ICSI researchers and their collaborators have developed a way to statistically compare two of the most widely used lexical resources for English, FrameNet and WordNet. With the Expected Jaccard Index, researchers can for the first time empirically quantify agreement between WordNet and FrameNet expert annotations of words and phrases pulled from real-world texts. Comparing the annotations helps researchers find problematic gaps in the data and align the resources so they can be used together.
ICSI researchers and their collaborators have developed an algorithm that estimates where random Internet videos were shot by training itself on both videos for which locations have already been identified and those for which they haven’t. It’s the first multimodal location estimation method to use both training and test data to improve its results and, in some case, is over 10 percent more accurate than existing methods. The improvement is particularly high when few training videos are available.
ICSI researchers and their collaborators have developed a way to statistically compare two of the most widely used lexical resources for English, FrameNet and WordNet. With the Expected Jaccard Index, researchers can for the first time empirically quantify agreement between WordNet and FrameNet expert annotations of words and phrases pulled from real-world texts. Comparing the annotations helps researchers find problematic gaps in the data and align the resources so they can be used together.
ICSI researchers and their collaborators have developed an algorithm that estimates where random Internet videos were shot by training itself on both videos for which locations have already been identified and those for which they haven’t. It’s the first multimodal location estimation method to use both training and test data to improve its results and, in some case, is over 10 percent more accurate than existing methods. The improvement is particularly high when few training videos are available.