Probabilistic Parsing for German using Sister-Head Dependencies
Amit Dubey
Department of Computational Linguistics
Saarland University
POBox151150
66041 Saarbr¨ucken, Germany
adubey@coli.uni-sb.de
Frank Keller
School of Informatics
University of Edinburgh
2 Buccleuch Place
Edinburgh EH8 9LW, UK
keller@inf.ed.ac.uk
Abstract
We present a probabilistic parsing model
for German trained on the Negra tree-
bank. We observe that existing lexicalized
parsing models using head-head depen-
dencies, while successful for English, fail
to outperform an unlexicalized baseline
model for German. Learning curves show
that this effect is not due to lack of training
data. We propose an alternative model that
uses sister-head dependencies instead of
head-head dependencies. This model out-
performs the baseline, achieving a labeled
precision and recall of up to 74%. This in-
dicates that sister-head dependencies are
more appropriate for treebanks with very
flat structures such as Negra.
1 Introduction
Treebank-based probabilistic parsing has been the
subject of intensive research over the past few years,
resulting in parsing models that achieve both broad
coverage and high parsing accuracy (e.g., Collins
1997; Charniak 2000). However, most of the ex-
isting models have been developed for English and
trained on the Penn Treebank (Marcus et al., 1993),
which raises the question whether these models
generalize to other languages, and to annotation
schemes that differ from the Penn Treebank markup.
The present paper addresses this question by
proposing a probabilistic parsing model trained on
Negra (Skut et al., 1997), a syntactically annotated
corpus for German. German has a number of syn-
tactic properties that set it apart from English, and
the Negra annotation scheme differs in important re-
spects from the Penn Treebank markup. While Ne-
gra has been used to build probabilistic chunkers
(Becker and Frank, 2002; Skut and Brants, 1998),
the research reported in this paper is the first attempt
to develop a probabilistic full parsing model for Ger-
man trained on a treebank (to our knowledge).
Lexicalization can increase parsing performance
dramatically for English (Carroll and Rooth, 1998;
Charniak, 1997, 2000; Collins, 1997), and the lexi-
calized model proposed by Collins (1997) has been
successfully applied to Czech (Collins et al., 1999)
and Chinese (Bikel and Chiang, 2000). However, the
resulting performance is significantly lower than the
performance of the same model for English (see Ta-
ble 1). Neither Collins et al. (1999) nor Bikel and
Chiang (2000) compare the lexicalized model to an
unlexicalized baseline model, leaving open the pos-
sibility that lexicalization is useful for English, but
not for other languages.
This paper is structured as follows. Section 2 re-
views the syntactic properties of German, focusing
on its semi-flexible wordorder. Section 3 describes
two standard lexicalized models (Carroll and Rooth,
1998; Collins, 1997), as well as an unlexicalized
baseline model. Section 4 presents a series of experi-
ments that compare the parsing performance of these
three models (and several variants) on Negra. The
results show that both lexicalized models fail to out-
perform the unlexicalized baseline. This is at odds
with what has been reported for English. Learning
curves show that the poor performance of the lexi-
calized models is not due to lack of training data.
Section 5 presents an error analysis for Collins’s
(1997) lexicalized model, which shows that the
head-head dependencies used in this model fail to
cope well with the flat structures in Negra. We pro-
pose an alternative model that uses sister-head de-
pendencies instead. This model outperforms the two
original lexicalized models, as well as the unlexical-
ized baseline. Based on this result and on the review
of the previous literature (Section 6), we argue (Sec-
tion 7) that sister-head models are more appropriate
for treebanks with very flat structures (such as Ne-
gra), typically used to annotate languages with semi-
free wordorder (such as German).
2 Parsing German
2.1 Syntactic Properties
German exhibits a number of syntactic properties
that distinguish it from English, the language that
has been the focus of most research in parsing.
Prominent among these properties is the semi-free
Language Size LR LP Source
English 40,000 87.4% 88.1% (Collins, 1997)
Chinese 3,484 69.0% 74.8% (Bikel and Chiang, 2000)
Czech 19,000 —- 80.0% —- (Collins et al., 1999)
Table 1: Results for the Collins (1997) model for
various languages (dependency precision for Czech)
wordorder, i.e., German wordorder is fixed in some
respects, but variable in others. Verb order is largely
fixed: in subordinate clauses such as (1a), both the
finite verb hat ‘has’ and the non-finite verb kom-
poniert ‘composed’ are in sentence final position.
