. First,
it should be able to insert empty nodes. Second, it
must thread the gap+ variables to the parent node of
the antecedent. Knowing this node is not enough,
though. Since the Penn Treebank grammar is not
binary-branching, the final task is to decide which
child of this node is the actual antecedent.
The first two modifications are not diffi-
cult conceptually. A bottom-up parser can be
easily modified to insert empty elements (c.f.
Dienes and Dubey (2003)). Likewise, the changes
required to include gap+ categories are not compli-
cated: we simply add the gap+ features to the non-
terminal category labels.
The final and perhaps most important concern
with developing a gap-threading parser is to ensure
it is possible to choose the correct child as the an-
tecedent of an EE. To achieve this task, we em-
ploy the algorithm presented in Figure 2. At any
node in the tree where the children, all together,
have more gap+ features activated than the par-
ent, the algorithm deduces that a gap+ must have
an antecedent. It then picks a child as the an-
tecedent and recursively removes the gap+ feature
corresponding to its EE from the non-terminal la-
bels. The algorithm has a shortcoming, though: it
cannot reliably handle cases when the antecedent
does not c-command its EE. This mostly happens
with PSEUDOs (pseudo-attachments), where the al-
gorithm gives up and (wrongly) assumes they have
no antecedent.
Given the perfect trees of the development set,
the antecedent recovery algorithm finds the correct
antecedent with 95% accuracy, rising to 98% if
PSEUDOs are excluded. Most of the remaining mis-
takes are caused either by annotation errors, or by
binding NP-traces (NP–NP) to adjunct NPs, as op-
posed to subject NPs.
The parsing experiments are carried out with an
unlexicalized PCFG augmented with the antecedent
recovery algorithm. We use an unlexicalized model
to emphasize the point that even a simple model de-
tects long distance dependencies successfully. The
parser uses beam thresholding (Goodman, 1998) to
for a tree T, iterate over nodes bottom-up
for a node with rule P a0 C0
a1a2a1a2a1
Cn
N a3 multiset of EEs in P
M a3 multiset of EEs in C0
a1a2a1a2a1
Cn
foreach EE of type e in M a4 N
pick a j such that e allows Cj
as an antecedent
pick a k such that k a5a6 j and
Ck dominates an EE of type e
if no such j or k exist,
return no antecedent
else
bind the EE dominated by Ck to
the antecedent Cj
Figure 2: The antecedent recovery algorithm.
ensure efficient parsing. PCFG probabilities are cal-
culated in the standard way (Charniak, 1993). In
order to keep the number of independently tunable
parameters low, no smoothing is used.
The parser is tested under two different condi-
tions. First, to assess the upper bound an EE-
detecting unlexicalized PCFG can achieve, the input
of the parser contains the empty elements as sepa-
rate words (PERFECT). Second, we let the parser
introduce the EEs itself (INSERT).
3.2 Evaluation
We evaluate on all sentences in the test section of the
treebank. As our interest lies in trace detection and
antecedent recovery, we adopt the evaluation mea-
sures introduced by Johnson (2002). An EE is cor-
rectly detected if our model gives it the correct la-
bel as well as the correct position (the words before
and after it). When evaluating antecedent recovery,
the EEs are regarded as four-tuples, consisting of the
type of the EE, its location, the type of its antecedent
and the location(s) (beginning and end) of the an-
tecedent. An antecedent is correctly recovered if
all four values match the gold standard. The preci-
sion, recall, and the combined F-score is presented
for each experiment. Missed parses are ignored for
evaluation purposes.
