This paper introduces a novel sentence processing model that consists of a parser augmented with a probabilistic logic-based model of coreference resolution , which allows us to simulate how context interacts with syntax in a reading task.

There are three main parts of the model: a syntactic processor, a coreference resolution system, and a simple pragmatics processor which computes certain limited forms of discourse coherence.

The coreference resolution system is implemented

The model comprises three parts: a parser, a coreference resolution system, and a pragmatics subsystem.

The primary function of the discourse processing module is to perform coreference resolution for each mention in an incrementally processed text.

Note that, unlike Huang eta1., we assume an ordering on c and y if Coref(x, y) is true: 3/ must occur earlier in the document than c. The remaining predicates in Table 1 are a subset of features used by other coreference resolution systems (cf.

A number of systems have tried to address the question of coreference in RTE as a preprocessing step prior to inference proper, with most systems using off-the-shelf coreference resolvers such as JavaRap (Qiu et al., 2004) or OpenNLP3.

Results were inconclusive, however, with several reports about errors introduced by automatic coreference resolution (Agichtein et al., 2008; Adams et al., 2007).

Specific evaluations of the contribution of coreference resolution yielded both small negative (Bar-Haim et al., 2008) and insignificant positive (Chambers et al., 2007) results.

While semantic knowledge (e.g., from WordNet or Wikipedia) has been used beneficially for coreference resolution (Soon et al., 2001; Ponzetto and Strube, 2006), reference resolution has, to our knowledge, not yet been employed to validate entailment rules’ applicability.

E.g., in Example 1 above, knowing that Kennedy was a president can alleviate the need for coreference resolution .

Conversely, coreference resolution can often be used to overcome gaps in entailment knowledge.

sented; (2) the off-the-shelf coreference resolution systems which may have been not robust enough; (3) the limitation to nominal coreference; and (4) overly simple integration of reference information into the inference engines.

Table 2 shows that 77% of all focus terms and 86% of the reference terms were nominal phrases, which justifies their prominent position in work on anaphora and coreference resolution .

This result reaffirms the usefulness of cross-document coreference resolution for inference (Huang et al., 2009).

The results confirmed our initial intuition about some phenomena: for example, that coreference resolution is central to RTE, and that detecting the connecting structure is crucial in discerning negative from positive examples.

Tasks such as Named Entity and coreference resolution , syntactic and shallow semantic parsing, and information and relation extraction have been identified as worthwhile tasks and pursued by numerous researchers.

ported by their designers were the use of structured representations of shallow semantic content (such as augmented dependency parse trees and semantic role labels); the application of NLP resources such as Named Entity recognizers, syntactic and dependency parsers, and coreference resolvers ; and the use of special-purpose ad-hoc modules designed to address specific entailment phenomena the researchers had identified, such as the need for numeric reasoning.

As the example in figure 1 illustrates, most RTE examples require a number of phenomena to be correctly resolved in order to reliably determine the correct label (the Interaction problem); a perfect coreference resolver might as a result yield little improvement on the standard RTE evaluation, even though coreference resolution is clearly required by human readers in a significant percentage of RTE examples.

We are not aware of similarly well-tested, publicly available coreference resolution systems that handle all types of anaphora for German.

We considered adapting the BART coreference resolution toolkit (Versley et a1., 2008) to German, but a number of language-dependent decisions regarding preprocessing, feature engineering, and the learning paradigm would need to be made in order to achieve reasonable performance comparable to state-of-the—art English coreference resolution systems.

The model also shows promise for other discourse-related tasks such as coreference resolution and discourse parsing.