《柯林斯合作英语词典》(Collins Cobuild English LanguageDictionary,下简称《合作》),是继《牛津现代英语高级学生词典》(下简称《牛津》)和《朗曼当代英语词典》(下简称((朗曼》)之后英国辞书界推出的又一教学词典。该词典问世后,...《柯林斯合作英语词典》(Collins Cobuild English LanguageDictionary,下简称《合作》),是继《牛津现代英语高级学生词典》(下简称《牛津》)和《朗曼当代英语词典》(下简称((朗曼》)之后英国辞书界推出的又一教学词典。该词典问世后,很快赢得读者的青睐。它在单词的注音、词义的诠释、义项的取舍。展开更多
Automatic web image annotation is a practical and effective way for both web image retrieval and image understanding. However, current annotation techniques make no further investigation of the statement-level syntact...Automatic web image annotation is a practical and effective way for both web image retrieval and image understanding. However, current annotation techniques make no further investigation of the statement-level syntactic correlation among the annotated words, therefore making it very difficult to render natural language interpretation for images such as "pandas eat bamboo". In this paper, we propose an approach to interpret image semantics through mining the visible and textual information hidden in images. This approach mainly consists of two parts: first the annotated words of target images are ranked according to two factors, namely the visual correlation and the pairwise co-occurrence; then the statement-level syntactic correlation among annotated words is explored and natural language interpretation for the target image is obtained. Experiments conducted on real-world web images show the effectiveness of the proposed approach.展开更多
文摘《柯林斯合作英语词典》(Collins Cobuild English LanguageDictionary,下简称《合作》),是继《牛津现代英语高级学生词典》(下简称《牛津》)和《朗曼当代英语词典》(下简称((朗曼》)之后英国辞书界推出的又一教学词典。该词典问世后,很快赢得读者的青睐。它在单词的注音、词义的诠释、义项的取舍。
基金Project supported by the National Natural Science Foundation of China (Nos 60533090 and 60603096)the National High-Tech Research and Development Program (863) of China (No 2006AA 010107)
文摘Automatic web image annotation is a practical and effective way for both web image retrieval and image understanding. However, current annotation techniques make no further investigation of the statement-level syntactic correlation among the annotated words, therefore making it very difficult to render natural language interpretation for images such as "pandas eat bamboo". In this paper, we propose an approach to interpret image semantics through mining the visible and textual information hidden in images. This approach mainly consists of two parts: first the annotated words of target images are ranked according to two factors, namely the visual correlation and the pairwise co-occurrence; then the statement-level syntactic correlation among annotated words is explored and natural language interpretation for the target image is obtained. Experiments conducted on real-world web images show the effectiveness of the proposed approach.
