We also propose to use a coreference resolution system and consider coreferent entities to be the same discourse entity.

As the coreference resolution system is trained on well-formed textual documents and expects a correct sentence ordering, we use in all our experiments only features that do not rely on sentence order (e.g.

Second, we want to evaluate the influence of automatically performed coreference resolution in a controlled fashion.

Finally, they include a heuristic coreference resolution component by linking mentions which share a

We present an unsupervised model for coreference resolution that casts the problem as a clustering task in a directed labeled weighted multigraph.

Coreference resolution is the task of determining which mentions in a text refer to the same entity.

Quite recently, however, rule-based approaches regained popularity due to Stanford’s multi-pass sieve approach which exhibits state-of-the-art performance on many standard coreference data sets (Raghunathan et al., 2010) and also won the CoNLL-2011 shared task on coreference resolution (Lee et al., 2011; Pradhan et al., 2011).

In this paper we present a graph-based approach for coreference resolution that models a document to be processed as a graph.

Graph-based coreference resolution .

Nicolae and Nicolae (2006) phrase coreference resolution as a graph clustering problem: they first perform pairwise classification and then construct a graph using the derived confidence values as edge weights.

(2010) and Cai and Strube (2010) perform coreference resolution in one step using graph partitioning approaches.

method to optimize the pairwise model of a coreference resolution system.

This is a rather idealized setting but our focus is on comparing various pairwise local models rather than on building a full coreference resolution system.

Coreference resolution is the problem of partitioning a sequence of noun phrases (or mentions), as they occur in a natural language text, into a set of referential entities.

In this kind of architecture, the performance of the entire coreference system strongly depends on the quality of the local pairwise classifier.1 Consequently, a lot of research effort on coreference resolution has focused on trying to boost the performance of the pairwise classifier.

For instance, some coreference resolution systems process different kinds of anaphors separately, which suggests for example that pairs containing an anaphoric pronoun behave differently from pairs with non-

From this formal point of view, the task of coreference resolution consists in fixing of, obserVing labeled samples (in): y)t}t€TrainSet and, given partially observed new variables

Finally, the postprocessing stage applies coreference resolution and sentence reordering to build the summary.

Then we conduct simple query expansion based on the title of the topic and cross-document coreference resolution .

Cross-document coreference resolution , semantic role labeling and relation extraction are accomplished Via the methods described in Section 5.

Efficiently incorporating entity-level information is a challenge for coreference resolution systems due to the difficulty of exact inference over partitions.

Our transitive system is more effective at using properties than a pairwise system and a previous entity-level system, and it achieves performance comparable to that of the Stanford coreference resolution system, the winner of the CoNLL 2011 shared task.

The inclusion of entity-level features has been a driving force behind the development of many coreference resolution systems (Luo et al., 2004; Rahman and Ng, 2009; Haghighi and Klein, 2010; Lee et al., 2011).