(1) a. Weil
because
er
er
gestern
yesterday
Musik
music
komponiert
composed
hat.
has
‘Because he has composed music yesterday.’
b. Hat er gestern Musik komponiert?
c. Er hat gestern Musik komponiert.
In yes/no questions such as (1b), the finite verb is
sentence initial, while the non-finite verb is sen-
tence final. In declarative main clauses (see (1c)), on
the other hand, the finite verb is in second position
(i.e., preceded by exactly one constituent), while the
non-finite verb is final.
While verb order is fixed in German, the order
of complements and adjuncts is variable, and influ-
enced by a variety of syntactic and non-syntactic
factors, including pronominalization, information
structure, definiteness, and animacy (e.g., Uszkor-
eit 1987). The first position in a declarative sen-
tence, for example, can be occupied by various con-
stituents, including the subject (er ‘he’ in (1c)), the
object (Musik ‘music’ in (2a)), an adjunct (gestern
‘yesterday’ in (2b)), or the non-finite verb (kom-
poniert ‘composed’ in (2c)).
(2) a. Musik hat er gestern komponiert.
b. Gestern hat er Musik komponiert .
c. Komponiert hat er gestern Musik.
The semi-free wordorder in German means that a
context-free grammar model has to contain more
rules than for a fixed wordorder language. For tran-
sitive verbs, for instance, we need the rules S !
VNPNP,S! NP V NP, and S ! NP NP V to
account for verb initial, verb second, and verb final
order (assuming a flat S, see Section 2.2).
2.2 Negra Annotation Scheme
The Negra corpus consists of around 350,000 words
of German newspaper text (20,602 sentences). The
annotation scheme (Skut et al., 1997) is modeled to a
certain extent on that of the Penn Treebank (Marcus
et al., 1993), with crucial differences. Most impor-
tantly, Negra follows the dependency grammar tra-
dition in assuming flat syntactic representations:
(a) There is no S ! NP VP rule. Rather, the sub-
ject, the verb, and its objects are all sisters of each
other, dominated by an S node. This is a way of
accounting for the semi-free wordorder of German
(see Section 2.1): the first NP within an S need not
be the subject.
(b) There is no SBAR ! Comp S rule. Main
clauses, subordinate clauses, and relative clauses all
share the category S in Negra; complementizers and
relative pronouns are simply sisters of the verb.
(c) There is no PP ! P NP rule, i.e., the prepo-
sition and the noun it selects (and determiners and
adjectives, if present) are sisters, dominated by a
PP node. An argument for this representation is that
prepositions behave like case markers in German; a
preposition and a determiner can merge into a single
word (e.g., in dem ‘in the’ becomes im).
Another idiosyncrasy of Negra is that it assumes
special coordinate categories. A coordinated sen-
tence has the category CS, a coordinate NP has the
category CNP, etc. While this does not make the
annotation more flat, it substantially increases the
number of non-terminal labels. Negra also contains
grammatical function labels that augment phrasal
and lexical categories. Example are MO (modifier),
HD (head), SB (subject), and OC (clausal object).
3 Probabilistic Parsing Models
3.1 Probabilistic Context-Free Grammars
Lexicalization has been shown to improve pars-
ing performance for the Penn Treebank (e.g., Car-
roll and Rooth 1998; Charniak 1997, 2000; Collins
1997). The aim of the present paper is to test if this
finding carries over to German and to the Negra cor-
pus. We therefore use an unlexicalized model as our
baseline against which to test the lexicalized models.
More specifically, we used a standard proba-
bilistic context-free grammar (PCFG; see Charniak
1993). Each context-free rule RHS ! LHS is anno-
tated with an expansion probability P(RHSjLHS).
The probabilities for all rules with the same lefthand
side have to sum to one, and the probability of a
parse tree T is defined as the product of the prob-
abilities of all rules applied in generating T .
3.2 Carroll and Rooth’s Head-Lexicalized
Model
The head-lexicalized PCFG model of Carroll and
Rooth (1998) is a minimal departure from the stan-
dard unlexicalized PCFG model, which makes it
ideal for a direct comparison.
1
A grammar rule LHS ! RHS can be written as
P ! C
1
:::C
n
,whereP is the mother category, and
C
1
:::C
n
are daughters. Let l(C) be the lexical head
1
Charniak (1997) proposes essentially the same model; we
will nevertheless use the label ‘Carroll and Rooth model’ as we
are using their implementation (see Section 4.1).
of the constituent C. The rule probability is then de-
fined as (see also Beil et al. 2002):
P(RHSjLHS)=P
rule
(C
1
:::C
n
jP;l(P))(3)
 
n
∏
i=1
P
choice
(l(C
i
)jC
i
;P;l(P))
Here P
rule
(C
1
:::C
n
jP;l(P)) is the probability that
category P with lexical head l(P) is expanded by the
rule P ! C
1
:::C
n
,andP
choice
(l(C)jC;P;l(P)) is the
probability that the (non-head) category C has the
lexical head l(C) given that its mother is P with lex-
ical head l(P).
