Sinno Jialin Pan
Biographic Data
| ID | 6536329 |
|---|---|
| NAME | Sinno Jialin Pan |
| GIVEN NAMES | Sinno Jialin |
| FAMILY NAME | Pan |
| SIGNATURE | PAN S J |
| AFFILIATIONS | School of Computer Science and Engineering, Nanyang Technological University, Singapore. [email protected] |
| ORCID | 0000-0001-6565-3836 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 1 |
| FIRST PUBLICATION YEAR | 2010 |
| LATEST PUBLICATION YEAR | 2021 |
| H-INDEX | 0 |
Variational Deep Logic Network for Joint Inference of Entities and Relations
Currently, deep learning models have been widely adopted and achieved promising results on various application domains. Despite their intriguing performance, most deep learning models function as black boxes, lacking explicit reasoning capabilities and explanations, which are usually essential for complex problems. Take joint inference in information extraction as an example. This task requires the identification of multiple structured knowledge …
Advances in Knowledge Discovery and Data Mining
Advances in Knowledge Discovery and Data Mining
Syntactically Meaningful and Transferable Recursive Neural Networks for Aspect and Opinion Extraction
In fine-grained opinion mining, extracting aspect terms (a.k.a. opinion targets) and opinion terms (a.k.a. opinion expressions) from user-generated texts is the most fundamental task in order to generate structured opinion summarization. Existing studies have shown that the syntactic relations between aspect and opinion words play an important role for aspect and opinion terms extraction. However, most of the works either relied on predefined rul…
A Survey on Transfer Learning
A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature…
No prominent works on this page.
A Survey on Transfer Learning
A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature…
Syntactically Meaningful and Transferable Recursive Neural Networks for Aspect and Opinion Extraction
In fine-grained opinion mining, extracting aspect terms (a.k.a. opinion targets) and opinion terms (a.k.a. opinion expressions) from user-generated texts is the most fundamental task in order to generate structured opinion summarization. Existing studies have shown that the syntactic relations between aspect and opinion words play an important role for aspect and opinion terms extraction. However, most of the works either relied on predefined rul…
Advances in Knowledge Discovery and Data Mining
Advances in Knowledge Discovery and Data Mining
Variational Deep Logic Network for Joint Inference of Entities and Relations
Currently, deep learning models have been widely adopted and achieved promising results on various application domains. Despite their intriguing performance, most deep learning models function as black boxes, lacking explicit reasoning capabilities and explanations, which are usually essential for complex problems. Take joint inference in information extraction as an example. This task requires the identification of multiple structured knowledge …
Computer Science (3 works) · Data mining (3 works) · Advanced Text Analysis Techniques (2 works) · Artificial Intelligence (2 works) · Artificial neural network (2 works) · Information extraction (2 works) · Relation (database (2 works) · Relationship extraction (2 works) · Sentiment Analysis and Opinion Mining (2 works) · Topic Modeling (2 works)