Research on metaphorical language has shown ties between abstractness and
emotionality with regard to metaphoricity; prior work is however limited to the
word and sentence levels, and up to date there is no empirical study
establishing the extent to which this is also true on the discourse level. This
paper explores which textual and perceptual features human annotators perceive
as important for the metaphoricity of discourses and expressions, and addresses
two research questions more specifically. First, is a metaphorically-perceived
discourse more abstract and more emotional in comparison to a
literally-perceived discourse? Second, is a metaphorical expression preceded by
a more metaphorical/abstract/emotional context than a synonymous literal
alternative? We used a dataset of 1,000 corpus-extracted discourses for which
crowdsourced annotators (1) provided judgements on whether they perceived the
discourses as more metaphorical or more literal, and (2) systematically listed
lexical terms which triggered their decisions in (1). Our results indicate that
metaphorical discourses are more emotional and to a certain extent more
abstract than literal discourses. However, neither the metaphoricity nor the
abstractness and emotionality of the preceding discourse seem to play a role in
triggering the choice between synonymous metaphorical vs. literal expressions.
Our dataset is available at
this https URL.

本研究探讨心理学领域中隐喻言语的抽象性和情感性如何影响篇章层面的隐喻性，并通过语料抽取和众包实验的方式，揭示人类注释者在判断隐喻表达方式及其的原因时使用的文本和感性特征。研究结果表明，与直喻相比，隐喻篇章更加情绪化和抽象化，而前文段中的隐喻性、抽象性和情感性并未对隐喻表达方式的选择产生影响。

话语层面感知隐喻性的特征：抽象性和情感性

Features of Perceived Metaphoricity on the Discourse Level: Abstractness and Emotionality

We present end-to-end neural models for detecting metaphorical word use in
context. We show that relatively standard BiLSTM models which operate on
complete sentences work well in this setting, in comparison to previous work
that used more restricted forms of linguistic context. These models establish a
new state-of-the-art on existing verb metaphor detection benchmarks, and show
strong performance on jointly predicting the metaphoricity of all words in a
running text.

本文提出端到端的神经模型，用于检测上下文中拟喻词的使用。结果显示，相较于先前使用更受限的语言环境的研究，针对完整句子进行操作的相对标准的 BiLSTM 模型在这种情况下表现良好。这些模型在现有的动词拟喻检测基准上建立了一个新的最优状态，并在同时预测运行文本中的所有词汇的拟喻性能方面显示了强大的性能。