Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Event extraction from texts aims to detect structured information such as what has happened, to whom, where and when. Event extraction and visualization are typically considered as two different tasks. ,  propose a novel approach based on probabilistic modelling to jointly extract and visualize events from tets where both tasks benefit from each other.  model each event as a joint distribution over named entities, a date, a location and event-related keywords. Moreover, both tets and event instances are associated with coordinates in the visualization space. The manifold assumption that the intrinsic geometry of tets is a low-rank, non-linear manifold within the high-dimensional space is incorporated into the learning framework using a regularization. Experimental results show that the proposed approach can effectively deal with both event extraction and visualization and performs remarkably better than both the state-of-the-art event extraction method and a pipeline approach for event extraction and visualization.
['Deyu Zhou', 'Tianmeng Gao', 'Yulan He']
I am writing a research paper on a idea Jointly Event Extraction and Visualization on Twitter via Probabilistic Modelling.
[{"from": "human", "value": "I am writing a research paper on a idea Jointly Event Extraction and Visualization on Twitter via Probabilistic Modelling."}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Event extraction from texts aims to detect structured information such as what has happened, to whom, where and when. Event extraction and visualization are typically considered as two different tasks. ,  propose a novel approach based on probabilistic modelling to jointly extract and visualize events from tets where both tasks benefit from each other.  model each event as a joint distribution over named entities, a date, a location and event-related keywords. Moreover, both tets and event instances are associated with coordinates in the visualization space. The manifold assumption that the intrinsic geometry of tets is a low-rank, non-linear manifold within the high-dimensional space is incorporated into the learning framework using a regularization. Experimental results show that the proposed approach can effectively deal with both event extraction and visualization and performs remarkably better than both the state-of-the-art event extraction method and a pipeline approach for event extraction and visualization."}]