Semantic Representations For Domain Adaptation: A Case Study On The Thee Kernel-Based Method For Relation Extraction
PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1(2015)
摘要
We study the application of word embeddings to generate semantic representations for the domain adaptation problem of relation extraction (RE) in the tree kernel-based method. We systematically evaluate various techniques to generate the semantic representations and demonstrate that they are effective to improve the generalization performance of a tree kernel-based relation extractor across domains (up to 7% relative improvement). In addition, we compare the tree kernel-based and the feature-based method for RE in a compatible way, on the same resources and settings, to gain insights into which kind of system is more robust to domain changes. Our results and error analysis shows that the tree kernel-based method outperforms the feature-based approach.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络