AAAI 2019 分析

AAAI 2019 分析

Google Scholar 订阅

CoKE : Word Sense Induction Using Contextualized Knowledge Embeddings

Word Embeddings can capture lexico-semantic information but remain flawed in their inability to assign unique representations to different senses of polysemous words.

They also fail to include information from well-curated semantic lexicons and dictionaries.

Previous approaches that obtain ontologically grounded word-sense representations learn embeddings that are superior in understanding contextual similarity but are outperformed on several word relatedness tasks by single prototype words.

In this work, we introduce a new approach that can induce polysemy to any pre-defined embedding space by jointly grounding contextualized sense representations learned from sense-tagged corpora and word embeddings to a knowledge base.

The advantage of this method is that it allows integrating ontological information while also readily inducing polysemy to pre-defined embedding spaces without the need for re-training.

We evaluate our vectors on several word similarity and relatedness tasks, along with two extrinsic tasks and find that it consistently outperforms current state-of-the-art.

《基于上下文化知识嵌入的词义归纳》

词汇嵌入可以捕获词汇语义信息，但在不能为多义词的不同语义赋予独特的表示上仍存在缺陷。

它们也没有包括来自精心编排的语义词典和词典的信息。

以前获得基于本体的词义表示的方法学习嵌入，这些嵌入在理解上下文相似性方面具有优势，但在几个单词相关任务上优于单个原型词。

在这篇文章中，我们引入了一种新的方法，通过将上下文化的意义表示（从带有意义的语料库和单词嵌入到知识库中）联合起来，可以诱导一词多义到任何预先定义的嵌入空间。

这种方法的优点是，它允许集成本体信息，同时也容易诱导一词多义到预先定义的嵌入空间，而不需要重新训练。

我们评估了几个词的相似度和相关性任务以及两个外在任务的向量，发现它始终优于当前的先进水平。