Zero-shot Word Sense Disambiguation using Sense De.nition Embeddings – NLPIR自然语言处理与信息检索共享平台

自然语言处理与信息检索共享平台 自然语言处理与信息检索共享平台

Zero-shot Word Sense Disambiguation using Sense De.nition Embeddings



In the new semester, our Lab, Web Search Mining and Security Lab, plans to hold an academic seminar every Monday, and each time a keynote speaker will share understanding of papers on his/her related research with you.


Tomorrow’s seminar is organized as follows:

  1. The seminar time is, Mon (August 26, 2019), at Zhongguancun Technology Park ,Building 5, 1306.
  2. Zhaoyang Wang is going to give a presentation, the paper’s title is Zero-shot Word Sense Disambiguation using Sense Definition Embeddings.
  3. The seminar will be hosted by Gang Wang.

Everyone interested in this topic is welcomed to join us.
the following is the abstract of the paper.

Zero-shot Word Sense Disambiguation using Sense Definition Embeddings

Sawan Kumar, Sharmistha Jat, Karan Saxena2, Partha Talukdar


Word Sense Disambiguation (WSD) is a long-standing but open problem in Natural Language Processing (NLP). WSD corpora are typically small in size, owing to an expensive annotation process. Current supervised WSD methods treat senses as discrete labels and also resort to predicting the Most-Frequent-Sense (MFS) for words unseen during training. This leads to poor performance on rare and unseen senses. To overcome this challenge, we propose Extended WSD Incorporating Sense Embeddings (EWISE), a supervised model to perform WSD by predicting over a continuous sense embedding space as opposed to a discrete label space. This allows EWISE to generalize over both seen and unseen senses, thus achieving generalized zero-shot learning. To obtain target sense embeddings, EWISE utilizes sense de.nitions. EWISE learns a novel sentence encoder for sense de.nitions by using WordNet relations and also ConvE, a recently proposed knowledge graph embedding method. We also compare EWISE against other sentence encoders pretrained on large corpora to generate definition embeddings. EWISE achieves new state-of-the-art WSD performance.


This Monday, Zhaoyang Wang introduced another paper from ACL 2019.

EWISE combines senseannotated data, dictionary definitions and lexical knowledge bases.

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