[PDF][PDF] Error Analysis for Learning-based Coreference Resolution.
O Uryupina - LREC, 2008 - cs.brandeis.edu
LREC, 2008•cs.brandeis.edu
State-of-the-art coreference resolution engines show similar performance figures (low sixties
on the MUC-7 data). Our system with a rich linguistically motivated feature set yields
significantly better performance values for a variety of machine learners, but still leaves
substantial room for improvement. In this paper we address a relatively unexplored area of
coreference resolution–we present a detailed error analysis in order to understand the
issues raised by corpus-based approaches to coreference resolution.
on the MUC-7 data). Our system with a rich linguistically motivated feature set yields
significantly better performance values for a variety of machine learners, but still leaves
substantial room for improvement. In this paper we address a relatively unexplored area of
coreference resolution–we present a detailed error analysis in order to understand the
issues raised by corpus-based approaches to coreference resolution.
Abstract
State-of-the-art coreference resolution engines show similar performance figures (low sixties on the MUC-7 data). Our system with a rich linguistically motivated feature set yields significantly better performance values for a variety of machine learners, but still leaves substantial room for improvement. In this paper we address a relatively unexplored area of coreference resolution–we present a detailed error analysis in order to understand the issues raised by corpus-based approaches to coreference resolution.
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