Multi-turn automatic question answering based entity-relation extraction method for power technology standards

Shiqing Wang; Peng Wang; Sunan Jiang; Feng Shang

doi:10.1117/12.3004580

16 August 2023 Multi-turn automatic question answering based entity-relation extraction method for power technology standards

Shiqing Wang, Peng Wang, Sunan Jiang, Feng Shang

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Proceedings Volume 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023); 127871X (2023) https://doi.org/10.1117/12.3004580
Event: 6th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2023), 2023, Shenyang, China

Abstract

The goal of entity relation extraction is to extract entities and the relations between entities from unstructured texts. Most of the existing research approaches are oriented towards common entity labels in general-purpose domains (e.g., time, place, person, institution, etc.) and simple texts in specialized domains (texts consisting of single sentences with low knowledge density). The existence of long-distance dependencies of entity pairs (cross-sentence entity pairs) and the phenomenon of overlapping relations (different relations sharing the same entity) in complex texts are ignored. However, complex texts are common in practical applications, especially in professional fields such as power technology standards, where the knowledge density of texts is high and the phenomenon of entity-pair cross-sentence dependency is significant. To solve the above problems, this paper proposes a novel multi-hop automatic question-and-answer-based entity relation extraction method, which combines the current well-established machine reading comprehension framework with the automatic question construction mechanism proposed in this paper, and uses the a priori knowledge provided by the question as the extraction type guide, and uses the multi-hop question-and-answer mechanism to reason about the answer span of the question, effectively alleviating the phenomena of overlapping relations and entity dependence on crosssentences in complex texts. We conducted extensive comparative experiments on the power technology standard dataset self-constructed in this paper, and the results show that the MT-auQA model proposed in this paper achieves optimal performance

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Shiqing Wang, Peng Wang, Sunan Jiang, and Feng Shang "Multi-turn automatic question answering based entity-relation extraction method for power technology standards", Proc. SPIE 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023), 127871X (16 August 2023); https://doi.org/10.1117/12.3004580

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KEYWORDS

Standards development

Data modeling

Performance modeling

Statistical modeling

Education and training

Semantics

Feature extraction

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