Semantic Role Labeling for Coreference Resolution
Simone Paolo Ponzetto and Michael Strube
EML Research gGmbH
Schloss-Wolfsbrunnenweg 33
69118 Heidelberg, Germany
http://www.eml-research.de/nlp/
Abstract
Extending a machine learning based coref-
erence resolution system with a feature
capturing automatically generated infor-
mation about semantic roles improves its
performance.
1 Introduction
The last years have seen a boost of work devoted
to the development of machine learning based
coreference resolution systems (Soon et al., 2001;
Ng & Cardie, 2002; Kehler et al., 2004, inter alia).
Similarly, many researchers have explored tech-
niques for robust, broad coverage semantic pars-
ing in terms of semantic role labeling (Gildea &
Jurafsky, 2002; Carreras & Ma`rquez, 2005, SRL
henceforth).
This paper explores whether coreference reso-
lution can benefit from SRL, more specifically,
which phenomena are affected by such informa-
tion. The motivation comes from the fact that cur-
rent coreference resolution systems are mostly re-
lying on rather shallow features, such as the dis-
tance between the coreferent expressions, string
matching, and linguistic form. On the other hand,
the literature emphasizes since the very begin-
ning the relevance of world knowledge and infer-
ence (Charniak, 1973). As an example, consider
a sentence from the Automatic Content Extraction
(ACE) 2003 data.
(1) A state commission of inquiry into the sinking of the
Kursk will convene in Moscow on Wednesday, the
Interfax news agency reported. It said that the diving
operation will be completed by the end of next week.
It seems that in this example, knowing that the In-
terfax news agency is the AGENT of the report
predicate, and It being the AGENT of say, could
trigger the (semantic parallelism based) inference
required to correctly link the two expressions, in
contrast to anchoring the pronoun to Moscow.
SRL provides the semantic relationships that
constituents have with predicates, thus allowing
us to include document-level event descriptive in-
formation into the relations holding between re-
ferring expressions (REs). This layer of semantic
context abstracts from the specific lexical expres-
sions used, and therefore represents a higher level
of abstraction than predicate argument statistics
(Kehler et al., 2004) and Latent Semantic Analy-
sis used as a model of world knowledge (Klebanov
& Wiemer-Hastings, 2002). In this respect, the
present work is closer in spirit to Ji et al. (2005),
who explore the employment of the ACE 2004 re-
lation ontology as a semantic filter.
2 Coreference Resolution Using SRL
2.1 Corpora Used
The system was initially prototyped using the
MUC-6 and MUC-7 data sets (Chinchor & Sund-
heim, 2003; Chinchor, 2001), using the standard
partitioning of 30 texts for training and 20-30 texts
for testing. Then, we developed and tested the
system with the ACE 2003 Training Data cor-
pus (Mitchell et al., 2003)1. Both the Newswire
(NWIRE) and Broadcast News (BNEWS) sections
where split into 60-20-20% document-based par-
titions for training, development, and testing, and
later per-partition merged (MERGED) for system
evaluation. The distribution of coreference chains
and referring expressions is given in Table 1.
2.2 Learning Algorithm
For learning coreference decisions, we used a
Maximum Entropy (Berger et al., 1996) model.
Coreference resolution is viewed as a binary clas-
sification task: given a pair of REs, the classifier
has to decide whether they are coreferent or not.
First, a set of pre-processing components includ-
1We used the training data corpus only, as the availability
of the test data was restricted to ACE participants.
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BNEWS NWIRE
#coref ch. #pron. #comm. nouns #prop. names #coref ch. #pron. #comm. nouns #prop. names
TRAIN. 587 876 572 980 904 1037 1210 2023
DEVEL 201 315 163 465 399 358 485 923
TEST 228 291 238 420 354 329 484 712
Table 1: Partitions of the ACE 2003 training data corpus
ing a chunker and a named entity recognizer is
applied to the text in order to identify the noun
phrases, which are further taken as REs to be used
for instance generation. Instances are created fol-
lowing Soon et al. (2001). During testing the
classifier imposes a partitioning on the available
REs by clustering each set of expressions labeled
as coreferent into the same coreference chain.
2.3 Baseline System Features
Following Ng & Cardie (2002), our baseline sys-
tem reimplements the Soon et al. (2001) system.
The system uses 12 features. Given a pair of can-
didate referring expressions REi and REj the fea-
tures are computed as follows2.
(a) Lexical features
STRING MATCH T if REi and REj have the
same spelling, else F.
ALIAS T if one RE is an alias of the other; else
F.
(b) Grammatical features
I PRONOUN T if REi is a pronoun; else F.
J PRONOUN T if REj is a pronoun; else F.
J DEF T if REj starts with the; else F.
J DEM T if REj starts with this, that, these, or
those; else F.
NUMBER T if both REi and REj agree in num-
ber; else F.
GENDER U if REi or REj have an undefined
gender. Else if they are both defined and agree
T; else F.
PROPER NAME T if both REi and REj are
proper names; else F.
APPOSITIVE T if REj is in apposition with
REi; else F.
(c) Semantic features
WN CLASS U if REi or REj have an undefined
WordNet semantic class. Else if they both have
a defined one and it is the same T; else F.
2Possible values are U(nknown), T(rue) and F(alse). Note
that in contrast to Ng & Cardie (2002) we classify ALIAS as
a lexical feature, as it solely relies on string comparison and
acronym string matching.
(d) Distance features
DISTANCE how many sentences REi and REj
are apart.
2.4 Semantic Role Features
The baseline system employs only a limited
amount of semantic knowledge. In particular, se-
mantic information is limited to WordNet seman-
tic class matching. Unfortunately, a simple Word-
Net semantic class lookup exhibits problems such
as coverage and sense disambiguation3, which
make the WN CLASS feature very noisy. As a
consequence, we propose in the following to en-
rich the semantic knowledge made available to the
classifier by using SRL information.
In our experiments we use the ASSERT
parser (Pradhan et al., 2004), an SVM based se-
mantic role tagger which uses a full syntactic
analysis to automatically identify all verb predi-
cates in a sentence together with their semantic
arguments, which are output as PropBank argu-
ments (Palmer et al., 2005). It is often the case
that the semantic arguments output by the parser
do not align with any of the previously identified
noun phrases. In this case, we pass a semantic role
label to a RE only in case the two phrases share the
same head. Labels have the form “ARG1 pred1 . . .
ARGn predn” for n semantic roles filled by a
constituent, where each semantic argument label
ARGi is always defined with respect to a predicate
lemma predi. Given such level of semantic infor-
mation available at the RE level, we introduce two
new features4.
I SEMROLE the semantic role argument-
predicate pairs of REi.
3Following the system to be replicated, we simply
mapped each RE to the first WordNet sense of the head noun.
4During prototyping we experimented unpairing the ar-
guments from the predicates, which yielded worse results.
This is supported by the PropBank arguments always being
defined with respect to a target predicate. Binarizing the fea-
tures — i.e. do REi and REj have the same argument or
predicate label with respect to their closest predicate? — also
gave worse results.
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