Enhancing Taxonomy Completion with Concept Generation via Fusing Relational Representations

• URL: http://arxiv.org/abs/2106.02974v1
• Date: Sat, 5 Jun 2021 21:50:13 GMT
• Title: Enhancing Taxonomy Completion with Concept Generation via Fusing Relational Representations
• Authors: Qingkai Zeng and Jinfeng Lin and Wenhao Yu and Jane Cleland-Huang and Meng Jiang
• Abstract summary: Existing taxonomy expansion or completion methods assume that new concepts have been accurately extracted and their embedding vectors learned from the text corpus. We propose GenTaxo to enhance taxonomy by identifying positions that need new concepts and then generating appropriate concept names. Instead of relying on the corpus for concept embeddings, GenTaxo learns the contextual embeddings from their surrounding graph-based and language-based information, and leverages the corpus for pre-training a concept name generator.
• Score: 41.629471011165684
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic construction of a taxonomy supports many applications in e-commerce, web search, and question answering. Existing taxonomy expansion or completion methods assume that new concepts have been accurately extracted and their embedding vectors learned from the text corpus. However, one critical and fundamental challenge in fixing the incompleteness of taxonomies is the incompleteness of the extracted concepts, especially for those whose names have multiple words and consequently low frequency in the corpus. To resolve the limitations of extraction-based methods, we propose GenTaxo to enhance taxonomy completion by identifying positions in existing taxonomies that need new concepts and then generating appropriate concept names. Instead of relying on the corpus for concept embeddings, GenTaxo learns the contextual embeddings from their surrounding graph-based and language-based relational information, and leverages the corpus for pre-training a concept name generator. Experimental results demonstrate that GenTaxo improves the completeness of taxonomies over existing methods.

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