3.3 Collins’s Head-Lexicalized Model
In contrast to Carroll and Rooth’s (1998) approach,
the model proposed by Collins (1997) does not com-
pute rule probabilities directly. Rather, they are gen-
erated using a Markov process that makes certain in-
dependence assumptions. A grammar rule LHS !
RHS can be written as P ! L
m
:::L
1
HR
1
:::R
n
where P is the mother and H is the head daughter.
Let l(C) be the head word of C and t(C) the tag of
the head word of C. Then the probability of a rule is
defined as:
P(RHSjLHS)=P(L
m
:::L
1
HR
1
:::R
n
jP)(4)
= P
h
(HjP)P
l
(L
m
:::L
1
jP;H)P
r
(R
1
:::R
n
jP;H)
= P
h
(HjP)
m
∏
i=0
P
l
(L
i
jP;H;d(i))
n
∏
i=0
P
r
(R
i
jP;H;d(i))
Here, P
h
is the probability of generating the head,
and P
l
and P
r
are the probabilities of generating the
nonterminals to the left and right of the head, re-
spectively; d(i) is a distance measure. (L
0
and R
0
are
stop categories.) At this point, the model is still un-
lexicalized. To add lexical sensitivity, the P
h
, P
r
and
P
l
probability functions also take into account head
words and their POS tags:
P(RHSjLHS)=P
h
(HjP;t(P);l(P))(5)
 
m
∏
i=0
P
l
(L
i
;t(L
i
);l(L
i
)jP;H;t(H);l(H);d(i))
 
n
∏
i=0
P
r
(R
i
;t(R
i
);l(R
i
)jP;H;t(H);l(H);d(i))
4 Experiment 1
This experiment was designed to compare the per-
formance of the three models introduced in the
last section. Our main hypothesis was that the lex-
icalized models will outperform the unlexicalized
baseline model. Another prediction was that adding
Negra-specific information to the models will in-
crease parsing performance. We therefore tested a
model variant that included grammatical function la-
bels, i.e., the set of categories was augmented by the
function tags specified in Negra (see Section 2.2).
Adding grammatical functions is a way of deal-
ing with the wordorder facts of German (see Sec-
tion 2.1) in the face of Negra’s very flat annota-
tion scheme. For instance, subject and object NPs
have different wordorder preferences (subjects tend
to be preverbal, while objects tend to be postver-
bal), a fact that is captured if subjects have the la-
bel NP-SB, while objects are labeled NP-OA (ac-
cusative object), NP-DA (dative object), etc. Also
the fact that verb order differs between subordinate
and main clauses is captured by the function labels:
the former are labeled S, while the latter are labeled
S-OC (object clause), S-RC (relative clause), etc.
Another idiosyncrasy of the Negra annotation is
that conjoined categories have separate labels (S and
CS, NP and CNP, etc.), and that PPs do not contain
an NP node. We tested a variant of the Carroll and
Rooth (1998) model that takes this into account.
4.1 Method
Data Sets All experiments reported in this paper
used the treebank format of Negra. This format,
which is included in the Negra distribution, was de-
rived from the native format by replacing crossing
branches with traces. We split the corpus into three
subsets. The first 18,602 sentences constituted the
training set. Of the remaining 2,000 sentences, the
first 1,000 served as the test set, and the last 1000 as
the development set. To increase parsing efficiency,
we removed all sentences with more than 40 words.
This resulted in a test set of 968 sentences and a
development set of 975 sentences. Early versions
of the models were tested on the development set,
and the test set remained unseen until all parameters
were fixed. The final results reported this paper were
obtained on the test set, unless stated otherwise.
Grammar Induction For the unlexicalized PCFG
model (henceforth baseline model), we used the
probabilistic left-corner parser Lopar (Schmid,
2000). When run in unlexicalized mode, Lopar im-
plements the model described in Section 3.1. A
grammar and a lexicon for Lopar were read off the
Negra training set, after removing all grammatical
function labels. As Lopar cannot handle traces, these
were also removed from the training data.
