{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-meta-embeddings-by-using-ensembles","title":"Learning Meta-Embeddings by Using Ensembles of Embedding Sets","arxiv_id":"1508.04257","date":"2015-08-18","proceeding":null,"authors":["Wenpeng Yin","Hinrich Schütze"],"abstract":"Word embeddings -- distributed representations of words -- in deep learning\nare beneficial for many tasks in natural language processing (NLP). However,\ndifferent embedding sets vary greatly in quality and characteristics of the\ncaptured semantics. Instead of relying on a more advanced algorithm for\nembedding learning, this paper proposes an ensemble approach of combining\ndifferent public embedding sets with the aim of learning meta-embeddings.\nExperiments on word similarity and analogy tasks and on part-of-speech tagging\nshow better performance of meta-embeddings compared to individual embedding\nsets. One advantage of meta-embeddings is the increased vocabulary coverage. We\nwill release our meta-embeddings publicly.","url_abs":"http://arxiv.org/abs/1508.04257v2","url_pdf":"http://arxiv.org/pdf/1508.04257v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-meta-embeddings-by-using-ensembles","repo_url":"https://github.com/Shujian2015/meta-embedding-paper-list","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"part-of-speech-tagging","task_name":"Part-Of-Speech Tagging"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"},{"task_slug":"word-similarity","task_name":"Word Similarity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1508.04257","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}