This paper focuses on the study of recognizing discontiguous entities.
Motivated by a previous work, we propose to use a novel hypergraph
representation to jointly encode discontiguous entities of unbounded length,
which can overlap with one another. To compare with existing approaches, we
first formally introduce the notion of model ambiguity, which defines the
difficulty level of interpreting the outputs of a model, and then formally
analyze the theoretical advantages of our model over previous existing
approaches based on linear-chain CRFs. Our empirical results also show that our
model is able to achieve significantly better results when evaluated on
standard data with many discontiguous entities.

该研究聚焦于识别不连续实体，通过使用新的超图表示方法来编码不连续且重叠的实体，形成一个独特的模型。研究结果表明，该模型在处理大量不连续实体的数据时显示出较强的优势。

学习识别不连续实体

Learning to Recognize Discontiguous Entities

This paper presents novel Bayesian optimisation algorithms for minimum error
rate training of statistical machine translation systems. We explore two
classes of algorithms for efficiently exploring the translation space, with the
first based on N-best lists and the second based on a hypergraph representation
that compactly represents an exponential number of translation options. Our
algorithms exhibit faster convergence and are capable of obtaining lower error
rates than the existing translation model specific approaches, all within a
generic Bayesian optimisation framework. Further more, we also introduce a
random embedding algorithm to scale our approach to sparse high dimensional
feature sets.

本文提出了一种使用贝叶斯优化算法进行最小误差率训练的新型统计机器翻译系统，探索了两类算法来高效地探索翻译空间，其中一类基于 N-best 列表，另一类基于超图表示法，同时介绍了一种随机嵌入算法来扩展我们的方法以处理稀疏高维特征集。