Chinese Open Relation Extraction with Pointer-Generator Networks

Cheng, Ziheng; Wu, Xu; Xie, Xing; Wu, Jingchen

doi:10.1109/dsc50466.2020.00054

Cited by 1 publication

(2 citation statements)

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“…PGCORE [29] casts relation extraction as a text summary task and proposes an end-toend abstract Chinese Open RE model based on the Pointer-Generator Network.…”

Section: Experiments Resultsmentioning

confidence: 99%

“…It first learns a Bayesian classifier, then generates all candidate triples for the input sentence; after that, it retains the results with high confidence through the classifier, and finally, it filters out unqualified results by counting the frequency of triples in the text. PGCORE [29] proposed Pointer-Generator Networks to extract open-domain relations endto-end, which outperforms rule-based methods, but it only considers the case where there is a single triple in the sentence. SpanOIE [30] first finds the predicates in the sentence, then takes the predicate and the sentence as an input and outputs the argument pairs that belong to this predicate.…”

Section: Open Domain Relation Extraction (Ore)mentioning

confidence: 99%

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A Unified Knowledge Extraction Method Based on BERT and Handshaking Tagging Scheme

Yang

Pun²,

Vai

et al. 2022

Applied Sciences

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In the actual knowledge extraction system, different applications have different entity classes and relationship schema, so the generalization and migration ability of knowledge extraction are very important. By training a knowledge extraction model in the source domain and applying the model to an arbitrary target domain directly, open domain knowledge extraction technology becomes crucial to mitigate the generalization and migration ability issues. Traditional knowledge extraction models cannot be directly transferred to new domains and also cannot extract undefined relation types. In order to deal with the above issues, in this paper, we proposed an end-to-end Chinese open-domain knowledge extraction model, TPORE (Extract Open-domain Relations through Token Pair linking), which combined BERT with a handshaking tagging scheme. TPORE can alleviate the nested entities and nested relations issues. Additionally, a new loss function that conducts a pairwise comparison of target category score and non-target category score to automatically balance the weight was adopted, and the experiment results indicate that the loss function can bring speed and performance improvements. The extensive experiments demonstrate that the proposed method can significantly surpass strong baselines. Specifically, our approach can achieve new state-of-the-art Chinese open Relation Extraction (ORE) benchmarks (COER and SAOKE). In the COER dataset, F1 increased from 66.36% to 79.63%, and in the SpanSAOKE dataset, F1 increased from 46.0% to 54.91%. In the medical domain, our method can obtain close performance compared with the SOTA method in the CMeIE and CMeEE datasets.

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“…PGCORE [29] casts relation extraction as a text summary task and proposes an end-toend abstract Chinese Open RE model based on the Pointer-Generator Network.…”

Section: Experiments Resultsmentioning

confidence: 99%

Section: Open Domain Relation Extraction (Ore)mentioning

confidence: 99%