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A Knowledge-Enriched Ensemble Method for Word Embedding and Multi-Sense Embedding
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DOI：10.1109/TKDE.2022.3159539.png)
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Representing words as embeddings has been proven to be successful in improving the performance in many natural language processing tasks. Different from the traditional methods that learn the embeddings from large text corpora, ensemble methods have been proposed to leverage the merits of pre-trained word embeddings as well as external semantic sources. In this paper, we propose a knowledge-enriched ensemble method to combine information from both knowledge graphs and pre-trained word embeddings. Specifically, we propose an attention network to retrofit the semantic information in the lexical knowledge graph into the pre-trained word embeddings. In addition, we further extend our method to contextual word embeddings and multi-sense embeddings. Extensive experiments demonstrate that the proposed word embeddings outperform the state-of-the-art models in word analogy, word similarity and several downstream tasks. The proposed word sense embeddings outperform the state-of-the-art models in word similarity and word sense induction tasks.
Keyword:
Task analysis
Context modeling
Semantics
Bit error rate
Knowledge engineering
Wheels
Vocabulary
Word embedding
multi-sense embedding
knowledge graph
ensemble model
期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
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