Search Space Pruning: A Simple Solution for Better Coreference Resolvers

Abstract

There is a significant gap between the performance of a coreference resolution system on gold mentions and on system mentions. This gap is due to the large and unbalanced search space in coreference resolution when using system mentions. In this paper we show that search space pruning is a simple but efficient way of improving coreference resolvers. By incorporating our pruning method in one of the state-of-the-art coreference resolution systems, we achieve the best reported overall score on the CoNLL 2012 English test set. A version of our pruning method is available with the Cort coreference resolution source code.

Use Generalized Representations, But Do Not Forget Surface Features

“…deepcoref introduced by Clark and Manning (2016a). However, we show that a simple SVM model that is adapted from our coreferent mention detection approach (Moosavi and Strube, 2016), significantly outperforms the more complex neural models. We show that the SVM model also generalizes better than the neural model on a new domain other than the CoNLL dataset.…”

“…singleton). The proposed approaches of Recasens et al (2013), Marneffe et al (2015), and Moosavi and Strube (2016) discriminate mentions for coreference resolution this way.…”

“…We introduce a new anaphoricity detection model as the second neural model using a Long-Short Term Memory (LSTM) network (Hochreiter and Schmidhuber, 1997). The third approach is adapted from our state-of-the-art coreferent mention detection (Moosavi and Strube, 2016).…”

“…In addition to the features we used for coreferent mention detection, we also add the following features for anaphoricity detection: string match in the previous context, head match in the previous context, mention words are contained in another mention, mention words are contained in a previous mention, mention contains words of another mention, mention contains words of a previous mention. Similar to Moosavi and Strube (2016), we use an anchored SVM (Goldberg and Elhadad, 2007) with a polynomial kernel of degree two and remove feature-values that occur less than 10 times. The use of an anchored SVM with pruning helps the model to generalize better on new domains (Goldberg and Elhadad, 2009).…”

Understanding Mention Detector-Linker Interaction in Neural Coreference Resolution

“…Alternatively, as Zhong and Chen (2021) showed the benefit of disentangling the span representations for entity detection and relation extraction in information extraction based on the intuition that they are disparate tasks, one may split the task of anaphoricity decision from mention linking and introduce a separately parameterized anaphoricity module, similarly considering the discrepancy between the two tasks. Recasens et al (2013); Moosavi and Strube (2016); inter alia have pursued similar ideas in the pre-neural era, but it has still not yet been explored with deep models.…”