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Exploring Statistical and Neural Models for Noun Ellipsis Detection and Resolution in English

Computational approaches to noun ellipsis resolution has been sparse, with only a naive rule-based approach that uses syntactic feature constraints for marking noun ellipsis licensors and selecting their antecedents. In this paper, we further the ellipsis research by exploring several statistical and neural models for both the subtasks involved in the ellipsis resolution process and addressing the representation and contribution of manual features proposed in previous research. Using the best performing models, we build an end-to-end supervised Machine Learning (ML) framework for this task that improves the existing F1 score by 16.55{\%} for the detection and 14.97{\%} for the resolution subtask. Our experiments demonstrate robust scores through pretrained BERT (Bidirectional Encoder Representations from Transformers) embeddings for word representation, and more so the importance of manual features{--} once again highlighting the syntactic and semantic characteristics of the ellipsis phenomenon. For the classification decision, we notice that a simple Multilayar Perceptron (MLP) works well for the detection of ellipsis; however, Recurrent Neural Networks (RNN) are a better choice for the much harder resolution step.

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