Studies on conjunctions used by Chinese English as a Foreign Language(EFL)learners over the past ten years have focused mainly on the use of conjunctions in argumentative writing,and there is little empirical work on ...Studies on conjunctions used by Chinese English as a Foreign Language(EFL)learners over the past ten years have focused mainly on the use of conjunctions in argumentative writing,and there is little empirical work on conjunction“and”in narrative writing.The purpose of this paper is to explore the characteristics of the semantic relations of“and”used in the narrative writing of Chinese EFL learners from the perspective of text coherence.Through analysis of narrative writing of 29 sophomores,this study investigates the characteristics of semantic relations expressed by the conjunction“and”and the differences in the use of semantic relations of“and”between high-score and low-score writing.The results show different frequencies of the use of semantic relations of“and”.ELF learners prefer to use the term“and”to build progressive relation and parallel relation more than any other relation.Both high-score and low-score writing use a sizable number of“and”to build progressive relation and parallel relation,but high-score writing obviously contains more guiding relations and fewer supplementary relations.These findings have some pedagogical implications for teaching transitions.展开更多
The joint entity relation extraction model which integrates the semantic information of relation is favored by relevant researchers because of its effectiveness in solving the overlapping of entities,and the method of...The joint entity relation extraction model which integrates the semantic information of relation is favored by relevant researchers because of its effectiveness in solving the overlapping of entities,and the method of defining the semantic template of relation manually is particularly prominent in the extraction effect because it can obtain the deep semantic information of relation.However,this method has some problems,such as relying on expert experience and poor portability.Inspired by the rule-based entity relation extraction method,this paper proposes a joint entity relation extraction model based on a relation semantic template automatically constructed,which is abbreviated as RSTAC.This model refines the extraction rules of relation semantic templates from relation corpus through dependency parsing and realizes the automatic construction of relation semantic templates.Based on the relation semantic template,the process of relation classification and triplet extraction is constrained,and finally,the entity relation triplet is obtained.The experimental results on the three major Chinese datasets of DuIE,SanWen,and FinRE showthat the RSTAC model successfully obtains rich deep semantics of relation,improves the extraction effect of entity relation triples,and the F1 scores are increased by an average of 0.96% compared with classical joint extraction models such as CasRel,TPLinker,and RFBFN.展开更多
This paper proposes a tree kernel method of semantic relation detection and classification (RDC) between named entities. It resolves two critical problems in previous tree kernel methods of RDC. First, a new tree ke...This paper proposes a tree kernel method of semantic relation detection and classification (RDC) between named entities. It resolves two critical problems in previous tree kernel methods of RDC. First, a new tree kernel is presented to better capture the inherent structural information in a parse tree by enabling the standard convolution tree kernel with context-sensitiveness and approximate matching of sub-trees. Second, an enriched parse tree structure is proposed to well derive necessary structural information, e.g., proper latent annotations, from a parse tree. Evaluation on the ACE RDC corpora shows that both the new tree kernel and the enriched parse tree structure contribute significantly to RDC and our tree kernel method much outperforms the state-of-the-art ones.展开更多
文摘Studies on conjunctions used by Chinese English as a Foreign Language(EFL)learners over the past ten years have focused mainly on the use of conjunctions in argumentative writing,and there is little empirical work on conjunction“and”in narrative writing.The purpose of this paper is to explore the characteristics of the semantic relations of“and”used in the narrative writing of Chinese EFL learners from the perspective of text coherence.Through analysis of narrative writing of 29 sophomores,this study investigates the characteristics of semantic relations expressed by the conjunction“and”and the differences in the use of semantic relations of“and”between high-score and low-score writing.The results show different frequencies of the use of semantic relations of“and”.ELF learners prefer to use the term“and”to build progressive relation and parallel relation more than any other relation.Both high-score and low-score writing use a sizable number of“and”to build progressive relation and parallel relation,but high-score writing obviously contains more guiding relations and fewer supplementary relations.These findings have some pedagogical implications for teaching transitions.
基金supported by the National Natural Science Foundation of China(Nos.U1804263,U1736214,62172435)the Zhongyuan Science and Technology Innovation Leading Talent Project(No.214200510019).
文摘The joint entity relation extraction model which integrates the semantic information of relation is favored by relevant researchers because of its effectiveness in solving the overlapping of entities,and the method of defining the semantic template of relation manually is particularly prominent in the extraction effect because it can obtain the deep semantic information of relation.However,this method has some problems,such as relying on expert experience and poor portability.Inspired by the rule-based entity relation extraction method,this paper proposes a joint entity relation extraction model based on a relation semantic template automatically constructed,which is abbreviated as RSTAC.This model refines the extraction rules of relation semantic templates from relation corpus through dependency parsing and realizes the automatic construction of relation semantic templates.Based on the relation semantic template,the process of relation classification and triplet extraction is constrained,and finally,the entity relation triplet is obtained.The experimental results on the three major Chinese datasets of DuIE,SanWen,and FinRE showthat the RSTAC model successfully obtains rich deep semantics of relation,improves the extraction effect of entity relation triples,and the F1 scores are increased by an average of 0.96% compared with classical joint extraction models such as CasRel,TPLinker,and RFBFN.
基金Supported by the National Natural Science Foundation of China under Grant Nos.60873150,60970056 and 90920004
文摘This paper proposes a tree kernel method of semantic relation detection and classification (RDC) between named entities. It resolves two critical problems in previous tree kernel methods of RDC. First, a new tree kernel is presented to better capture the inherent structural information in a parse tree by enabling the standard convolution tree kernel with context-sensitiveness and approximate matching of sub-trees. Second, an enriched parse tree structure is proposed to well derive necessary structural information, e.g., proper latent annotations, from a parse tree. Evaluation on the ACE RDC corpora shows that both the new tree kernel and the enriched parse tree structure contribute significantly to RDC and our tree kernel method much outperforms the state-of-the-art ones.