Selectional preferences have long been claimed to be essential for
coreference resolution. However, they are mainly modeled only implicitly by
current coreference resolvers. We propose a dependency-based embedding model of
selectional preferences which allows fine-grained compatibility judgments with
high coverage. We show that the incorporation of our model improves coreference
resolution performance on the CoNLL dataset, matching the state-of-the-art
results of a more complex system. However, it comes with a cost that makes it
debatable how worthwhile such improvements are.

本研究提出一种基于依存关系嵌入技术的语义选择偏好模型，能够对高频词汇的匹配提供细粒度的判断。实验结果表明，该模型提高了 CoNLL 数据集的共指解析性能，达到了更复杂系统的最新水平，但其代价也值得商榷。

重审共指消解的选取偏好

Revisiting Selectional Preferences for Coreference Resolution

Implicit semantic role labeling (iSRL) is the task of predicting the semantic
roles of a predicate that do not appear as explicit arguments, but rather
regard common sense knowledge or are mentioned earlier in the discourse. We
introduce an approach to iSRL based on a predictive recurrent neural semantic
frame model (PRNSFM) that uses a large unannotated corpus to learn the
probability of a sequence of semantic arguments given a predicate. We leverage
the sequence probabilities predicted by the PRNSFM to estimate selectional
preferences for predicates and their arguments. On the NomBank iSRL test set,
our approach improves state-of-the-art performance on implicit semantic role
labeling with less reliance than prior work on manually constructed language
resources.

本文提出了一种基于预测递归神经语义框架模型（PRNSFM）的隐含语义角色标注（iSRL）方法，利用大型未注释语料库来学习给定谓词的一系列语义论证的概率，同时通过 PRNSFM 预测的序列概率估计谓词及其参数的选择性偏好，并在 NomBank iSRL 测试集上实验验证此方法的最新状态 - of-the-art 表现优于以往方法，且依赖较少于手工构建的语言资源。