The head-lexicalized model of Carroll and Rooth
(1998) (henceforth C&R model) was again realized
using Lopar, which in lexicalized mode implements
the model in Section 3.2. Lexicalization requires that
each rule in a grammar has one of the categories on
its righthand side annotated as the head. For the cate-
gories S, VP, AP, and AVP, the head is marked in Ne-
gra. For the other categories, we used rules to heuris-
tically determine the head, as is standard practice for
the Penn Treebank.
The lexicalized model proposed by Collins (1997)
(henceforth Collins model) was re-implemented by
one of the authors. For training, empty categories
were removed from the training data, as the model
cannot handle them. The same head finding strategy
was applied as for the C&R model.
In this experiment, only head-head statistics were
used (see (5)). The original Collins model uses
sister-head statistics for non-recursive NPs. This will
be discussed in detail in Section 5.
Training and Testing For all three models, the
model parameters were estimated using maximum
likelihood estimation. Both Lopar and the Collins
model use various backoff distributions to smooth
the estimates. The reader is referred to Schmid
(2000) and Collins (1997) for details. For the C&R
model, we used a cutoff of one for rule frequencies
P
rule
and lexical choice frequencies P
choice
(the cutoff
value was optimized on the development set).
We also tested variants of the baseline model and
the C&R model that include grammatical function
information, as we hypothesized that this informa-
tion might help the model to handle wordorder vari-
ation more adequately, as explained above.
Finally, we tested variant of the C&R model that
uses Lopar’s parameter pooling feature. This fea-
ture makes it possible to collapse the lexical choice
distribution P
choice
for either the daughter or the
mother categories of a rule (see Section 3.2). We
pooled the estimates for pairs of conjoined and non-
conjoined daughter categories (S and CS, NP and
CNP, etc.): these categories should be treated as the
same daughters; e.g., there should be no difference
between S !NP V and S!CNP V. We also pooled
the estimates for the mother categories NPs and PPs.
This is a way of dealing with the fact that there is no
separate NP node within PPs in Negra.
Lopar and the Collins model differ in their han-
dling of unknown words. In Lopar, a POS tag distri-
bution for unknown words has to be specified, which
is then used to tag unknown words in the test data.
The Collins model treats any word seen fewer than
five times in the training data as unseen and uses an
external POS tagger to tag unknown words. In order
to make the models comparable, we used a uniform
approach to unknown words. All models were run
on POS-tagged input; this input was created by tag-
ging the test set with a separate POS tagger, for both
known and unknown words. We used TnT (Brants,
2000), trained on the Negra training set. The tagging
accuracy was 97.12% on the development set.
In order to obtain an upper bound for the perfor-
mance of the parsing models, we also ran the parsers
on the test set with the correct tags (as specified in
Negra), again for both known and unknown words.
We will refer to this mode as ‘perfect tagging’.
All models were evaluated using standard PAR-
SEVAL measures. We report labeled recall (LR)
labeled precision (LP), average crossing brackets
(CBs), zero crossing brackets (0CB), and two or less
crossing brackets ( 2CB). We also give the cover-
age (Cov), i.e., the percentage of sentences that the
parser was able to parse.
4.2 Results
The results for all three models and their variants
are given in Table 2, for both TnT tags and per-
fect tags. The baseline model achieves 70.56% LR
and 66.69% LP with TnT tags. Adding grammatical
functions reduces both figures slightly, and cover-
age drops by about 15%. The C&R model performs
worse than the baseline, at 68.04% LR and 60.07%
LP (for TnT tags). Adding grammatical function
again reduces performance slightly. Parameter pool-
ing increases both LR and LP by about 1%. The
Collins models also performs worse than the base-
line, at 67.91% LR and 66.07% LP.
Performance using perfect tags (an upper bound
of model performance) is 2–3% higher for the base-
line and for the C&R model. The Collins model
gains only about 1%. Perfect tagging results in a per-
formance increase of over 10% for the models with
grammatical functions. This is not surprising, as the
perfect tags (but not the TnT tags) include grammat-
ical function labels. However, we also observe a dra-
matic reduction in coverage (to about 65%).
4.3 Discussion
We added grammatical functions to both the base-
line model and the C&R model, as we predicted
that this would allow the model to better capture the
wordorder facts of German. However, this predic-
tion was not borne out: performance with grammat-
ical functions (on TnT tags) was slightly worse than
without, and coverage dropped substantially. A pos-
sible reason for this is sparse data: a grammar aug-
mented with grammatical functions contains many
additional categories, which means that many more
parameters have to be estimated using the same
training set. On the other hand, a performance in-
crease occurs if the tagger also provides grammati-
cal function labels (simulated in the perfect tags con-
dition). However, this comes at the price of an unac-
ceptable reduction in coverage.
