Humans often make creative use of words to express novel senses. A long-standing effort in natural language processing has been focusing on word sense disambiguation (WSD), but little has been explored about how the sense inventory of a word may be extended toward novel meanings. We present a paradigm of word sense extension (WSE) that enables words to spawn new senses toward novel context. We develop a framework that simulates novel word sense extension by first partitioning a polysemous word type into two pseudo-tokens that mark its different senses, and then inferring whether the meaning of a pseudo-token can be extended to convey the sense denoted by the token partitioned from the same word type. Our framework combines cognitive models of chaining with a learning scheme that transforms a language model embedding space to support various types of word sense extension. We evaluate our framework against several competitive baselines and show that it is superior in predicting plausible novel senses for over 7,500 English words. Furthermore, we show that our WSE framework improves performance over a range of transformer-based WSD models in predicting rare word senses with few or zero mentions in the training data.

本研究提出了一种单词意义的扩展范式，以启发式的方法解决了在语言处理中单词意义消岐的限制，展示了一种基于链式认知模型和语言模型嵌入空间的学习算法，该算法可以有效地扩展英语单词的新意义，同时提高了对训练数据中罕见单词通常使用的WSD模型的预测准确率。

词义拓展