3.3 Results
The main results for the two conditions are summa-
rized in Table 2. In the INSERT case, the parser de-
tects empty elements with precision 64.7%, recall
40.3% and F-Score 49.7%. It recovers antecedents
Condition PERFECT INSERT
Empty element
detection (F-score) – 49a0 7%
Antecedent recovery
(F-score) 91a0 4% 43a0 0%
Parsing time (sec/sent) 2a0 5 21
Missed parses 1a0 6% 44a0 3%
Table 2: EE detection, antecedent recovery, parsing
times, and missed parses for the parser
with overall precision 55.7%, recall 35.0% and F-
score 43.0%. With a beam width of 1000, about
half of the parses were missed, and successful parses
take, on average, 21 seconds per sentence and enu-
merate 1.7 million edges. Increasing the beam size
to 40000 decreases the number of missed parses
marginally, while parsing time increases to nearly
two minutes per sentence, with 2.9 million edges
enumerated.
In the PERFECT case, when the sites of the empty
elements are known before parsing, only about 1.6%
of the parses are missed and average parsing time
goes down to 2a0 5 seconds per sentence. More impor-
tantly, the overall precision and recall of antecedent
recovery is 91.4%.
3.4 Discussion
The result of the experiment where the parser is to
detect long-distance dependencies is negative. The
parser misses too many parses, regardless of the
beam size. This cannot be due to the lack of smooth-
ing: the model with perfect information about the
EE-sites does not run into the same problem. Hence,
the edges necessary to construct the required parse
are available but, in the INSERT case, the beam
search loses them due to unwanted local edges hav-
ing a higher probability. Doing an exhaustive search
might help in principle, but it is infeasible in prac-
tice. Clearly, the problem is with the parsing model:
an unlexicalized PCFG parser is not able to detect
where EEs can occur, hence necessary edges get low
probability and are, thus, filtered out.
The most interesting result, though, is the dif-
ference in speed and in antecedent recovery accu-
racy between the parser that inserts traces, and the
parser which uses perfect information from the tree-
bank about the sites of EEs. Thus, the question
wi a6 X; wia1 1 a6 X; wia2 1 a6 X
X is a prefix of wi, a3 X a3a5a4 4
X is a suffix of wi, a3 X a3a6a4 4
wi contains a number
wi contains uppercase character
wi contains hyphen
lia1 1 a6 X
posi a6 X; posia1 1 a6 X; posia2 1 a6 X
posia1 1 posi a6 XY
posia1 2 posia1 1 posi a6 XYZ
posi posia2 1 a6 XY
posi posia2 1 posia2 2 a6 XYZ
Table 3: Local features at position i a7 1.
naturally arises: could EEs be detected before pars-
ing? The benefit would be two-fold: EEs might be
found more reliably with a different module, and the
parser would be fast and accurate in recovering an-
tecedents. In the next section we show that it is in-
deed possible to detect EEs without explicit knowl-
edge of phrase structure, using a simple finite-state
tagger.
4 Detecting empty elements
This section shows that EEs can be detected fairly
reliably before parsing, i.e. without using phrase
structure information. Specifically, we develop a
finite-state tagger which inserts EEs at the appro-
priate sites. It is, however, unable to find the an-
tecedents for the EEs; therefore, in the next section,
we combine the tagger with the PCFG parser to re-
cover the antecedents.
4.1 Method
Detecting empty elements can be regarded as a sim-
ple tagging task: we tag words according to the ex-
istence and type of empty elements preceding them.
For example, the word Sasha in the sentence
Sam said COMP–SBAR Sasha snores.
will get the tag EE=COMP–SBAR, whereas the word
Sam is tagged with EE=* expressing the lack of an
EE immediately preceding it. If a word is preceded
by more than one EE, such as to in the following
example, it is tagged with the concatenation of the
two EEs, i.e., EE=COMP–WHNP PRO–NP.
It would have been too late COMP–WHNP
PRO–NP to think about on Friday.
