论文标题
HAPYBOX：使用盒子嵌入的高nym发现的监督方法
HyperBox: A Supervised Approach for Hypernym Discovery using Box Embeddings
论文作者
论文摘要
Hypernymy在分类学学习，本体学习等许多AI任务中起着基本作用。这促使发展许多自动识别方法来提取这种关系，其中大多数依赖于单词分布。我们提出了一种新型的模型超箱，以学习用于HyperNym Discovery的盒子嵌入。给定输入项，Hyperbox从目标语料库中检索其合适的高nym。对于此任务，我们将发布的数据集用于Semeval 2018 HyperNym Discovery共享任务。我们比较了模型在两个特定知识领域的表现：医学和音乐。在实验上，我们表明我们的模型在大多数评估指标上都优于现有方法。此外，我们的模型仅使用一小部分训练数据就可以很好地概括过未见的超呼气对。
Hypernymy plays a fundamental role in many AI tasks like taxonomy learning, ontology learning, etc. This has motivated the development of many automatic identification methods for extracting this relation, most of which rely on word distribution. We present a novel model HyperBox to learn box embeddings for hypernym discovery. Given an input term, HyperBox retrieves its suitable hypernym from a target corpus. For this task, we use the dataset published for SemEval 2018 Shared Task on Hypernym Discovery. We compare the performance of our model on two specific domains of knowledge: medical and music. Experimentally, we show that our model outperforms existing methods on the majority of the evaluation metrics. Moreover, our model generalize well over unseen hypernymy pairs using only a small set of training data.
