@inproceedings{9d8dd722b4fb4e7eb3dd594ae6a10616,
title = "Production of Large Analogical Clusters from Smaller Example Seed Clusters Using Word Embeddings",
abstract = "We introduce a method to automatically produce large analogical clusters from smaller seed clusters of representative examples. The method is based on techniques of processing and solving analogical equations in word vector space models, i.e., word embeddings. In our experiments, we use standard data sets in English which cover different relations extending from derivational morphology (like adjective–adverb, positive–comparative forms of adjectives) or inflectional morphology (like present–past forms) to encyclopedic semantics (like country–capital relations). The analogical clusters produced by our method are shown to be of reasonably good quality, as shown by comparing human judgment against automatic NDCG@n scores. In total, they contain 8.5 times as many relevant word pairs as the seed clusters.",
keywords = "Analogical clusters, Analogy, Word embeddings",
author = "Yuzhong Hong and Yves Lepage",
year = "2018",
month = jan,
day = "1",
doi = "10.1007/978-3-030-01081-2_36",
language = "English",
isbn = "9783030010805",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "548--562",
editor = "Cox, {Michael T.} and Peter Funk and Shahina Begum",
booktitle = "Case-Based Reasoning Research and Development - 26th International Conference, ICCBR 2018, Proceedings",
note = "26th International Conference on Case-Based Reasoning, ICCBR 2018 ; Conference date: 09-07-2018 Through 12-07-2018",
}