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In this paper we propose a competition learning approach to coreference resolution. Traditionally, supervised machine learning approaches adopt the single-candidate model. Nevertheless the preference relationship between the antecedent candidates cannot be determined accurately in this model. By contrast, our approach adopts a twin-candidate learning model.(More)
Developing a full coreference system able to run all the way from raw text to semantic interpretation is a considerable engineering effort, yet there is very limited availability of off-the shelf tools for researchers whose interests are not in coreference, or for researchers who want to concentrate on a specific aspect of the problem. We present BART, a(More)
The Lyme disease agent Borrelia burgdorferi naturally persists in a cycle that primarily involves ticks and mammals. We have now identified a tick receptor (TROSPA) that is required for spirochetal colonization of Ixodes scapularis. B. burgdorferi outer surface protein A, which is abundantly expressed on spirochetes within the arthropod and essential for(More)
The traditional mention-pair model for coref-erence resolution cannot capture information beyond mention pairs for both learning and testing. To deal with this problem, we present an expressive entity-mention model that performs coreference resolution at an entity level. The model adopts the Inductive Logic Programming (ILP) algorithm, which provides a(More)
In this paper we present a noun phrase coreference resolution system which aims to enhance the identification of the coreference realized by string matching. For this purpose, we make two extensions to the standard learning based resolution framework. First, to improve the recall rate, we introduce an additional set of features to capture the different(More)
Syntactic knowledge is important for pronoun resolution. Traditionally, the syntactic information for pronoun resolution is represented in terms of features that have to be selected and defined heuristically. In the paper, we propose a kernel-based method that can automatically mine the syntactic information from the parse trees for pronoun resolution.(More)
Semantic relatedness is a very important factor for the coreference resolution task. To obtain this semantic information, corpus-based approaches commonly leverage patterns that can express a specific semantic relation. The patterns, however, are designed manually and thus are not necessarily the most effective ones in terms of accuracy and breadth. To deal(More)
In this paper we focus on how to improve pronoun resolution using the statistics-based semantic compatibility information. We investigate two unexplored issues that influence the effectiveness of such information: statistics source and learning framework. Specifically, we for the first time propose to utilize the web and the twin-candidate model, in(More)
Although effective for antecedent determination, the traditional twin-candidate model can not prevent the invalid resolution of non-anaphors without additional measures. In this paper we propose a modified learning framework for the twin-candidate model. In the new framework, we make use of non-anaphors to create a special class of training instances, which(More)
The molecular basis of how Borrelia burgdorferi (Bb), the Lyme disease spirochete, maintains itself in nature via a complex life cycle in ticks and mammals is poorly understood. Outer surface (lipo)protein A (OspA) of Bb has been the most intensively studied of all borrelial molecular constituents, and hence, much has been speculated about the potential(More)