AAAI 2019 分析

AAAI 2019 分析

Google Scholar 訂閱

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.

《基於上下文化知識嵌入的詞義概括》

詞彙嵌入能夠捕獲詞彙語義信息，但在不能爲多義詞的不一樣語義賦予獨特的表示上仍存在缺陷。

它們也沒有包括來自精心編排的語義詞典和詞典的信息。

之前得到基於本體的詞義表示的方法學習嵌入，這些嵌入在理解上下文類似性方面具備優點，但在幾個單詞相關任務上優於單個原型詞。

在這篇文章中，咱們引入了一種新的方法，經過將上下文化的意義表示（從帶有意義的語料庫和單詞嵌入到知識庫中）聯合起來，能夠誘導一詞多義到任何預先定義的嵌入空間。

這種方法的優勢是，它容許集成本體信息，同時也容易誘導一詞多義到預先定義的嵌入空間，而不須要從新訓練。

咱們評估了幾個詞的類似度和相關性任務以及兩個外在任務的向量，發現它始終優於當前的先進水平。