A Comparison of Two Paraphrase Models for Taxonomy Augmentation
| Autor: | Jochen L. Leidner, Vassilis Plachouras, Fabio Petroni, Timothy Nugent |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
business.industry
Computer science 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Paraphrase Taxonomy (general) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Natural language processing 0105 earth and related environmental sciences |
| Zdroj: | NAACL-HLT (2) |
| DOI: | 10.18653/v1/n18-2051 |
| Popis: | Taxonomies are often used to look up the concepts they contain in text documents (for instance, to classify a document). The more comprehensive the taxonomy, the higher recall the application has that uses the taxonomy. In this paper, we explore automatic taxonomy augmentation with paraphrases. We compare two state-of-the-art paraphrase models based on Moses, a statistical Machine Translation system, and a sequence-to-sequence neural network, trained on a paraphrase datasets with respect to their abilities to add novel nodes to an existing taxonomy from the risk domain. We conduct component-based and task-based evaluations. Our results show that paraphrasing is a viable method to enrich a taxonomy with more terms, and that Moses consistently outperforms the sequence-to-sequence neural model. To the best of our knowledge, this is the first approach to augment taxonomies with paraphrases. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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