Concept representation by learning explicit and implicit concept couplings

Wenpeng Lu, Yuteng Zhang, Shoujin Wang, Heyan Huang, Qian Liu, Sheng Luo

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)6-15
Number of pages10
JournalIEEE Intelligent Systems
Issue number1
Publication statusPublished - Jan 2021


  • Concept Representation
  • Coupling Learning
  • non-IID Learning
  • Representation Learning
  • Word Similarity


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