The authors remark that extracted sentences with VFs that are referentially related to previous context (e. g., they contain a coreferential noun phrase or a discourse relation like “therefore”) are reinserted at higher accuracies.

The main focus of that work, however, was to adapt the model for use in a low-resource situation when perfect coreference information is not available.

Table 3: Accuracy of automatic annotations of noun phrases with coreferents .

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.

This is the first model we know of which introduces a broad-coverage sentence processing model which takes the effect of coreference and discourse into account.

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.

However, as the coreference processor takes trees as input, we must therefore unpack parses before resolving referential ambiguity.

the agent), get the -LGS label; (iv) non-recursive NPs are renamed NPbase (the coreference system treats each NPbase as a markable).

Although we consistently observed development gains from using automatic coreference resolution, this process creates errors that need to be studied more closely.

First, the system identified coreferent mentions of Olivetti that participated in exporting and supplying events (not shown).

A candidate constituent c will often form a coreference chain with other constituents in the discourse.

When determining whether 0 is the iargg of investment, one can draw evidence from other mentions in 0’s coreference chain.

Thus, the unit of classification for a candidate constituent c is the three-tuple (p, iargn, c’), where c’ is a coreference chain comprising 0 and its coreferent constituents.3 We defined a binary classification function Pr(+| (p,iargn,c’ that predicts the probability that the entity referred to by c fills the missing argument position iargn of predicate instance p. In the remainder of this paper, we will refer to c as the primary filler, differentiating it from other mentions in the coreference chain c’ .

(2005) suggested approaches to implicit argument identification based on observed coreference patterns; however, the authors did not implement and evaluate such methods.

analysis of naturally occurring coreference patterns to aid implicit argument identification.

Discourse references, notably coreference and bridging, play an important role in many text understanding applications, but their impact on textual entailment is yet to be systematically understood.

The simplest form of information that discourse provides is coreference , i.e., information that two linguistic expressions refer to the same entity or event.

Coreference is particularly important for processing pronouns and other anaphoric expressions, such as he in Example 1.

While coreference indicates equivalence, bridging points to the existence of a salient semantic relation between two distinct entities or events.

The detection and resolution of discourse references such as coreference and bridging anaphora play an important role in text understanding applications, like question answering and information extraction.

The understanding that the second sentence of the text entails the hypothesis draws on two coreference relationships, namely that he is Oswald, and

However, the utilization of discourse information for such inferences has been so far limited mainly to the substitution of nominal coreferents , while many aspects of the interface between discourse and semantic inference needs remain unexplored.

Focus, coherence and referential clarity are best evaluated by a class of features measuring local coherence on the basis of cosine similarity between sentences, coreference information, and summarization specific features.

This class of linguistic quality indicators is a combination of factors related to coreference , adjacent sentence similarity, and summary-specific context of surface cohesive devices.

Coreference Steinberger et al.

(2007) compare the coreference chains in input documents and in summaries in order to locate potential problems.

For all four other questions, the best feature set is Continuity, which is a combination of summarization specific features, coreference features and cosine similarity of adjacent sentences.

We now investigate to what extent each of its components—summary-specific features, coreference , and cosine similarity between adjacent sentences—contribute to performance.

However, the coreference features do not seem to contribute much towards predicting summary linguistic quality.

From the tables it is apparent that good performance on a range of phenomena in our inference model are likely to have a significant effect on RTE results, with coreference being deemed essential to the inference process for 35% of examples, and a number of other phenomena are sufficiently well represented to merit near-future attention (assuming that RTE systems do not already handle these phenomena, a question we address in section 4).

Phenomenon Occurrence Agreement coreference 35.00% 0.698 simple rewrite rule 32.62% 0.580 lexical relation 25.00% 0.738 implicit relation 23.33% 0.633 factoid 15.00% 0.412 parent-sibling 1 1.67% 0.500 genetive relation 9.29% 0.608 nominalization 8.33% 0.514 event chain 6.67% 0.589 coerced relation 6.43% 0.540 passive-active 5.24% 0.583 numeric reasoning 4.05% 0.847 spatial reasoning 3.57% 0.720

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.

relevant NLP tasks such as NER, Coreference , parsing, data acquisition and application, and others.

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 then clustered the noun phrases into coreferents for the same entity (person or organization).

For each named entity, we generate variations on the name that we would expect to see in a coreferent .

For each named entity, we compile a list of other named entities that may be coreferents , either because they are identical or because one is an expected variation on the other.

For every event, we collect its trigger and event type; for every argument, we use coreference information and record every entity and its role(s) in events of a certain type.

( coreferential ) entity mentions.

Event extraction depends on previous phases entity mention classification and coreference .

Note that entity mentions that share the same EntityID are coreferential and treated as the same object.

The task of identifying reference relations including anaphora and coreferences within texts has received a great deal of attention in natural language processing, from both theoretical and empirical perspectives.

In these data sets, coreference relations are defined as a limited version of a typical coreference; this generally means that only the relations where expressions refer to the same named entities are addressed, because it makes the coreference resolution task more information extraction-oriented.

In other words, the coreference task as defined by MUC and ACE is geared toward only identifying coreference relations anchored to an entity within the text.

These features have been examined by approaches to anaphora or coreference resolution (Soon et al., 2001; Ng and Cardie, 2002, etc.)