{"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/how-to-generate-a-good-word-embedding","title":"How to Generate a Good Word Embedding?","arxiv_id":"1507.05523","date":"2015-07-20","proceeding":null,"authors":["Siwei Lai","Kang Liu","Liheng Xu","Jun Zhao"],"abstract":"We analyze three critical components of word embedding training: the model,\nthe corpus, and the training parameters. We systematize existing\nneural-network-based word embedding algorithms and compare them using the same\ncorpus. We evaluate each word embedding in three ways: analyzing its semantic\nproperties, using it as a feature for supervised tasks and using it to\ninitialize neural networks. We also provide several simple guidelines for\ntraining word embeddings. First, we discover that corpus domain is more\nimportant than corpus size. We recommend choosing a corpus in a suitable domain\nfor the desired task, after that, using a larger corpus yields better results.\nSecond, we find that faster models provide sufficient performance in most\ncases, and more complex models can be used if the training corpus is\nsufficiently large. Third, the early stopping metric for iterating should rely\non the development set of the desired task rather than the validation loss of\ntraining embedding.","url_abs":"http://arxiv.org/abs/1507.05523v1","url_pdf":"http://arxiv.org/pdf/1507.05523v1.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":"how-to-generate-a-good-word-embedding","repo_url":"https://github.com/licstar/compare","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"how-to-generate-a-good-word-embedding","repo_url":"https://github.com/domyounglee/NNLM_implementation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[{"method_slug":"early-stopping","method_name":"Early Stopping"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}