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In proposed method, the associative knowledge network learned from the text corpus is first regarded as a background knowledge network, and then the multilevel contextual associative coupling degree features of noun words in given detection document are computed. Finally, contextual coherence detection and the location of those inconsistent noun words can be realized by using an interpretable classification method such as decision tree. Our sufficient experimental results show that above proposed method can obtain excellent performance and completely reach or even partially exceed the performance obtained by the latest deep neural network methods especially in F1 score metric. In addition, the natural interpretability and incremental learning ability of our proposed method should be extremely valuable than deep neural network methods. So, this study provides a very enlightening idea for developing interpretable machine learning methods, especially for the tasks of text semantic representation and writing error detection.<\/jats:p>","DOI":"10.1007\/s40747-022-00757-y","type":"journal-article","created":{"date-parts":[[2022,5,13]],"date-time":"2022-05-13T06:02:57Z","timestamp":1652421777000},"page":"5265-5285","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["An associative knowledge network model for interpretable semantic representation of noun context"],"prefix":"10.1007","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8130-4003","authenticated-orcid":false,"given":"Yulin","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9481-9599","authenticated-orcid":false,"given":"Zhenping","family":"Xie","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9937-8534","authenticated-orcid":false,"given":"Fanyu","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,13]]},"reference":[{"key":"757_CR1","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1109\/TKDE.2018.2883446","volume":"32","author":"A Moreo","year":"2020","unstructured":"Moreo A, Esuli A, Sebastiani F (2020) Learning to weight for text classification. 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