ParsingPhrase: Parsing-based automated quality phrase mining

Wu, Yongliang; Zhao, Shuliang; Dou, Shimao; Li, Jinghui

doi:10.1016/j.ins.2023.03.089

Cited by 3 publications

(2 citation statements)

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“…However, compared with the three methods, the phrasebased text representation method is the closest to human understanding of semantics [10,11]. Because phrases are the representation unit of human language semantics, they use a fxed sequence of words to represent independent semantics.…”

Section: Text Representationmentioning

confidence: 99%

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Knowledge Graph-Based Hierarchical Text Semantic Representation

Wu,

Pan,

et al. 2024

International Journal of Intelligent Systems

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Document representation is the basis of language modeling. Its goal is to turn natural language text that flows into a structured form that can be stored and processed by a computer. The bag-of-words model is used by most of the text-representation methods that are currently available. And yet, they do not consider how phrases are used in the text, which hurts the performance of tasks that use natural language processing later on. Representing the meaning of text by phrases is a promising area of future research, but it is hard to do well because phrases are organized in a hierarchy and mining efficiency is low. In this paper, we put forward a method called hierarchical text semantic representation using the knowledge graph (HTSRKG), which uses syntactic structure features to find hierarchical phrases and knowledge graphs to improve how phrases are evaluated. First, we use CKY and PCFG to build the syntax tree sentence by sentence. Second, we walk through the parse tree using the hierarchical routing process to obtain the mixed phrase semantics in passages. Finally, the introduction of the knowledge graph improves the efficiency of text semantic extraction and the accuracy of text representation. This gives us a solid foundation for tasks involving natural language processing that come after. Extensive testing on actual datasets shows that HTSRKG surpasses baseline approaches with respect to text semantic representation, and the results of a recent benchmarking study support this.

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Section: Text Representationmentioning

confidence: 99%

“…From the perspective of semantic integrity, phrases are more suitable basic units of textual representation than words [10,11]. We believe the following two issues can efectively improve the quality of text representation and have intense research signifcance.…”

Section: Introductionmentioning

confidence: 99%