Automatically scoring metaphor novelty is an unexplored topic in natural language processing, and research in this area could benefit a wide range of NLP tasks. However, no publicly available metaphor novelty datasets currently exist, making it difficult to perform research on this topic. We introduce a large corpus of metaphor novelty scores for syntactically related word pairs, and release it freely to the research community. We describe the corpus here, and include an analysis of its score distribution and the types of word pairs included in the corpus. We also provide a brief overview of standard metaphor detection corpora, to provide the reader with greater context regarding how this corpus compares to other datasets used for different types of computational metaphor processing. Finally, we establish a performance benchmark to which future researchers can compare, and show that it is possible to learn to score metaphor novelty on our dataset at a rate ignificantly better than chance or naive strategies.
@InProceedings{PARDE18.242, author = {Natalie Parde and Rodney Nielsen}, title = "{A Corpus of Metaphor Novelty Scores for Syntactically-Related Word Pairs}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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