面向无监督词义消歧的上下文感知语义相似度计算
Context-Aware Semantic Similarity Measurement for Unsupervised Word Sense Disambiguation
AI总结：
针对无监督词义消歧任务，提出一种将上下文信息融入语义相似度计算的新方法，在基准数据集上的表现优于现有多种最优技术，验证了上下文信息对无监督词义消歧的重要作用。
摘要：
词义歧义问题是自然语言处理领域的一大挑战，因为可供机器学习模型使用的标注数据十分稀缺，难以应对这一难题。为此，研究人员开发了无监督词义消歧方法，无需依赖标注数据即可解决该问题。本研究提出了一种新的上下文感知无监督词义消歧方法，提供了一种将上下文信息融入相似度计算过程的灵活机制。我们在一个常用基准数据集上开展实验，评估所提策略的性能，并将其与当前最优的无监督词义消歧技术进行对比。实验结果表明，我们的方法大幅提升了消歧准确率，性能超过了多种现有技术。研究结果凸显了在语义相似度计算中整合上下文信息，对于在无监督场景下有效处理词义歧义的重要性。
英文摘要：
The issue of word sense ambiguity poses a significant challenge in natural language processing due to the scarcity of annotated data to feed machine learning models to face the challenge. Therefore, unsupervised word sense disambiguation methods have been developed to overcome that challenge without relying on annotated data. This research proposes a new context-aware approach to unsupervised word sense disambiguation, which provides a flexible mechanism for incorporating contextual information into the similarity measurement process. We experiment with a popular benchmark dataset to evaluate the proposed strategy and compare its performance with state-of-the-art unsupervised word sense disambiguation techniques. The experimental results indicate that our approach substantially enhances disambiguation accuracy and surpasses the performance of several existing techniques. Our findings underscore the significance of integrating contextual information in semantic similarity measurements to manage word sense ambiguity in unsupervised scenarios effectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
