Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). An Attention-Based Model Using Character Composition of Entities in Chinese Relation Extraction. Retrieved May 24, 2026, from https://4ort.xyz/entity/an-attention-based-model-using-character-composition-of-entities-in-chinese-relation-extraction
MLA“An Attention-Based Model Using Character Composition of Entities in Chinese Relation Extraction.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/an-attention-based-model-using-character-composition-of-entities-in-chinese-relation-extraction.
BibTeX@misc{4ortxyz_an-attention-based-model-using-character-composition-of-entities-in-chinese-relation-extraction_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{An Attention-Based Model Using Character Composition of Entities in Chinese Relation Extraction}}, year = {2026}, url = {https://4ort.xyz/entity/an-attention-based-model-using-character-composition-of-entities-in-chinese-relation-extraction}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): An Attention-Based Model Using Character Composition of Entities in Chinese Relation Extraction — https://4ort.xyz/entity/an-attention-based-model-using-character-composition-of-entities-in-chinese-relation-extraction (retrieved 2026-05-24)