When training the C&R model, we included a
variant that makes use of Lopar’s parameter pool-
ing feature. We pooled the estimates for conjoined
daughter categories, and for NP and PP mother cat-
egories. This is a way of taking the idiosyncrasies of
the Negra annotation into account, and resulted in a
small improvement in performance.
The most surprising finding is that the best per-
formance was achieved by the unlexicalized PCFG
TnT tagging Perfect tagging
LR LP CBs 0CB  2CB Cov LR LP CBs 0CB  2CB Cov
Baseline 70.56 66.69 1.03 58.21 84.46 94.42 72.99 70.00 0.88 60.30 87.42 95.25
Baseline + GF 70.45 65.49 1.07 58.02 85.01 79.24 81.14 78.37 0.46 74.25 95.26 65.39
C&R 68.04 60.07 1.31 52.08 79.54 94.42 70.79 63.38 1.17 54.99 82.21 95.25
C&R + pool 69.07 61.41 1.28 53.06 80.09 94.42 71.74 64.73 1.11 56.40 83.08 95.25
C&R + GF 67.66 60.33 1.31 55.67 80.18 79.24 81.17 76.83 0.48 73.46 94.15 65.39
Collins 67.91 66.07 0.73 65.67 89.52 95.21 68.63 66.94 0.71 64.97 89.73 96.23
Table 2: Results for Experiment 1: comparison of lexicalized and unlexicalized models (GF: grammatical
functions; pool: parameter pooling for NPs/PPs and conjoined categories)
0 2040608010
percent of training corpus
45
50
55
60
65
70
75
f-score
unlexicalized PCFG
lexicalized PCFG (Collins)
lexicalized PCFG (C&R)
Figure 1: Learning curves for all three models
baseline model. Both lexicalized models (C&R and
Collins) performed worse than the baseline. This re-
sults is at odds with what has been found for En-
glish, where lexicalization is standardly reported to
increase performance by about 10%. The poor per-
formance of the lexicalized models could be due to
a lack of sufficient training data: our Negra training
set contains approximately 18,000 sentences, and is
therefore significantly smaller than the Penn Tree-
bank training set (about 40,000 sentences). Negra
sentences are also shorter: they contain, on average,
15 words compared to 22 in the Penn Treebank.
We computed learning curves for the unmodified
variants (without grammatical functions or parame-
ter pooling) of all three models (on the development
set). The result (see Figure 1) shows that there is no
evidence for an effect of sparse data. For both the
baseline and the C&R model, a fairly high f-score
is achieved with only 10% of the training data. A
slow increase occurs as more training data is added.
The performance of the Collins model is even less
affected by training set size. This is probably due to
the fact that it does not use rule probabilities directly,
but generates rules using a Markov chain.
5 Experiment 2
As we saw in the last section, lack of training data is
not a plausible explanation for the sub-baseline per-
formance of the lexicalized models. In this experi-
ment, we therefore investigate an alternative hypoth-
esis, viz., that the lexicalized models do not cope
Penn Negra
NP 2.20 3.08
PP 2.03 2.66
Penn Negra
VP 2.32 2.59
S 2.22 4.22
Table 3: Average number of daughters for the gram-
matical categories in the Penn Treebank and Negra
well with the fact that Negra rules are so flat (see
Section 2.2). We will focus on the Collins model, as
it outperformed the C&R model in Experiment 1.
An error analysis revealed that many of the errors
of the Collins model in Experiment 1 are chunking
errors. For example, the PP neben den Mitteln des
Theaters should be analyzed as (6a). But instead the
parser produces two constituents as in (6b)):
(6) a. [PP neben
apart
den
the
Mitteln
means
[NP des
the
Theaters]]
theater’s
‘apart from the means of the theater’.
b. [PP neben den Mitteln] [NP des Theaters]
The reason for this problem is that neben is the head
of the constituent in (6), and the Collins model uses
a crude distance measure together with head-head
dependencies to decide if additional constituents
should be added to the PP. The distance measure is
inadequate for finding PPs with high precision.
The chunking problem is more widespread than
PPs. The error analysis shows that other con-
stituents, including Ss and VPs, also have the wrong
boundary. This problem is compounded by the fact
that the rules in Negra are substantially flatter than
the rules in the Penn Treebank, for which the Collins
model was developed. Table 3 compares the average
number of daughters in both corpora.
