Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Temporal evidence classification, i.e., finding associations beten temporal expressions and relations expressed in text, is an important part of temporal relation extraction. To capture the variations found in this setting,  employ a distant supervision approach, modeling the task as multi-class text classification. There are two main challenges with distant supervision: (1) noise generated by incorrect heuristic labeling, and (2) distribution mismatch beten the target and distant supervision examples.  are particularly interested in addressing the second problem and propose a sampling approach to handle the distribution mismatch. Our prior-informed distant supervision approach improves over basic distant supervision and outperforms a purely supervised approach when evaluated on TAC-KBP data, both on classification and end-to-end metrics.
['Ridho Reinanda', 'Maarten de Rijke']
I am writing a research paper on a idea Prior-informed Distant Supervision for Temporal Evidence Classification
[{"from": "human", "value": "I am writing a research paper on a idea Prior-informed Distant Supervision for Temporal Evidence Classification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Temporal evidence classification, i.e., finding associations beten temporal expressions and relations expressed in text, is an important part of temporal relation extraction. To capture the variations found in this setting,  employ a distant supervision approach, modeling the task as multi-class text classification. There are two main challenges with distant supervision: (1) noise generated by incorrect heuristic labeling, and (2) distribution mismatch beten the target and distant supervision examples.  are particularly interested in addressing the second problem and propose a sampling approach to handle the distribution mismatch. Our prior-informed distant supervision approach improves over basic distant supervision and outperforms a purely supervised approach when evaluated on TAC-KBP data, both on classification and end-to-end metrics."}]