3 citations · 3 across the 5 of their papers we have counts for
6 papers
Dependency-aware Self-training for Entity Alignment
Bing Liu, Tiancheng Lan, Wen Hua +1
Entity Alignment (EA), which aims to detect entity mappings (i.e. equivalent entity pairs) in different Knowledge Graphs (KGs), is critical for KG fusion. Neural EA methods dominat…
Guiding Neural Entity Alignment with Compatibility
Bing Liu, Harrisen Scells, Wen Hua +3
Entity Alignment (EA) aims to find equivalent entities between two Knowledge Graphs (KGs). While numerous neural EA models have been devised, they are mainly learned using labelled…
ActiveEA: Active Learning for Neural Entity Alignment
Bing Liu, Harrisen Scells, Guido Zuccon +2
Entity Alignment (EA) aims to match equivalent entities across different Knowledge Graphs (KGs) and is an essential step of KG fusion. Current mainstream methods -- neural EA model…
Transfer-based adaptive tree for multimodal sentiment analysis based on user latent aspects
Sana Rahmani, Saeid Hosseini, Raziyeh Zall +3
Multimodal sentiment analysis benefits various applications such as human-computer interaction and recommendation systems. It aims to infer the users' bipolar ideas using visual, t…
Cognitive-aware Short-text Understanding for Inferring Professions
Sayna Esmailzadeh, Saeid Hosseini, Mohammad Reza Kangavari +1
Leveraging short-text contents to estimate the occupation of microblog authors has significant gains in many applications. Yet challenges abound. Firstly brief textual contents com…
EmoDNN: Understanding emotions from short texts through a deep neural network ensemble
Sara Kamran, Raziyeh Zall, Mohammad Reza Kangavari +3
The latent knowledge in the emotions and the opinions of the individuals that are manifested via social networks are crucial to numerous applications including social management, d…