TY - GEN
T1 - Short-Text Author Linking Through Multi-Facet Temporal-Textual Embedding (Extended Abstract)
AU - Najafipour, Saeed Najafipour
AU - Hosseini, Saeid
AU - Hua, Wen
AU - Reza Kangavari, Mohammad
AU - Zhou, Xiaofang
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - We devise a neural network-based temporal-textual framework that generates subgraphs with highly correlated authors from short-text contents. Our approach computes the relevance score (edge weight) between authors by considering a portmanteau of contents and concepts. It then employs a stack-wise graph-cutting algorithm to extract communities of related authors. Experimental results show that our multi-aspect vector space model can gain higher performance than other knowledge-centered competitors in linking short-text authors.
AB - We devise a neural network-based temporal-textual framework that generates subgraphs with highly correlated authors from short-text contents. Our approach computes the relevance score (edge weight) between authors by considering a portmanteau of contents and concepts. It then employs a stack-wise graph-cutting algorithm to extract communities of related authors. Experimental results show that our multi-aspect vector space model can gain higher performance than other knowledge-centered competitors in linking short-text authors.
KW - Author Linking
KW - Semantic Understanding
KW - Short Text Inference
KW - Temporally Multifaceted
KW - Word2Vec
UR - https://www.scopus.com/pages/publications/85200439131
U2 - 10.1109/ICDE60146.2024.00475
DO - 10.1109/ICDE60146.2024.00475
M3 - Conference article published in proceeding or book
AN - SCOPUS:85200439131
T3 - Proceedings - International Conference on Data Engineering
SP - 5687
EP - 5688
BT - Proceedings - 2024 IEEE 40th International Conference on Data Engineering, ICDE 2024
PB - IEEE Computer Society
T2 - 40th IEEE International Conference on Data Engineering, ICDE 2024
Y2 - 13 May 2024 through 17 May 2024
ER -