Target Matching regexp Explanation
NP–NP BE RB* VBN passive
a0
NP–NP
PRO-NPa1
RB* to RB* VB to-infinitive
N [,:] RB* VBG gerund
COMP–SBAR (V
a3 ,) !that* (
MD
a3
V) lookahead for that
WH–NP !IN a2
a3a4 WP
WDT
COMP–WHNP
a5a6
a7
!WH–NP* V lookback for pending WHNPs
WH–ADVP WRB !WH–ADVP* V !WH–ADVP* [.,:] lookback for pending WHADVP before a verb
UNIT $ CD* $ sign before numbers
Table 4: Non-local binary feature templates; the EE-site is indicated by
Although this approach is closely related to POS-
tagging, there are certain differences which make
this task more difficult. Despite the smaller tagset,
the data exhibits extreme sparseness: even though
more than 50% of the sentences in the Penn Tree-
bank contain some EEs, the actual number of EEs is
very small. In Section 0 of the WSJ corpus, out of
the 46451 tokens only 3056 are preceded by one or
more EEs, that is, approximately 93.5% of the words
are tagged with the EE=* tag.
The other main difference is the apparently non-
local nature of the problem, which motivates our
choice of a Maximum Entropy (ME) model for the
tagging task (Berger et al., 1996). ME allows the
flexible combination of different sources of informa-
tion, i.e., local and long-distance cues characterizing
possible sites for EEs. In the ME framework, linguis-
tic cues are represented by (binary-valued) features
( fi), the relative importance (weight, λi) of which is
determined by an iterative training algorithm. The
weighted linear combination of the features amount
to the log-probability of the label (l) given the con-
text (c):
pa8 la9 ca10a12a11 1Z
a8 ca10
expa8 ∑i λi fia8 la13 ca10a14a10 (1)
where Za8 ca10 is a context-dependent normalizing fac-
tor to ensure that pa8 la9 ca10 be a proper probability dis-
tribution. We determine weights for the features
with a modified version of the Generative Iterative
Scaling algorithm (Curran and Clark, 2003).
Templates for local features are similar to the ones
employed by Ratnaparkhi (1996) for POS-tagging
(Table 3), though as our input already includes POS-
tags, we can make use of part-of-speech information
as well. Long-distance features are simple hand-
written regular expressions matching possible sites
for EEs (Table 4). Features and labels occurring less
than 10 times in the training corpus are ignored.
Since our main aim is to show that finding empty
elements can be done fairly accurately without us-
ing a parser, the input to the tagger is a POS-tagged
corpus, containing no syntactic information. The
best label-sequence is approximated by a bigram
Viterbi-search algorithm, augmented with variable
width beam-search.
4.2 Results
The results of the EE-detection experiment are sum-
marized in Table 5. The overall unlabeled F-score is
85a0 3%, whereas the labeled F-score is 79a0 1%, which
amounts to 97a0 9% word-level tagging accuracy.
For straightforward comparison with Johnson’s
results, we must conflate the categories PRO–NP and
NP–NP. If the trace detector does not need to differ-
entiate between these two categories, a distinction
that is indeed important for semantic analysis, the
overall labeled F-score increases to 83a0 0%, which
outperforms Johnson’s approach by 4%.
4.3 Discussion
The success of the trace detector is surprising, es-
pecially if compared to Johnson’s algorithm which
uses the output of a parser. The tagger can reliably
detect extraction sites without explicit knowledge of
the phrase structure. This shows that, in English, ex-
traction can only occur at well-defined sites, where
local cues are generally strong.
Indeed, the strength of the model lies in detecting
such sites (empty units, UNIT; NP traces, NP–NP)
or where clear-cut long-distance cues exist (WH–S,
COMP–SBAR). The accuracy of detecting uncon-
EE Prec. Rec. F-score
Here Here Here Johnson
LABELED 86.5% 72.9% 79.1% –
UNLABELED 93.3% 78.6% 85.3% –
NP–NP 87.8% 79.6% 83.5% –
WH–NP 92.5% 75.6% 83.2% 81.0%
PRO–NP 68.7% 70.4% 69.5% –
COMP–SBAR 93.8% 78.6% 85.5% 88.0%
UNIT 99.1% 92.5% 95.7% 92.0%
WH–S 94.4% 91.3% 92.8% 87.0%
WH–ADVP 81.6% 46.8% 59.5% 56.0%
CLAUSE 80.4% 68.3% 73.8% 70.0%
COMP–WHNP 67.2% 38.3% 48.8% 47.0%
Table 5: EE-detection results on Section 23 and com-
parison with Johnson (2002) (where applicable).
