GGP: Glossary guided post-processing for word embedding learning

Ruosong Yang, Jiannong Cao, Zhiyuan Wen

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


Word embedding learning is the task to map each word into a low-dimensional and continuous vector based on a large corpus. To enhance corpus based word embedding models, researchers utilize domain knowledge to learn more distinguishable representations via joint optimization and post-processing based models. However, joint optimization based models require much training time. Existing post-processing models mostly consider semantic knowledge so that learned embedding models show less functional information. Compared with semantic knowledge sources, glossary is a comprehensive linguistic resource which contains complete semantics. Previous glossary based post-processing method only processed words occurred in the glossary, and did not distinguish multiple senses of each word. In this paper, to make better use of glossary, we utilize attention mechanism to integrate multiple sense representations which are learned respectively. With measuring similarity between word representation and combined sense representation, we aim to capture more topical and functional information. We propose GGP (Glossary Guided Post-processing word embedding) model which consists of a global post-processing function to fine-tune each word vector, and an auto-encoding model to learn sense representations, furthermore, constrains each post-processed word representation and the composition of its sense representations to be similar. We evaluate our model by comparing it with two state-of-the-art models on six word topical/functional similarity datasets, and the results show that it outperforms competitors by an average of 4.1% across all datasets. And our model outperforms GloVe by more than 7%.

Original languageEnglish
Title of host publicationLREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings
EditorsNicoletta Calzolari, Frederic Bechet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
PublisherEuropean Language Resources Association (ELRA)
Number of pages5
ISBN (Electronic)9791095546344
Publication statusPublished - 2020
Event12th International Conference on Language Resources and Evaluation, LREC 2020 - Marseille, France
Duration: 11 May 202016 May 2020

Publication series

NameLREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings


Conference12th International Conference on Language Resources and Evaluation, LREC 2020


  • Post-processing model
  • Representation Learning
  • Word Embedding

ASJC Scopus subject areas

  • Language and Linguistics
  • Education
  • Library and Information Sciences
  • Linguistics and Language


Dive into the research topics of 'GGP: Glossary guided post-processing for word embedding learning'. Together they form a unique fingerprint.

Cite this