The flatness of PPs is easy to reduce. As detailed
in Section 2.2, PPs lack an intermediate NP projec-
tion, which can be inserted straightforwardly using
the following rule:
(7) [PPP...]! [PPP[NP...]]
In the present experiment, we investigated if parsing
performance improves if we test and train on a ver-
sion of Negra on which the transformation in (7) has
been applied.
In a second series of experiments, we investigated
a more general way of dealing with the flatness of
C&R Collins Charniak Current
Head sister category X X X
Head sister head word X X X
Head sister head tag X X
Prev. sister category X X X
Prev. sister head word X
Prev. sister head tag X
Table 4: Linguistic features in the current model
compared to the models of Carroll and Rooth
(1998), Collins (1997), and Charniak (2000)
Negra, based on Collins’s (1997) model for non-
recursive NPs in the Penn Treebank (which are also
flat). For non-recursive NPs, Collins (1997) does not
use the probability function in (5), but instead sub-
stitutes P
r
(and, by analogy, P
l
) by:
P
r
(R
i
;t(R
i
);l(R
i
)jP;R
i−1
;t(R
i−1
);l(R
i−1
);d(i))(8)
Here the head H is substituted by the sister R
i−1
(and L
i−1
). In the literature, the version of P
r
in (5)
is said to capture head-head relationships. We will
refer to the alternative model in (8) as capturing
sister-head relationships.
Using sister-head relationships is a way of coun-
teracting the flatness of the grammar productions;
it implicitly adds binary branching to the grammar.
Our proposal is to extend the use of sister-head re-
lationship from non-recursive NPs (as proposed by
Collins) to all categories.
Table 4 shows the linguistic features of the result-
ing model compared to the models of Carroll and
Rooth (1998), Collins (1997), and Charniak (2000).
The C&R model effectively includes category infor-
mation about all previous sisters, as it uses context-
free rules. The Collins (1997) model does not use
context-free rules, but generates the next category
using zeroth order Markov chains (see Section 3.3),
hence no information about the previous sisters is
included. Charniak’s (2000) model extends this to
higher order Markov chains (first to third order), and
therefore includes category information about previ-
ous sisters.The current model differs from all these
proposals: it does not use any information about the
head sister, but instead includes the category, head
word, and head tag of the previous sister, effectively
treating it as the head.
5.1 Method
We first trained the original Collins model on a mod-
ified versions of the training test from Experiment 1
in which the PPs were split by applying rule (7).
In a second series of experiments, we tested a
range of models that use sister-head dependencies
instead of head-head dependencies for different cat-
egories. We first added sister-head dependencies for
NPs (following Collins’s (1997) original proposal)
and then for PPs, which are flat in Negra, and thus
similar in structure to NPs (see Section 2.2). Then
we tested a model in which sister-head relationships
are applied to all categories.
In a third series of experiments, we trained mod-
els that use sister-head relationships everywhere ex-
cept for one category. This makes it possible to de-
termine which sister-head dependencies are crucial
for improving performance of the model.
5.2 Results
The results of the PP experiment are listed in Ta-
ble 5. Again, we give results obtained using TnT tags
and using perfect tags. The row ‘Split PP’ contains
the performance figures obtained by including split
PPs in both the training and in the testing set. This
leads to a substantial increase in LR (6–7%) and LP
(around 8%) for both tagging schemes. Note, how-
ever, that these figures are not directly comparable to
the performance of the unmodified Collins model: it
is possible that the additional brackets artificially in-
flate LR and LP. Presumably, the brackets for split
PPs are easy to detect, as they are always adjacent to
a preposition. An honest evaluation should therefore
train on the modified training set (with split PPs),
but collapse the split categories for testing, i.e., test
on the unmodified test set. The results for this evalu-
ation are listed in rows ‘Collapsed PP’. Now there is
no increase in performance compared to the unmod-
ified Collins model; rather, a slight drop in LR and
LP is observed.
Table 5 also displays the results of our exper-
iments with the sister-head model. For TnT tags,
we observe that using sister-head dependencies for
NPs leads to a small decrease in performance com-
pared to the unmodified Collins model, resulting in
67.84% LR and 65.96% LP. Sister-head dependen-
cies for PPs, however, increase performance sub-
stantially to 70.27% LR and 68.45% LP. The high-
est improvement is observed if head-sister depen-
dencies are used for all categories; this results in
71.32% LR and 70.93% LP, which corresponds to an
improvement of 3% in LP and 5% in LR compared
to the unmodified Collins model. Performance with
perfect tags is around 2–4% higher than with TnT
tags. For perfect tags, sister-head dependencies lead
to an improvement for NPs, PPs, and all categories.
