Index of papers in Proc. ACL 2014 that mention
  • coreference resolution
Björkelund, Anders and Kuhn, Jonas
Abstract
We investigate different ways of learning structured perceptron models for coreference resolution when using nonlocal features and beam search.
Background
Coreference resolution is the task of grouping referring expressions (or mentions) in a text into disjoint clusters such that all mentions in a cluster refer to the same entity.
Background
In recent years much work on coreference resolution has been devoted to increasing the ex-pressivity of the classical mention-pair model, in which each coreference classification decision is limited to information about two mentions that make up a pair.
Background
Nevertheless, the two best systems in the latest CoNLL Shared Task on coreference resolution (Pradhan et al., 2012) were both variants of the mention-pair model.
Introducing Nonlocal Features
While beam search and early updates have been successfully applied to other NLP applications, our task differs in two important aspects: First, coreference resolution is a much more difficult task, which relies on more (world) knowledge than what is available in the training data.
Introduction
We show that for the task of coreference resolution the straightforward combination of beam search and early update (Collins and Roark, 2004) falls short of more limited feature sets that allow for exact search.
Introduction
This approach provides a powerful boost to the performance of coreference resolvers , but we find that it does not combine well with the LaSO learning strategy.
Related Work
The perceptron has previously been used to train coreference resolvers either by casting the problem as a binary classification problem that considers pairs of mentions in isolation (Bengtson and Roth, 2008; Stoyanov et al., 2009; Chang et al., 2012, inter alia) or in the structured manner, where a clustering for an entire document is predicted in one go (Fernandes et al., 2012).
Results
For English we also compare it to the Berkeley system (Durrett and Klein, 2013), which, to our knowledge, is the best publicly available system for English coreference resolution (denoted D&K).
coreference resolution is mentioned in 13 sentences in this paper.
Topics mentioned in this paper:
Liu, Changsong and She, Lanbo and Fang, Rui and Chai, Joyce Y.
Evaluation and Discussion
With no surprise, the coreference resolution performance plays an important role in the final grounding performance (see the grounding performance of using manually annotated coreference in the bottom part of Table 1).
Evaluation and Discussion
Due to the simplicity of our current coreference classifier and the flexibility of the human-human dialogue in the data, the pairwise coreference resolution only achieves 0.74 in precision and 0.43 in recall.
Evaluation and Discussion
The low recall of coreference resolution makes it difficult to link interrelated referring expressions and resolve them jointly.
Probabilistic Labeling for Reference Grounding
Our system first processes the data using automatic semantic parsing and coreference resolution .
Probabilistic Labeling for Reference Grounding
We then perform pairwise coreference resolution on the discourse entities to find out the discourse relations between entities from different utterances.
Probabilistic Labeling for Reference Grounding
Based on the semantic parsing and pairwise coreference resolution results, our system further builds a graph representation to capture the collaborative discourse and formulate referential grounding as a probabilistic labeling problem, as described next.
coreference resolution is mentioned in 9 sentences in this paper.
Topics mentioned in this paper:
Andrews, Nicholas and Eisner, Jason and Dredze, Mark
Abstract
In this paper, we propose a model for cross-document coreference resolution that achieves robustness by learning similarity from unlabeled data.
Conclusions
Our primary contribution consists of new modeling ideas, and associated inference techniques, for the problem of cross-document coreference resolution .
Introduction
In this paper, we propose a method for jointly (1) learning similarity between names and (2) clustering name mentions into entities, the two major components of cross-document coreference resolution systems (Baron and Freedman, 2008; Finin et al., 2009; Rao et al., 2010; Singh et al., 2011; Lee et al., 2012; Green et al., 2012).
Overview and Related Work
Cross-document coreference resolution (CDCR) was first introduced by Bagga and Baldwin (1998b).
Overview and Related Work
Name similarity is also an important component of within-document coreference resolution , and efforts in that area bear resemblance to our approach.
coreference resolution is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Mazidi, Karen and Nielsen, Rodney D.
Approach
Coreference resolution , which could help avoid vague question generation, is discussed in Section 5.
Linguistic Challenges
Here we briefly describe three challenges: negation detection, coreference resolution , and verb forms.
Linguistic Challenges
5.2 Coreference Resolution
Linguistic Challenges
Currently, our system does not use any type of coreference resolution .
coreference resolution is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Bamman, David and Underwood, Ted and Smith, Noah A.
Data
While previous work uses the Stanford CoreNLP toolkit to identify characters and extract typed dependencies for them, we found this approach to be too slow for the scale of our data (a total of 1.8 billion tokens); in particular, syntactic parsing, with cubic complexity in sentence length, and out-of-the-box coreference resolution (with thousands of potential antecedents) prove to be
Data
3.2 Pronominal Coreference Resolution
Data
While the character clustering stage is essentially performing proper noun coreference resolution , approximately 74% of references to characters in books come in the form of pronouns.5 To resolve this more difficult class at the scale of an entire book, we train a log-linear discriminative classifier only on the task of resolving pronominal anaphora (i.e., ignoring generic noun phrases such as the paint or the rascal).
Introduction
(2013) explicitly learn character types (or “personas”) in a dataset of Wikipedia movie plot summaries; and entity-centric models form one dominant approach in coreference resolution (Durrett et al., 2013; Haghighi and Klein, 2010).
coreference resolution is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Luo, Xiaoqiang and Pradhan, Sameer and Recasens, Marta and Hovy, Eduard
Introduction
Coreference resolution aims at identifying natural language expressions (or mentions) that refer to the same entity.
Introduction
A critically important problem is how to measure the quality of a coreference resolution system.
Introduction
Therefore, the identical-mention-set assumption limits BLANC-gold’s applicability when gold mentions are not available, or when one wants to have a single score measuring both the quality of mention detection and coreference resolution .
Original BLANC
When Tk, = Tr, Rand Index can be applied directly since coreference resolution reduces to a clustering problem where mentions are partitioned into clusters (entities):
coreference resolution is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Raghavan, Preethi and Fosler-Lussier, Eric and Elhadad, Noémie and Lai, Albert M.
Problem Description
Thus, in order to align event sequences, we need to compute scores corresponding to cross-narrative medical event coreference resolution and cross-narrative temporal relations.
Problem Description
4 Cross-Narrative Coreference Resolution and Temporal Relation Learning
Problem Description
The coreference resolution performs with 71.5% precision and 82.3% recall.
coreference resolution is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: