
Root, Rebecca Louise et al. use the Discourse Representation Theory of Hans Kamp to develop a theory in which each sentence contributes to building a representation of the meaning of the discourse. They introduce ``reference markers'' for each noun phrase and truth is defined in terms of an embedding of the representation of the model. Along with the formal explanations, it is argued that this approach is attractive from a computational point of view.



@phdthesis{root-RL-86-phd-ut_semantics-of-anaphora-in-discourse,
  title={The semantics of anaphora in discourse},
  author={Root, Rebecca Louise},
  year={1986},
  school={University of Texas at Austin},
  annote = {

ABSTRACT

Anaphora may link to discontinuous antecedents, as in "John found a piano
teacher.  They are both fond of Mozart", and links from plural anaphors to
singular antecedents in distributive contexts, as in "Every girl brought a
cake.  Coincidentally, they were all chocolate." Despite the surface
variation, it is argued here that a uniform account of these constructions is
possible. An analysis of the semantic properties of discourse anaphora is
presented here which offers a unified explanation of both the truth
conditions and the acceptability conditions of this phenomenon. The analysis
is framed within the context of the Discourse Representation Theory of Hans
Kamp. In this theory, each sentence contributes to the construction of a
representation of the meaning of the discourse. This is done in part through
the introduction of "reference markers" for each noun phrase. Truth is
defined for such a representation in terms of an embedding of the
representation in a model. In this account, an anaphoric link is truthful if
the set of individuals to which the anaphor's reference marker is mapped, is
the same as the set of individuals to which the reference markers of the
antecedent(s) are mapped in the course of finding an embedding for the
representation. . This definition formalized, together with the assumptions
that the discourse is true and that there is a principle of semantic number
agreement, constitute the analysis presented here. In addition to the formal
explanations, it is argued that this approach is attractive from a
computational point of view, and an implementation of the leading ideas is
presented.



Altaf Rahman and Vincent Ng use ensembles from a variety of supervised coreference models. They discuss different methods to apply a model-heterogenous ensemble for coreference resolution: ensemble-based methods significantly outperform the best member of the ensemble.

@inproceedings{rahman-ng-11ijc_ensemble-based-coreference-resolution,
  title={Ensemble-based coreference resolution},
  author={Altaf Rahman and Vincent Ng},
  booktitle={Proceedings of the 22nd IJCAI Volume Three},
  pages={1884--1889},
  year={2011},
  abstract = {

We investigate new methods for creating and applying ensembles for
coreference resolution. While existing ensembles for coreference resolution
are typically created using different learning algorithms, clustering
algorithms or training sets, we harness recent advances in coreference
modeling and propose to create our ensemble from a variety of supervised
coreference models. However, the presence of pairwise and non-pairwise
coreference models in our ensemble presents a challenge to its application:
it is not immediately clear how to combine the coreference decisions made by
these models. We investigate different methods for applying a
model-heterogeneous ensemble for coreference resolution. Empirical results on
the ACE data sets demonstrate the promise of ensemble approaches: all
ensemble-based systems significantly outperform the best member of the
ensemble.
}}


Nguyen et al. use a filtering algorithm to rerank the output of coreference hypotheses, where the filter is based on relational structures between mentions and relationships. The second approach uses a joint model augmented with a set of relational features derived from semantic relationships of mentions. They claim improved performance of state-of-the-art coreference resolver based on learning.

@inproceedings{nguyen-poesio-12coling_relational-coreference-resolution,
 author = {Nguyen, Truc-Vien T. and Poesio, Massimo},
 title = {Relational Structures and Models for Coreference Resolution},
 booktitle = {Proceedings of the 24rd International Conference on Computational Linguistics},
 series = {COLING '12},
 month = {December},
 year = {2012},
 location = {Mumbai, India},
 numpages = {10}
 annote = {
Truc-Vien T. Nguyen, M assimo Poesio 1,2

(1) CIMeC, University of Trento, Rovereto (TN), 38068, Italy
(2) University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK
{trucvien.nguyenthi,massimo.poesio}@unitn.it

Abstract

Coreference resolution is the task of identifying the sets of mentions
referring to the same entity.  Although modern machine learning approaches to
coreference resolution exploit a variety of semantic information, the
literature on the effect of relational information on coreference is still
very limited. In this paper, we discuss and compare two methods for
incorporating relational information into a coreference resolver. One
approach is to use a filtering algorithm to rerank the output of coreference
hypotheses. The filter is based on the relational structures between mentions
and their corresponding relationships. The second approach is to use a joint
model enriched with a set of relational features derived from semantic
relations of each mention. Both methods have shown to improve the performance
of a learning-based state-of-the-art coreference resolver.

}}



Veselin Stoyanov et al build the coreference clusters incrementally, beginning with the most precise rules (termed as sieves by Raghunathan et al. (2010)) and use the available coreference information to guide the less precise sieves. Coreference resolution is inherently a global job. In this context, the approach of Raghunathan et al. (2010) may be taken as a rule-based greedy search for a globally consistent clustering.



@inproceedings{stoyanov-eisneree-12coling_easy-first-coreference-resolution,
  title =	 {Easy-first Coreference Resolution},
  author =	 {Veselin Stoyanov and Jason Eisneree},
  booktitle =	 {Proceedings 24rd COLING '12},
  month =	 {Dec},
  year =	 {2012},
  location =	 {Mumbai, India},
  annote = {

EXAMPLE:
   Tamara Maschino, a resident of the Clear Lake area of Houston, criticizes
   Bush for his lack of attention to pollution problems from chemical plants
   near her home.

here the phrases [Tamara Maschino] [resident of the Clear Lake area of
Houston] and [her] all have the same referent, while {Bush] and [his] have
the same.

coreference resolution is an NLP area which tries to identify phrases having
the same coreferent.

this paper presents a rule-based approach to coreference resolution, which
starts with the easier approaches.  It is also a detailed analysis of this
work:

abstract
Coreference resolution has traditionally benefited from machine learning
approaches (Soon et al., 2001; Ng and Cardie, 2002; Bengtson and Roth, 2008;
Stoyanov et al., 2009). Surprisingly, however, recent work has shown that
simple rule-based coreference systems can compete with the state-of-the-art
machine-learning-based systems if provided with rich lexical, syntactic,
semantic and discourse information (Haghighi and Klein, 2010; Raghunathan et
al., 2010). In fact, the rule-based system of Raghunathan et al. (2010)
exhibited the top score in the recent CoNLL evaluation (Pradhan et al.,
2011). This system’s innovation is to build the coreference clusters
incrementally, starting with the most precise rules (dubbed sieves by
Raghunathan et al.  (2010)) and use the available coreference information to
guide the less precise sieves.

Coreference resolution is inherently a global task: for example, discovering
that Smith and she corefer makes it more probable that Smith corefers with
Jane Smith (i.e., a female name) rather than Jason Smith. Furthermore,
grouping Jane Smith, she and Jason Smith in the same cluster should be
rejected by the clustering algorithm because it results in poor gender and
proper name agreement. Yet incorporating such structured information in
coreference has proven challenging.

There have been several successful attempts to incorporate structured
information through joint inference such as Culotta et al. (2007) and Poon
and Domingos (2008), but they do not explicitly learn parameters tuned to the
inference algorithm used at test time (and the latter relies on a complicated
and expensive inference procedures). The difficulty of inferring globally
consistent clusterings lies in the fact that there are exponentially many
clusterings and producing an "optimal" clustering according to most measures
of global coherence is NP-hard.  In this context, the approach of Raghunathan
et al. (2010) can be seen as a rule-based greedy search for a globally
consistent clustering.