The third series of experiments was designed to
determine which categories are crucial for achiev-
ing this performance gain. This was done by train-
ing models that use sister-head dependencies for all
categories but one. Table 6 shows the change in LR
and LP that was found for each individual category
(again for TnT tags and perfect tags). The highest
drop in performance (around 3%) is observed when
the PP category is reverted to head-head dependen-
cies. For S and for the coordinated categories (CS,
TnT tagging Perfect tagging
LR LP CBs 0CB  2CB Cov LR LP CBs 0CB  2CB Cov
Unmod. Collins 67.91 66.07 0.73 65.67 89.52 95.21 68.63 66.94 0.71 64.97 89.73 96.23
Split PP 73.84 73.77 0.82 62.89 88.98 95.11 75.93 75.27 0.77 65.36 89.03 93.79
Collapsed PP 66.45 66.07 0.89 66.60 87.04 95.11 68.22 67.32 0.94 66.67 85.88 93.79
Sister-head NP 67.84 65.96 0.75 65.85 88.97 95.11 71.54 70.31 0.60 68.03 93.33 94.60
Sister-head PP 70.27 68.45 0.69 66.27 90.33 94.81 73.20 72.44 0.60 68.53 93.21 94.50
Sister-head all 71.32 70.93 0.61 69.53 91.72 95.92 73.93 74.24 0.54 72.30 93.47 95.21
Table 5: Results for Experiment 2: performance for models using split phrases and sister-head dependencies
CNP, etc.), a drop in performance of around 1% each
is observed. A slight drop is observed also for VP
(around 0.5%). Only minimal fluctuations in perfor-
mance are observed when the other categories are
removed (AP, AVP, and NP): there is a small effect
(around 0.5%) if TnT tags are used, and almost no
effect for perfect tags.
5.3 Discussion
We showed that splitting PPs to make Negra less
flat does not improve parsing performance if test-
ing is carried out on the collapsed categories. How-
ever, we observed that LR and LP are artificially in-
flated if split PPs are used for testing. This finding
goes some way towards explaining why the parsing
performance reported for the Penn Treebank is sub-
stantially higher than the results for Negra: the Penn
Treebank contains split PPs, which means that there
are lot of brackets that are easy to get right. The re-
sulting performance figures are not directly compa-
rable to figures obtained on Negra, or other corpora
with flat PPs.
2
We also obtained a positive result: we demon-
strated that a sister-head model outperforms the un-
lexicalized baseline model (unlike the C&R model
and the Collins model in Experiment 1). LR was
about 1% higher and LP about 4% higher than the
baseline if lexical sister-head dependencies are used
for all categories. This holds both for TnT tags and
for perfect tags (compare Tables 2 and 5). We also
found that using lexical sister-head dependencies for
all categories leads to a larger improvement than us-
ing them only for NPs or PPs (see Table 5). This
result was confirmed by a second series of experi-
ments, where we reverted individual categories back
to head-head dependencies, which triggered a de-
crease in performance for all categories, with the ex-
ception of NP, AP, and AVP (see Table 6).
On the whole, the results of Experiment 2 are at
odds with what is known about parsing for English.
The progression in the probabilistic parsing litera-
ture has been to start with lexical head-head depen-
dencies (Collins, 1997) and then add non-lexical sis-
2
This result generalizes to Ss, which are also flat in Negra
(see Section 2.2). We conducted an experiment in which we
added an SBAR above the S. No increase in performance was
obtained if the evaluation was carried using collapsed Ss.
TnT tagging Perfect tagging
∆LR ∆LP ∆LR ∆LP
PP −3.45 −1.60 −4.21 −3.35
S −1.28 0.11 −2.23 −1.22
Coord −1.87 −0.39 −1.54 −0.80
VP −0.72 0.18 −0.58 −0.30
AP −0.57 0.10 0.08 −0.07
AV P −0.32 0.44 0.10 0.11
NP 0.06 0.78 −0.15 0.02
Table 6: Change in performance when reverting to
head-head statistics for individual categories
ter information (Charniak, 2000), as illustrated in
Table 4. Lexical sister-head dependencies have only
been found useful in a limited way: in the original
Collins model, they are used for non-recursive NPs.
Our results show, however, that for parsing Ger-
man, lexical sister-head information is more im-
portant than lexical head-head information. Only a
model that replaced lexical head-head with lexical
sister-head dependencies was able to outperform a
baseline model that uses no lexicalization.
