Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describes FCICU team participation in SemEval 2015 for Semantic Textual Similarity challenge. Our main contribution is to propose a word-sense similarity method using BabelNet relationships. In the English subtask challenge,  submitted three systems (runs) to assess the proposed method. In Run1,  used our proposed method coupled with a string kernel mapping function to calculate the textual similarity. In Run2,  used the method with a tree kernel function. In Run3,  averaged Run1 with a previously proposed surface-based approach as a kind of integration. The three runs are ranked 41 st , 57 th , and 20 th of 73 systems, with mean correlation 0.702, 0.597, and 0.759 respectively. For the interpretable task,  submitted a modified version of Run1 achieving mean F1 0.846, 0.461, 0.722, and 0.44 for alignment, type, score, and score with type respectively.
['Basma Hassan', 'Samir AbdelRahman', 'Reem Bahgat']
I am writing a research paper on a idea FCICU: The Integration between Sense-Based Kernel and Surface-Based Methods to Measure Semantic Textual Similarity
[{"from": "human", "value": "I am writing a research paper on a idea FCICU: The Integration between Sense-Based Kernel and Surface-Based Methods to Measure Semantic Textual Similarity"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describes FCICU team participation in SemEval 2015 for Semantic Textual Similarity challenge. Our main contribution is to propose a word-sense similarity method using BabelNet relationships. In the English subtask challenge,  submitted three systems (runs) to assess the proposed method. In Run1,  used our proposed method coupled with a string kernel mapping function to calculate the textual similarity. In Run2,  used the method with a tree kernel function. In Run3,  averaged Run1 with a previously proposed surface-based approach as a kind of integration. The three runs are ranked 41 st , 57 th , and 20 th of 73 systems, with mean correlation 0.702, 0.597, and 0.759 respectively. For the interpretable task,  submitted a modified version of Run1 achieving mean F1 0.846, 0.461, 0.722, and 0.44 for alignment, type, score, and score with type respectively."}]