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Traditional word segmentation techniques attempt to discern the word units accurately from texts; however, they are unable to appropriately and efficiently identify all new words. Identification of new words, especially in languages such as Chinese, remains a challenge. In recent years, word embedding methods have used numerical word vectors to retain the semantic and correlated information between words in a corpus. In this article, we propose the word-embedding-based method (WEBM), a novel method that combines word embedding and frequent n-gram string mining for discovering new words from domain corpora. First, we mapped all word units in a domain corpus to a high-dimension word vector space. Second, we used a frequent n-gram word string mining method to identify a set of candidates for new words. We designed a pruning strategy based on the word vectors to quantify the possibility of a word string being a new word, thereby allowing the evaluation of candidates based on the similarity of word units in the same string. In a comparative study, our experimental results revealed that WEBM had a great advantage in detecting new words from massive Chinese corpora.<\/jats:p>","DOI":"10.1177\/0165551518786676","type":"journal-article","created":{"date-parts":[[2018,7,10]],"date-time":"2018-07-10T08:50:40Z","timestamp":1531212640000},"page":"196-211","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":22,"title":["Detecting new Chinese words from massive domain texts with word embedding"],"prefix":"10.1177","volume":"45","author":[{"given":"Yu","family":"Qian","sequence":"first","affiliation":[{"name":"School of Management and Economics, University of Electronic Science and Technology of China, P.R. 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