3
Based
on the error analysis for Experiment 1, we claim that
the reason for the success of the sister-head model is
the fact that the rules in Negra are so flat; using a
sister-head model is a way of binarizing the rules.
6 Comparison with Previous Work
There are currently no probabilistic, treebank-
trained parsers available for German (to our knowl-
edge). A number of chunking models have been pro-
posed, however. Skut and Brants (1998) used Ne-
gra to train a maximum entropy-based chunker, and
report LR and LP of 84.4% for NP and PP chunk-
ing. Using cascaded Markov models, Brants (2000)
reports an improved performance on the same task
(LR 84.4%, LP 88.3%). Becker and Frank (2002)
train an unlexicalized PCFG on Negra to perform
a different chunking task, viz., the identification of
topological fields (sentence-based chunks). They re-
port an LR and LP of 93%.
The head-lexicalized model of Carroll and Rooth
(1998) has been applied to German by Beil et al.
3
It is unclear what effect bi-lexical statistics have on the
sister-head model; while Gildea (2001) shows bi-lexical statis-
tics are sparse for some grammars, Hockenmaier and Steedman
(2002) found they play a greater role in binarized grammars.
(1999, 2002). However, this approach differs in the
number of ways from the results reported here: (a) a
hand-written grammar (instead of a treebank gram-
mar) is used; (b) training is carried out on unan-
notated data; (c) the grammar and the training set
cover only subordinate and relative clauses, not un-
restricted text. Beil et al. (2002) report an evaluation
using an NP chunking task, achieving 92% LR and
LP. They also report the results of a task-based eval-
uation (extraction of sucategorization frames).
There is some research on treebank-based pars-
ing of languages other than English. The work by
Collins et al. (1999) and Bikel and Chiang (2000)
has demonstrated the applicability of the Collins
(1997) model for Czech and Chinese. The perfor-
mance reported by these authors is substantially
lower than the one reported for English, which might
be due to the fact that less training data is avail-
able for Czech and Chinese (see Table 1). This hy-
pothesis cannot be tested, as the authors do not
present learning curves for their models. However,
the learning curve for Negra (see Figure 1) indicates
that the performance of the Collins (1997) model
is stable, even for small training sets. Collins et al.
(1999) and Bikel and Chiang (2000) do not compare
their models with an unlexicalized baseline; hence
it is unclear if lexicalization really improves parsing
performance for these languages. As Experiment 1
showed, this cannot be taken for granted.
7 Conclusions
We presented the first probabilistic full parsing
model for German trained on Negra, a syntactically
annotated corpus. This model uses lexical sister-
head dependencies, which makes it particularly suit-
able for parsing Negra’s flat structures. The flatness
of the Negra annotation reflects the syntactic proper-
ties of German, in particular its semi-free wordorder.
In Experiment 1, we applied three standard pars-
ing models from the literature to Negra: an un-
lexicalized PCFG model (the baseline), Carroll
and Rooth’s (1998) head-lexicalized model, and
Collins’s (1997) model based on head-head depen-
dencies. The results show that the baseline model
achieves a performance of up to 73% recall and 70%
precision. Both lexicalized models perform substan-
tially worse. This finding is at odds with what has
been reported for parsing models trained on the Penn
Treebank. As a possible explanation we considered
lack of training data: Negra is about half the size of
the Penn Treebank. However, the learning curves for
the three models failed to produce any evidence that
they suffer from sparse data.
In Experiment 2, we therefore investigated an al-
ternative hypothesis: the poor performance of the
lexicalized models is due to the fact that the rules in
Negra are flatter than in the Penn Treebank, which
makes lexical head-head dependencies less useful
for correctly determining constituent boundaries.
Based on this assumption, we proposed an alterna-
tive model hat replaces lexical head-head dependen-
cies with lexical sister-head dependencies. This can
the thought of as a way of binarizing the flat rules in
Negra. The results show that sister-head dependen-
cies improve parsing performance not only for NPs
(which is well-known for English), but also for PPs,
VPs, Ss, and coordinate categories. The best perfor-
mance was obtained for a model that uses sister-head
dependencies for all categories. This model achieves
up to 74% recall and precision, thus outperforming
the unlexicalized baseline model.
It can be hypothesized that this finding carries
over to other treebanks that are annotated with flat
structures. Such annotation schemes are often used
for languages that (unlike English) have a free or
semi-free wordorder. Testing our sister-head model
on these languages is a topic for future research.
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