What and how we translate are questions often argued about. No matter what kind of answers one may give, priority in translation should be granted to meaning, especially those meanings that exist in all concerned lang...What and how we translate are questions often argued about. No matter what kind of answers one may give, priority in translation should be granted to meaning, especially those meanings that exist in all concerned languages. This research defines them as universal sememes, and the study of them as universal semantics, of which applications are also briefly looked into.展开更多
A sememe is defined as the minimum semantic unit of languages in linguistics.Sememe knowledge bases are built by manually annotating sememes for words and phrases.HowNet is the most well-known sememe knowledge base.It...A sememe is defined as the minimum semantic unit of languages in linguistics.Sememe knowledge bases are built by manually annotating sememes for words and phrases.HowNet is the most well-known sememe knowledge base.It has been extensively utilized in many natural language processing tasks in the era of statistical natural language processing and proven to be effective and helpful to understanding and using languages.In the era of deep learning,although data are thought to be of vital importance,there are some studies working on incorporating sememe knowledge bases like HowNet into neural network models to enhance system performance.Some successful attempts have been made in the tasks including word representation learning,language modeling,semantic composition,etc.In addition,considering the high cost of manual annotation and update for sememe knowledge bases,some work has tried to use machine learning methods to automatically predict sememes for words and phrases to expand sememe knowledge bases.Besides,some studies try to extend HowNet to other languages by automatically predicting sememes for words and phrases in a new language.In this paper,we summarize recent studies on application and expansion of sememe knowledge bases and point out some future directions of research on sememes.展开更多
文摘What and how we translate are questions often argued about. No matter what kind of answers one may give, priority in translation should be granted to meaning, especially those meanings that exist in all concerned languages. This research defines them as universal sememes, and the study of them as universal semantics, of which applications are also briefly looked into.
基金the National Key Research and Development Program of China(2018 YFB1004503)the National Natural Science Foundation of China(NSFC Grant Nos.61732008,61532010).
文摘A sememe is defined as the minimum semantic unit of languages in linguistics.Sememe knowledge bases are built by manually annotating sememes for words and phrases.HowNet is the most well-known sememe knowledge base.It has been extensively utilized in many natural language processing tasks in the era of statistical natural language processing and proven to be effective and helpful to understanding and using languages.In the era of deep learning,although data are thought to be of vital importance,there are some studies working on incorporating sememe knowledge bases like HowNet into neural network models to enhance system performance.Some successful attempts have been made in the tasks including word representation learning,language modeling,semantic composition,etc.In addition,considering the high cost of manual annotation and update for sememe knowledge bases,some work has tried to use machine learning methods to automatically predict sememes for words and phrases to expand sememe knowledge bases.Besides,some studies try to extend HowNet to other languages by automatically predicting sememes for words and phrases in a new language.In this paper,we summarize recent studies on application and expansion of sememe knowledge bases and point out some future directions of research on sememes.
