时间表征对隐喻识别的影响
On the Impact of Temporal Representations on Metaphor Detection
摘要：
现有的隐喻检测方法使用基于神经网络的隐喻分类器来比较它们的字面意义或核心意义以及它们的上下文意义。然而，由于文化和社会的影响，隐喻表达方式随着时间的推移而演变。人们知道隐喻表达与语言和字面词义共同进化，甚至在某种程度上推动了这种进化。这就提出了一个问题，即不同的，可能是特定时间的，字面意义的表征是否会影响隐喻检测任务。据我们所知，这是第一个研究，探讨隐喻检测任务，详细的探索性分析，不同的时间和静态词嵌入被用来解释不同的表征字面意义。我们的实验分析是基于三个流行的基准用于隐喻检测和词嵌入提取不同的语料库和时间对齐使用不同的国家的最先进的方法。结果表明，不同的静态词嵌入方法的使用确实影响隐喻检测任务，一些时间词嵌入略优于静态方法。然而，研究结果也表明，时间词嵌入可能提供表征的核心意义的隐喻，甚至太接近他们的上下文意义，从而混淆分类。总的来说，时间语言进化和隐喻检测之间的相互作用在我们的实验中使用的基准数据集似乎很小。这表明，未来对这一重要语言现象的计算分析工作应该首先从创建一个新的数据集开始，在这个数据集上更好地表示这种相互作用。
英文摘要：
State-of-the-art approaches for metaphor detection compare their literal - or core - meaning and their contextual meaning using metaphor classifiers based on neural networks. However, metaphorical expressions evolve over time due to various reasons, such as cultural and societal impact. Metaphorical expressions are known to co-evolve with language and literal word meanings, and even drive, to some extent, this evolution. This poses the question of whether different, possibly time-specific, representations of literal meanings may impact the metaphor detection task. To the best of our knowledge, this is the first study that examines the metaphor detection task with a detailed exploratory analysis where different temporal and static word embeddings are used to account for different representations of literal meanings. Our experimental analysis is based on three popular benchmarks used for metaphor detection and word embeddings extracted from different corpora and temporally aligned using different state-of-the-art approaches. The results suggest that the usage of different static word embedding methods does impact the metaphor detection task and some temporal word embeddings slightly outperform static methods. However, the results also suggest that temporal word embeddings may provide representations of the core meaning of the metaphor even too close to their contextual meaning, thus confusing the classifier. Overall, the interaction between temporal language evolution and metaphor detection appears tiny in the benchmark datasets used in our experiments. This suggests that future work for the computational analysis of this important linguistic phenomenon should first start by creating a new dataset where this interaction is better represented.
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。
