Open Relation Extraction for Chinese Noun Phrases

• Published in: IEEE transactions on knowledge and data engineering Vol. 33; no. 6; pp. 2693 - 2708
• Main Authors: Wang, Chengyu, He, Xiaofeng, Zhou, Aoying
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Electronic publishing; Encyclopedias; graph clique mining; hypergraph-based random walk; Internet; Knowledge acquisition; Magnetic heads; noun phrase segmentation; Open relation extraction; Probabilistic methods; Random walk; Semantics; Sentences; Task analysis
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2019.2953839

Relation Extraction (RE) aims at harvesting relational facts from texts. A majority of existing research targets at knowledge acquisition from sentences, where subject-verb-object structures are usually treated as the signals of existence of relations. In contrast, relational facts expressed within noun phrases are highly implicit. Previous works mostly relies on human-compiled assertions and textual patterns in English to address noun phrase-based RE. For Chinese, the corresponding task is non-trivial because Chinese is a highly analytic language with flexible expressions. Additionally, noun phrases tend to be incomplete in grammatical structures, where clear mentions of predicates are often missing. In this article, we present an unsupervised Noun Phrase-based Open RE system for the Chinese language (NPORE), which employs a three-layer data-driven architecture. The system contains three components, i.e., Modifier-sensitive Phrase Segmenter, Candidate Relation Generator and Missing Relation Predicate Detector. It integrates with a graph clique mining algorithm to chunk Chinese noun phrases, considering how relations are expressed. We further propose a probabilistic method with knowledge priors and a hypergraph-based random walk process to detect missing relation predicates. Experiments over Chinese Wikipedia show NPORE outperforms state-of-the-art, capable of extracting 55.2 percent more relations than the most competitive baseline, with a comparable precision at 95.4 percent.