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In contrast, we experiment with weakly supervised and unsupervised techniques\u2014with little or no annotation\u2014to generalize higher-level mechanisms of metaphor from distributional properties of concepts. We investigate different levels and types of supervision (learning from linguistic examples vs. learning from a given set of metaphorical mappings vs. learning without annotation) in flat and hierarchical, unconstrained and constrained clustering settings. Our aim is to identify the optimal type of supervision for a learning algorithm that discovers patterns of metaphorical association from text. In order to investigate the scalability and adaptability of our models, we applied them to data in three languages from different language groups\u2014English, Spanish, and Russian\u2014achieving state-of-the-art results with little supervision. Finally, we demonstrate that statistical methods can facilitate and scale up cross-linguistic research on metaphor.<\/jats:p>","DOI":"10.1162\/coli_a_00275","type":"journal-article","created":{"date-parts":[[2016,12,14]],"date-time":"2016-12-14T19:29:07Z","timestamp":1481743747000},"page":"71-123","source":"Crossref","is-referenced-by-count":31,"title":["Multilingual Metaphor Processing: Experiments with Semi-Supervised and Unsupervised Learning"],"prefix":"10.1162","volume":"43","author":[{"given":"Ekaterina","family":"Shutova","sequence":"first","affiliation":[{"name":"University of Cambridge"}]},{"given":"Lin","family":"Sun","sequence":"additional","affiliation":[{"name":"Greedy Intelligence"}]},{"given":"Elkin Dar\u00edo","family":"Guti\u00e9rrez","sequence":"additional","affiliation":[{"name":"University of California, San Diego"}]},{"given":"Patricia","family":"Lichtenstein","sequence":"additional","affiliation":[{"name":"University of California, Merced"}]},{"given":"Srini","family":"Narayanan","sequence":"additional","affiliation":[{"name":"Google Research"}]}],"member":"281","reference":[{"key":"bib1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuropsychologia.2012.01.028"},{"key":"bib2","unstructured":"Badryzlova, Yulia, Natalia Shekhtman, Yekaterina Isaeva, and Ruslan Kerimov. 2013. 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