trolled PROs (PRO–NP) is rather low, since it is a dif-
ficult task to tell them apart from NP traces: they are
confused in 10 a0 15% of the cases. Furthermore, the
model is unable to capture for. . . to+INF construc-
tions if the noun-phrase is long.
The precision of detecting long-distance NP ex-
traction (WH–NP) is also high, but recall is lower:
in general, the model finds extracted NPs with
overt complementizers. Detection of null WH-
complementizers (COMP–WHNP), however, is fairly
inaccurate (48a0 8% F-score), since finding it and the
corresponding WH–NP requires information about
the transitivity of the verb. The performance of the
model is also low (59a0 5%) in detecting movement
sites for extracted WH-adverbs (WH–ADVP) despite
the presence of unambiguous cues (where, how, etc.
starting the subordinate clause). The difficulty of the
task lies in finding the correct verb-phrase as well
as the end of the verb-phrase the constituent is ex-
tracted from without knowing phrase boundaries.
One important limitation of the shallow approach
described here is its inability to find the antecedents
of the EEs, which clearly requires knowledge of
phrase structure. In the next section, we show
that the shallow trace detector and the unlexicalized
PCFG parser can be coupled to efficiently and suc-
cessfully tackle antecedent recovery.
Condition NOINSERT INSERT
Antecedent recovery
(F-score) 72a0 6% 69a0 3%
Parsing time (sec/sent) 2a0 7 25
Missed parses 2a0 4% 5a0 3%
Table 6: Antecedent recovery, parsing times, and
missed parses for the combined model
5 Combining the models
In Section 3, we found that parsing with EEs is only
feasible if the parser knows the location of EEs be-
fore parsing. In Section 4, we presented a finite-state
tagger which detects these sites before parsing takes
place. In this section, we validate the two-step ap-
proach, by applying the parser to the output of the
trace tagger, and comparing the antecedent recovery
accuracy to Johnson (2002).
5.1 Method
Theoretically, the ‘best’ way to combine the trace
tagger and the parsing algorithm would be to build a
unified probabilistic model. However, the nature of
the models are quite different: the finite-state model
is conditional, taking the words as given. The pars-
ing model, on the other hand, is generative, treat-
ing the words as an unlikely event. There is a rea-
sonable basis for building the probability models in
different ways. Most of the tags emitted by the EE
tagger are just EE=*, which would defeat genera-
tive models by making the ‘hidden’ state uninfor-
mative. Conditional parsing algorithms do exist, but
they are difficult to train using large corpora (John-
son, 2001). However, we show that it is quite ef-
fective if the parser simply treats the output of the
tagger as a certainty.
Given this combination method, there still are two
interesting variations: we may use only the EEs
proposed by the tagger (henceforth the NOINSERT
model), or we may allow the parser to insert even
more EEs (henceforth the INSERT model). In both
cases, EEs outputted by the tagger are treated as sep-
arate words, as in the PERFECT model of Section 3.
5.2 Results
The NOINSERT model did better at antecedent de-
tection (see Table 6) than the INSERT model. The
Type Prec. Rec. F-score
Here Here Here Johnson
OVERALL 80.5% 66.0% 72.6% 68.0%
NP–NP 71.2% 62.8% 66.8% 60.0%
WH–NP 91.6% 71.9% 80.6% 80.0%
PRO–NP 68.7% 70.4% 69.5% 50.0%
COMP–SBAR 93.8% 78.6% 85.5% 88.0%
UNIT 99.1% 92.5% 95.7% 92.0%
WH–S 86.7% 83.9% 84.8% 87.0%
WH–ADVP 67.1% 31.3% 42.7% 56.0%
CLAUSE 80.4% 68.3% 73.8% 70.0%
COMP–WHNP 67.2% 38.8% 48.8% 47.0%
Table 7: Antecedent recovery results for the
combined NOINSERT model and comparison with
Johnson (2002).
