This paper presents a constraint-based graph partitioning approach to coreference resolution solved by relaxation labeling. The approach combines the strengths of groupwise classifiers and chain formation methods in one global method. Experiments show that our approach significantly outperforms systems based on separate classification and chain formation steps, and that it achieves the best results in the state of the art for the same dataset and metrics.Peer Reviewe
In this paper, we describe a coreference solver based on the extensive use of lexical fea-tures and ...
Coreference Resolution is an important step in many NLP tasks and has been a popular topic within th...
State-of-the-art coreference resolution systems are mostly knowledge-based systems that operate by ...
This paper presents a constraint-based graph partitioning approach to coreference resolution solve...
This paper describes the participation of RelaxCor in the Semeval-2010 task number 1: "Coreference R...
This paper describes the participation of RelaxCor in the CoNLL-2011 shared task: "Modeling Unrestri...
This work is focused on research in machine learning for coreference resolution. Coreference resolut...
The objectives of this thesis are focused on research in machine learning for coreference resolutio...
This report presents a graph partitioning approach given a set of constraints to resolve coreference...
We describe a scaffolding approach to the task of coreference resolution that incrementally combines...
We investigate new methods for creating and ap-plying ensembles for coreference resolution. While ex...
Traditionally, coreference resolution is done by mining the reference relationships between NP pairs...
This paper presents the integration of RelaxCor into FreeLing. RelaxCor is a coreference resolution ...
We propose an algorithm for coreference resolution based on analogy with shift-reduce pars-ing. By r...
Coreference resolution is one of the most fundamental Natural Language Processing tasks, aiming to i...
In this paper, we describe a coreference solver based on the extensive use of lexical fea-tures and ...
Coreference Resolution is an important step in many NLP tasks and has been a popular topic within th...
State-of-the-art coreference resolution systems are mostly knowledge-based systems that operate by ...
This paper presents a constraint-based graph partitioning approach to coreference resolution solve...
This paper describes the participation of RelaxCor in the Semeval-2010 task number 1: "Coreference R...
This paper describes the participation of RelaxCor in the CoNLL-2011 shared task: "Modeling Unrestri...
This work is focused on research in machine learning for coreference resolution. Coreference resolut...
The objectives of this thesis are focused on research in machine learning for coreference resolutio...
This report presents a graph partitioning approach given a set of constraints to resolve coreference...
We describe a scaffolding approach to the task of coreference resolution that incrementally combines...
We investigate new methods for creating and ap-plying ensembles for coreference resolution. While ex...
Traditionally, coreference resolution is done by mining the reference relationships between NP pairs...
This paper presents the integration of RelaxCor into FreeLing. RelaxCor is a coreference resolution ...
We propose an algorithm for coreference resolution based on analogy with shift-reduce pars-ing. By r...
Coreference resolution is one of the most fundamental Natural Language Processing tasks, aiming to i...
In this paper, we describe a coreference solver based on the extensive use of lexical fea-tures and ...
Coreference Resolution is an important step in many NLP tasks and has been a popular topic within th...
State-of-the-art coreference resolution systems are mostly knowledge-based systems that operate by ...