Name
Abstract | ||
|---|---|---|
Generating the precise semantic representation of a word or concept is a fundamental task in natural language processing. Recent studies which incorporate semantic knowledge into word embedding have shown their potential in improving the semantic representation of a concept. However, existing approaches only achieved limited performance improvement as they usually 1) model a word's semantics from some explicit aspects while ignoring the intrinsic aspects of the word, 2) treat semantic knowledge as a supplement of word embeddings, and 3) consider partial relations between concepts while ignoring rich coupling relations between them, such as explicit concept co-occurrences in descriptive texts in a corpus as well as concept hyperlink relations in a knowledge network, and implicit couplings between concept co-occurrences and hyperlinks. In human consciousness, a concept is always associated with various couplings that exist within/between descriptive texts and knowledge networks, which inspires us to capture as many concept couplings as possible for building a more informative concept representation. We thus propose a neural coupled concept representation (CoupledCR) framework and its instantiation: a coupled concept embedding (CCE) model. CCE first learns two types of explicit couplings that are based on concept co-occurrences and hyperlink relations, respectively, and then learns a type of high-level implicit couplings between these two types of explicit couplings for better concept representation. Extensive experimental results on six real-world datasets show that CCE significantly outperforms eight state-of-the-art word embeddings and semantic representation methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/MIS.2020.3021188 | IEEE Intelligent Systems |
Keywords | DocType | Volume |
Concept Representation,Coupling Learning,non-IID Learning,Representation Learning,Word Similarity | Journal | 36 |
Issue | ISSN | Citations |
1 | 1541-1672 | 3 |
PageRank | References | Authors |
0.42 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenpeng Lu | 1 | 15 | 6.06 |
| Yuteng Zhang | 2 | 4 | 0.77 |
| Shoujin Wang | 3 | 65 | 13.10 |
| Heyan Huang | 4 | 173 | 61.47 |
| Qian Liu | 5 | 46 | 20.95 |
| Sheng Luo | 6 | 3 | 0.42 |