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The CoNLL-2014 shared task is an extension of last year's shared task and fo-cuses on correcting grammatical errors in essays written by non-native learners of English. In this paper, we describe the Illinois-Columbia system that participated in the shared task. Our system ranked second on the original annotations and first on the revised annotations. The(More)
This paper describes a supervised, knowledge-intensive approach to the automatic identification of semantic relations between nominals in English sentences. The system employs different sets of new and previously used lexical, syntactic, and semantic features extracted from various knowledge sources. At SemEval 2007 the system achieved an F-measure of 72.4%(More)
We describe the University of Illinois (UI) system that participated in the Helping Our Own (HOO) 2012 shared task, which focuses on correcting preposition and determiner errors made by non-native English speakers. The task consisted of three metrics: Detection, Recognition, and Correction, and measured performance before and after additional revisions to(More)
Despite the rising interest in developing grammatical error detection systems for non-native speakers of English, progress in the field has been hampered by a lack of informative met-rics and an inability to directly compare the performance of systems developed by different researchers. In this paper we address these problems by presenting two evaluation(More)
The CoNLL-2012 shared task is an extension of the last year's coreference task. We participated in the closed track of the shared tasks in both years. In this paper, we present the improvements of Illinois-Coref system from last year. We focus on improving mention detection and pronoun coreference resolution, and present a new learning protocol. These new(More)
This paper presents Illinois-Coref, a system for coreference resolution that participated in the CoNLL-2011 shared task. We investigate two inference methods, Best-Link and All-Link, along with their corresponding, pairwise and structured, learning protocols. Within these, we provide a flexible architecture for incorporating linguistically-motivated(More)