In this paper,we study cross-domain relation extraction.Since new data mapping to feature spaces always differs from the previously seen data due to a domain shif,few-shot relation extraction often perform poorly.To s...In this paper,we study cross-domain relation extraction.Since new data mapping to feature spaces always differs from the previously seen data due to a domain shif,few-shot relation extraction often perform poorly.To solve the problems caused by cross-domain,we propose a method for combining the pure entity,relation labels and adversarial(PERLA).We first use entities and complete sentences for separate encoding to obtain context-independent entity features.Then,we combine relation labels which are useful for relation extraction to mitigate context noise.We combine adversarial to reduce the noise caused by cross-domain.We conducted experiments on the publicly available cross-domain relation extraction dataset Fewrel 2.o[1]o,and the results show that our approach improves accuracy and has better transferability for better adaptation to cross-domain tasks.展开更多
In this paper,we present a relation matrix description of temporal relations.Based on this modela new algorithm for labelling temporal relations is proposed.Under certain conditions the algorithm iscompleted and has a...In this paper,we present a relation matrix description of temporal relations.Based on this modela new algorithm for labelling temporal relations is proposed.Under certain conditions the algorithm iscompleted and has a polynomial complexity.In general cases it is still an efficient algorithm comparedto some known algorithms.展开更多
基金The State Key Program of National Natural Science of China,Grant/Award Number:61533018National Natural Science Foundation of China,Grant/Award Number:61402220+2 种基金The Philosophy and Social Science Foundation of Hunan Province,Grant/Award Number:16YBA323Natural Science Foundation of Hunan Province,Grant/Award Number:2020J4525,2022J30495Scientific Research Fund of Hunan Provincial Education Department,Grant/Award Number:18B279,19A439.
文摘In this paper,we study cross-domain relation extraction.Since new data mapping to feature spaces always differs from the previously seen data due to a domain shif,few-shot relation extraction often perform poorly.To solve the problems caused by cross-domain,we propose a method for combining the pure entity,relation labels and adversarial(PERLA).We first use entities and complete sentences for separate encoding to obtain context-independent entity features.Then,we combine relation labels which are useful for relation extraction to mitigate context noise.We combine adversarial to reduce the noise caused by cross-domain.We conducted experiments on the publicly available cross-domain relation extraction dataset Fewrel 2.o[1]o,and the results show that our approach improves accuracy and has better transferability for better adaptation to cross-domain tasks.
文摘In this paper,we present a relation matrix description of temporal relations.Based on this modela new algorithm for labelling temporal relations is proposed.Under certain conditions the algorithm iscompleted and has a polynomial complexity.In general cases it is still an efficient algorithm comparedto some known algorithms.
