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Language related issues for machine translation between closely related south Slavic languages
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Synthetic, Yet Natural: Properties of WordNet Random Walk Corpora and the impact of rare words on embedding performance
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In: Conference papers (2019)
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Abstract:
Creating word embeddings that reflect semantic relationships encoded in lexical knowledge resources is an open challenge. One approach is to use a random walk over a knowledge graph to generate a pseudo-corpus and use this corpus to train embeddings. However, the effect of the shape of the knowledge graph on the generated pseudo-corpora, and on the resulting word embeddings, has not been studied. To explore this, we use English WordNet, constrained to the taxonomic (tree-like) portion of the graph, as a case study. We investigate the properties of the generated pseudo-corpora, and their impact on the resulting embeddings. We find that the distributions in the psuedo-corpora exhibit properties found in natural corpora, such as Zipf’s and Heaps’ law, and also ob- serve that the proportion of rare words in a pseudo-corpus affects the performance of its embeddings on word similarity.
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Keyword:
Artificial Intelligence and Robotics; Computational Linguistics; corpus; evaluation; Numerical Analysis and Scientific Computing; random walk; representations; Software Engineering; taxonomy; word embeddings; word similarity; WordNet
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URL: https://arrow.tudublin.ie/scschcomcon/271
https://arrow.tudublin.ie/cgi/viewcontent.cgi?article=1283&context=scschcomcon
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Size Matters: The Impact of Training Size in Taxonomically-Enriched Word Embeddings
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In: Articles (2019)
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