You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Is your feature request related to a problem? Please describe.
Events are linked together through a variety of temporal structures. The temporal relations are expressed both explicitly, through words like after, and implicitly through inference. Extracting these sorts of temporal structures is crucial for an understanding of the text. Machine reasoning requires an explicit representation of the temporal structure. Such an explicit representation can be formed by identifying specific words or phrases as the event anchors of the structure, and then drawing explicit temporal relation links between the various events. Examples are given below:
Describe the solution you'd like
CTakes used SVM-based temporal relation annotators which achieves an F-score of 0.589. State-of-the-art results for event-time relations were achieved with our neural network approaches. All the annotators were trained and tested on colon cancer notes from the THYME data set. Similar module is expected by using any reliable algorithm. Please find some resources to refer down below.
Is your feature request related to a problem? Please describe.
Events are linked together through a variety of temporal structures. The temporal relations are expressed both explicitly, through words like after, and implicitly through inference. Extracting these sorts of temporal structures is crucial for an understanding of the text. Machine reasoning requires an explicit representation of the temporal structure. Such an explicit representation can be formed by identifying specific words or phrases as the event anchors of the structure, and then drawing explicit temporal relation links between the various events. Examples are given below:
Describe the solution you'd like
CTakes used SVM-based temporal relation annotators which achieves an F-score of 0.589. State-of-the-art results for event-time relations were achieved with our neural network approaches. All the annotators were trained and tested on colon cancer notes from the THYME data set. Similar module is expected by using any reliable algorithm. Please find some resources to refer down below.
Additional Resources
The text was updated successfully, but these errors were encountered: