Abstract: Implementations are provided for automatically mining corpus(es) of electronic video files for video clips that contain spoken utterances that are suitable usage examples to accompany or compliment dictionary definitions. These video clips may then be associated with target n-grams in a searchable database, such as a database underlying an online dictionary. In various implementations, a set of candidate video clips in which a target n-gram is uttered in a target context may be identified from a corpus of electronic video files. For each candidate video clip of the set, pre-existing manual subtitles associated with the candidate video clip may be compared to text generated based on speech recognition processing of an audio portion of the candidate video clip. Based at least in part on the comparing, a measure of suitability as a dictionary usage example may be calculated for the candidate video clip.

Abstract: Implementations are provided for automatically mining corpus(es) of electronic video files for video clips that contain spoken utterances that are suitable usage examples to accompany or compliment dictionary definitions. These video clips may then be associated with target n-grams in a searchable database, such as a database underlying an online dictionary. In various implementations, a set of candidate video clips in which a target n-gram is uttered in a target context may be identified from a corpus of electronic video files. For each candidate video clip of the set, pre-existing manual subtitles associated with the candidate video clip may be compared to text generated based on speech recognition processing of an audio portion of the candidate video clip. Based at least in part on the comparing, a measure of suitability as a dictionary usage example may be calculated for the candidate video clip.

Abstract: Implementations are provided for automatically mining corpus(es) of electronic video files for video clips that contain spoken utterances that are suitable usage examples to accompany or compliment dictionary definitions. These video clips may then be associated with target n-grams in a searchable database, such as a database underlying an online dictionary. In various implementations, a set of candidate video clips in which a target n-gram is uttered in a target context may be identified from a corpus of electronic video files. For each candidate video clip of the set, pre-existing manual subtitles associated with the candidate video clip may be compared to text generated based on speech recognition processing of an audio portion of the candidate video clip. Based at least in part on the comparing, a measure of suitability as a dictionary usage example may be calculated for the candidate video clip.

Abstract: Technology described herein identifies and demotes query completions which divert users from their original search intent. The identified query completions may represent informational requests that are amusing diversions for some users, but which are not relevant to the actual information of interest for many users. For example, the identified query completions may represent information requests that some users may find humorous or entertaining, but which are not relevant to actual information of interest for many other users. The identification of these query completions may allow alternative query completions to be provided that can more likely assist users in finding the information they actually seek.