Bootstrapping Relation Extractors using Syntactic Search by Examples

• URL: http://arxiv.org/abs/2102.05007v1
• Date: Tue, 9 Feb 2021 18:17:59 GMT
• Title: Bootstrapping Relation Extractors using Syntactic Search by Examples
• Authors: Matan Eyal, Asaf Amrami, Hillel Taub-Tabib, Yoav Goldberg
• Abstract summary: We propose a process for bootstrapping training datasets which can be performed quickly by non-NLP-experts. We take advantage of search engines over syntactic-graphs which expose a friendly by-example syntax. We show that the resulting models are competitive with models trained on manually annotated data and on data obtained from distant supervision.
• Score: 47.11932446745022
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The advent of neural-networks in NLP brought with it substantial improvements in supervised relation extraction. However, obtaining a sufficient quantity of training data remains a key challenge. In this work we propose a process for bootstrapping training datasets which can be performed quickly by non-NLP-experts. We take advantage of search engines over syntactic-graphs (Such as Shlain et al. (2020)) which expose a friendly by-example syntax. We use these to obtain positive examples by searching for sentences that are syntactically similar to user input examples. We apply this technique to relations from TACRED and DocRED and show that the resulting models are competitive with models trained on manually annotated data and on data obtained from distant supervision. The models also outperform models trained using NLG data augmentation techniques. Extending the search-based approach with the NLG method further improves the results.

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