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.
翻译:用来比较其字面或核心含义和背景含义的隐喻式比喻式方法, 比较其字面或核心含义及其背景含义。 但是, 隐喻式表达由于文化和社会影响等各种原因, 随着时间的推移, 隐喻式表达方式会演变。 代词表达方式已知与语言和字面文字含义共同演变, 甚至在某种程度上推动这种演变。 这就提出了不同、 可能具体时间、 字面含义表达方式是否会影响隐喻式检测任务的问题。 对我们的最佳了解而言, 这是第一次研究, 以详细的探索性分析来研究隐喻式检测任务, 使用不同的时间和静态字嵌入方式来计算不同的字面含义。 我们的实验性分析基于三种流行的基准, 用于隐喻性检测和字面文字嵌入, 并使用不同的状态方法。 结果表明, 使用不同的静态词嵌入式表达方式确实影响隐喻式检测任务和一些时间单词嵌入略偏差的静态方法。 然而, 实验结果还表明, 隐喻性比喻式比喻性分析的比喻性分析, 因此, 隐喻性分析中, 的比喻性分析, 的比喻性分析, 意味着, 这个比喻性分析, 的比喻性分析, 的比喻性分析, 意味着, 的比喻性分析, 的比喻性分析, 的比喻性分析, 意味着,,,, 的比喻性分析, 更接近性分析, 的比喻性分析, 的比喻性分析, 的比喻性分析, 的比喻性分析, 的比喻性分析, 的比喻性分析, 的比喻性分析, 的,, 的,,,, 的, 的 的 的 的 的 的 的 的 的 的,,,,,, 的,,,,,, 的,,,,,,,, 的,,,,,,,,,,,,,,,, 更接近,,,,