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概括》

词汇嵌入能够捕获词汇语义信息，但在不能为多义词的不一样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。

之前得到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类似性方面具备优点，但在几个单词相关任务上优于单个原型词。

在这篇文章中，咱们引入了一种新的方法，经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，能够诱导一词多义到任何预先定义的嵌入空间。

这种方法的优势是，它容许集成本体信息，同时也容易诱导一词多义到预先定义的嵌入空间，而不须要从新训练。

咱们评估了几个词的类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。