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公开(公告)号：US09342233B1
公开(公告)日：2016-05-17
申请号：US13452305
申请日：2012-04-20
IPC分类号： G06F3/048 , G06F3/0488 , G06F17/27
CPC分类号： G06F3/0488 , G06F17/2735
摘要： In some implementations, a user of an electronic device may select a term of one or more words from the text of a displayed digital work. In response, a dictionary module may select a particular dictionary to access from among a plurality of dictionaries available on the electronic device. For example, based at least in part on metadata corresponding to the displayed digital work, the dictionary module may select a dictionary to access from a hierarchy of dictionaries. The dictionary module may present a definition obtained from the selected dictionary in a dictionary interface displayed over the presentation of the digital work. Further, the dictionary module may identify a location of at least one other occurrence of the selected term in the digital work or in a different digital work, and may display an excerpt of text including the at least one other occurrence of the selected term.
摘要翻译： 在一些实现中，电子设备的用户可以从显示的数字作品的文本中选择一个或多个单词的术语。 作为响应，字典模块可以从电子设备上可用的多个字典中选择特定字典以访问。 例如，至少部分地基于对应于所显示的数字作品的元数据，字典模块可以选择字典以从词典的层次结构访问。 字典模块可以呈现从在数字作品的呈现上显示的字典界面中从所选字典获得的定义。 此外，字典模块可以识别在数字作业中或在不同的数字作品中所选项的至少一个其他出现的位置，并且可以显示包括所选项的至少一个其他出现的文本的摘录。
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公开(公告)号：US09852215B1
公开(公告)日：2017-12-26
申请号：US13624628
申请日：2012-09-21
申请人： Todd H. Sullivan , Thomas F. Dimson
发明人： Todd H. Sullivan , Thomas F. Dimson
CPC分类号： G06F17/30731 , G06F17/30663 , G06F17/30699 , G06N99/005
摘要： A body of text may be compared with one or more user-selected text portions to rank a plurality of text portions of the body of text, such as for predicting which of the text portions are likely to be annotated by users. As one example, the text of a content item may be compared with excerpts of other content items that have been highlighted or otherwise annotated by a plurality of users. Based at least in part on the comparison, some implementations identify one or more portions of text of the content item that are likely to be selected or highlighted by users that access the content item. In some examples, a classifier may be trained based on popular highlights determined for a plurality of content items. The classifier may be applied to a body of text to determine portions that users are likely to consider profound or interesting.
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