重新思考词相似度：通过分类混淆实现语义相似度
Rethinking Word Similarity: Semantic Similarity through Classification Confusion
AI总结：
针对传统词嵌入余弦相似度无法捕捉语义相似度的上下文依赖等特性问题，提出基于分类混淆的Word Confusion度量方法，其匹配人类判断性能与余弦相似度相当，还能测试历史词义变化假设，助力相关领域发展。
摘要：
词相似度在社会科学和文化分析任务中有诸多应用，例如衡量意义随时间的变化以及理解有争议的术语。然而，传统基于词嵌入余弦相似度的方法无法捕捉语义相似度的上下文依赖、不对称和多义性本质。我们提出一种新的相似度度量方法Word Confusion，将语义相似度重构为基于特征的分类混淆问题。该方法受Tversky关于动态选择相似特征建议的启发：训练分类器将上下文嵌入映射到词标识，将分类器混淆（选择混淆词c而非正确目标词t的概率）作为c和t的相似度度量，潜在混淆词集作为所选特征。在MEN、WordSim353和SimLex数据集上，该方法匹配人类相似度判断的性能与余弦相似度相当，且能利用预定感兴趣特征衡量相似度。我们通过应用它测试18世纪法语单词“revolution”在法国大革命期间从“民众行动”到“国家行动”的意义变化假设，展示了其利用动态特征的能力。我们希望这种对语义相似度的重新构想能启发新工具的开发，更好捕捉语言的多面性和动态性，推动计算社会科学、文化分析等领域的发展。
英文摘要：
Word similarity has many applications to social science and cultural analytics tasks like measuring meaning change over time and making sense of contested terms. Yet traditional similarity methods based on cosine similarity between word embeddings cannot capture the context-dependent, asymmetrical, polysemous nature of semantic similarity. We propose a new measure of similarity, Word Confusion, that reframes semantic similarity in terms of feature-based classification confusion. Word Confusion is inspired by Tversky's suggestion that similarity features be chosen dynamically. Here we train a classifier to map contextual embeddings to word identities and use the classifier confusion (the probability of choosing a confounding word c instead of the correct target word t) as a measure of the similarity of c and t. The set of potential confounding words acts as the chosen features. Our method is comparable to cosine similarity in matching human similarity judgments across several datasets (MEN, WirdSim353, and SimLex), and can measure similarity using predetermined features of interest. We demonstrate our model's ability to make use of dynamic features by applying it to test a hypothesis about changes in the 18th C. meaning of the French word "revolution" from popular to state action during the French Revolution. We hope this reimagining of semantic similarity will inspire the development of new tools that better capture the multi-faceted and dynamic nature of language, advancing the fields of computational social science and cultural analytics and beyond.
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