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Improving Open Information Extraction with Distant Supervision Learning

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Abstract

Open information extraction (Open IE), as one of the essential applications in the area of Natural Language Processing (NLP), has gained great attention in recent years. As a critical technology for building Knowledge Bases (KBs), it converts unstructured natural language sentences into structured representations, usually expressed in the form of triples. Most conventional open information extraction approaches leverage a series of manual pre-defined extraction patterns or learn patterns from labeled training examples, which requires a large number of human resources. Additionally, many Natural Language Processing tools are involved, which leads to error accumulation and propagation. With the rapid development of neural networks, neural-based models can minimize the error propagation problem, but it also faces the problem of data-hungry in supervised learning. Especially, they leverage existing Open IE tools to generate training data, and it causes data quality issues. In this paper, we employ a distant supervision learning approach to improve the Open IE task. We conduct extensive experiments by employing two popular sequence-to-sequence models (RNN and Transformer) and a large benchmark data set to demonstrate the performance of our approach.

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Availability of data and materials

The experimental data we used, including the experimental test data, are open and transparent.

Notes

  1. https://github.com/allenai/openie-standalone.

  2. https://github.com/dair-iitd/OpenIE-standalone.

  3. https://github.com/knowitall/openie.

  4. https://dumps.wikimedia.org/enwiki/20200620/.

  5. https://lod-cloud.net/dataset/wikidata.

  6. https://github.com/keras-team/keras.

  7. https://github.com/CyberZHG/keras-transformer.

  8. https://github.com/gabrielStanovsky/oie-benchmark.

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This paper was partially supported by NSFC grant U1866602, 61772157.

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  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China

    Jiabao Han & Hongzhi Wang

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  1. Jiabao Han
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Jiabao Han is responsible for this paper design and experimentation and partial writing. Hongzhi Wang is responsible for the writing and review of the paper.

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Correspondence to Hongzhi Wang.

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Han, J., Wang, H. Improving Open Information Extraction with Distant Supervision Learning. Neural Process Lett 53, 3287–3306 (2021). https://doi.org/10.1007/s11063-021-10548-0

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