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

Word sense extension

The Word-in-Context (WiC) task has attracted considerable attention in the
NLP community, as demonstrated by the popularity of the recent MCL-WiC SemEval
shared task. Systems and lexical resources from word sense disambiguation (WSD)
are often used for the WiC task and WiC dataset construction. In this paper, we
establish the exact relationship between WiC and WSD, as well as the related
task of target sense verification (TSV). Building upon a novel hypothesis on
the equivalence of sense and meaning distinctions, we demonstrate through the
application of tools from theoretical computer science that these three
semantic classification problems can be pairwise reduced to each other, and
therefore are equivalent. The results of experiments that involve systems and
datasets for both WiC and WSD provide strong empirical evidence that our
problem reductions work in practice.

本文提出了关于 Word-in-Context （WiC）和 word sense disambiguation（WSD）的新假设，并应用理论计算机科学工具表明了 WiC、WSD 和 target sense verification（TSV）三个语义分类问题之间的等价关系，通过实验证明了问题的彼此转化的可行性。

WiC = TSV = WSD：三个语义任务的等价性

WiC = TSV = WSD: On the Equivalence of Three Semantic Tasks

Word Sense Disambiguation (WSD) aims to identify the correct meaning of
polysemous words in the particular context. Lexical resources like WordNet
which are proved to be of great help for WSD in the knowledge-based methods.
However, previous neural networks for WSD always rely on massive labeled data
(context), ignoring lexical resources like glosses (sense definitions). In this
paper, we integrate the context and glosses of the target word into a unified
framework in order to make full use of both labeled data and lexical knowledge.
Therefore, we propose GAS: a gloss-augmented WSD neural network which jointly
encodes the context and glosses of the target word. GAS models the semantic
relationship between the context and the gloss in an improved memory network
framework, which breaks the barriers of the previous supervised methods and
knowledge-based methods. We further extend the original gloss of word sense via
its semantic relations in WordNet to enrich the gloss information. The
experimental results show that our model outperforms the state-of-theart
systems on several English all-words WSD datasets.

该论文提出了一种新的神经网络模型 GAS，在词汇资源和上下文信息的共同作用下实现了更好的词义消歧效果。