NOINSERT model was also faster, taking on aver-
age 2.7 seconds per sentence and enumerating about
160,000 edges whereas the INSERT model took 25
seconds on average and enumerated 2 million edges.
The coverage of the NOINSERT model was higher
than that of the INSERT model, missing 2.4% of all
parses versus 5.3% for the INSERT model.
Comparing our results to Johnson (2002), we find
that the NOINSERT model outperforms that of John-
son by 4.6% (see Table 7). The strength of this sys-
tem lies in its ability to tell unbound PROs and bound
NP–NP traces apart.
5.3 Discussion
Combining the finite-state tagger with the parser
seems to be invaluable for EE detection and an-
tecedent recovery. Paradoxically, taking the com-
bination to the extreme by allowing both the parser
and the tagger to insert EEs performed worse.
While the INSERT model here did have wider
coverage than the parser in Section 3, it seems the
real benefit of using the combined approach is to
let the simple model reduce the search space of
the more complicated parsing model. This search
space reduction works because the shallow finite-
state method takes information about adjacent words
into account, whereas the context-free parser does
not, since a phrase boundary might separate them.
6 Related Work
Excluding Johnson (2002)’s pattern-matching al-
gorithm, most recent work on finding head–
dependencies with statistical parser has used statis-
tical versions of deep grammar formalisms, such as
CCG (Clark et al., 2002) or LFG (Riezler et al.,
2002). While these systems should, in theory, be
able to handle discontinuities accurately, there has
not yet been a study on how these systems handle
such phenomena overall.
The tagger presented here is not the first one
proposed to recover syntactic information deeper
than part-of-speech tags. For example, supertag-
ging (Joshi and Bangalore, 1994) also aims to do
more meaningful syntactic pre-processing. Unlike
supertagging, our approach only focuses on detect-
ing EEs.
The idea of threading EEs to their antecedents in
a stochastic parser was proposed by Collins (1997),
following the GPSG tradition (Gazdar et al., 1985).
However, we extend it to capture all types of EEs.
7 Conclusions
This paper has three main contributions. First, we
show that gap+ features, encoding necessary infor-
mation for antecedent recovery, do not incur any
substantial computational overhead.
Second, the paper demonstrates that a shallow
finite-state model can be successful in detecting sites
for discontinuity, a task which is generally under-
stood to require deep syntactic and lexical-semantic
knowledge. The results show that, at least in En-
glish, local clues for discontinuity are abundant.
This opens up the possibility of employing shal-
low finite-state methods in novel situations to exploit
non-apparent local information.
Our final contribution, but the one we wish to em-
phasize the most, is that the combination of two or-
thogonal shallow models can be successful at solv-
ing tasks which are well beyond their individual
power. The accent here is on orthogonality – the two
models take different sources of information into ac-
count. The tagger makes good use of adjacency at
the word level, but is unable to handle deeper re-
cursive structures. A context-free grammar is better
at finding vertical phrase structure, but cannot ex-
ploit linear information when words are separated
by phrase boundaries. As a consequence, the finite-
state method helps the parser by efficiently and re-
liably pruning the search-space of the more compli-
cated PCFG model. The benefits are immediate: the
parser is not only faster but more accurate in recov-
ering antecedents. The real power of the finite-state
model is that it uses information the parser cannot.
Acknowledgements
The authors would like to thank Jason Baldridge,
Matthew Crocker, Geert-Jan Kruijff, Miles Osborne
and the anonymous reviewers for many helpful com-
ments.
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