Weeding out Conventionalized Metaphors: A Corpus of Novel Metaphor Annotations

We encounter metaphors every day, but only a few jump out on us and make us stumble. However, little effort has been devoted to investigating more novel metaphors in comparison to general metaphor detection efforts. We attribute this gap primarily to the lack of larger datasets that distinguish between conventionalized, i.e., very common, and novel metaphors. The goal of this paper is to alleviate this situation by introducing a crowdsourced novel metaphor annotation layer for an existing metaphor corpus. Further, we analyze our corpus and investigate correlations between novelty and features that are typically used in metaphor detection, such as concreteness ratings and more semantic features like the Potential for Metaphoricity. Finally, we present a baseline approach to assess novelty in metaphors based on our annotations.

[1]  Yorick Wilks,et al.  Making Preferences More Active , 1978, Artif. Intell..

[2]  G. Lakoff,et al.  Metaphors We Live by , 1982 .

[3]  Jeremy H. Clear,et al.  The British national corpus , 1993 .

[4]  Tian Zhang,et al.  BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.

[5]  R. Gibbs,et al.  MIP: A method for identifying metaphorically used words in discourse , 2007 .

[6]  Brendan T. O'Connor,et al.  Cheap and Fast – But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks , 2008, EMNLP.

[7]  Simone Teufel,et al.  Metaphor Corpus Annotated for Source - Target Domain Mappings , 2010, LREC.

[8]  Gerard J. Steen,et al.  A method for linguistic metaphor identification : from MIP to MIPVU , 2010 .

[9]  Eduard Hovy,et al.  Identifying Metaphorical Word Use with Tree Kernels , 2013 .

[10]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[11]  Bryan Rink,et al.  Semi-supervised methods for expanding psycholinguistics norms by integrating distributional similarity with the structure of WordNet , 2014, LREC.

[12]  Jonathan Dunn,et al.  Measuring metaphoricity , 2014, ACL.

[13]  Beata Beigman Klebanov,et al.  Different Texts, Same Metaphors: Unigrams and Beyond , 2014 .

[14]  Amy Beth Warriner,et al.  Concreteness ratings for 40 thousand generally known English word lemmas , 2014, Behavior research methods.

[15]  Yulia Tsvetkov,et al.  Metaphor Detection with Cross-Lingual Model Transfer , 2014, ACL.

[16]  Ekaterina Shutova,et al.  Design and Evaluation of Metaphor Processing Systems , 2015, CL.

[17]  Beata Beigman Klebanov,et al.  Supervised Word-Level Metaphor Detection: Experiments with Concreteness and Reweighting of Examples , 2015 .

[18]  Carolyn Penstein Rosé,et al.  Metaphor Detection in Discourse , 2015, SIGDIAL Conference.

[19]  Saif Mohammad,et al.  Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best–Worst Scaling , 2016, NAACL.

[20]  Núria Bel,et al.  Assessing the Potential of Metaphoricity of verbs using corpus data , 2016, LREC.

[21]  Iryna Gurevych,et al.  Crowdsourcing a Large Dataset of Domain-Specific Context-Sensitive Semantic Verb Relations , 2016, LREC.

[22]  Saif Mohammad,et al.  Metaphor as a Medium for Emotion: An Empirical Study , 2016, *SEMEVAL.

[23]  Johannes Bjerva,et al.  Detecting novel metaphor using selectional preference information , 2016 .

[24]  Jean Maillard,et al.  Black Holes and White Rabbits: Metaphor Identification with Visual Features , 2016, NAACL.

[25]  Ekaterina Shutova,et al.  Grasping the Finer Point: A Supervised Similarity Network for Metaphor Detection , 2017, EMNLP.

[26]  Rodney D. Nielsen,et al.  A Corpus of Metaphor Novelty Scores for Syntactically-Related Word Pairs , 2018, LREC.