{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T12:10:59Z","timestamp":1769343059886,"version":"3.49.0"},"reference-count":43,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T00:00:00Z","timestamp":1632355200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Internet technologies are emerging very fast nowadays, due to which web pages are generated exponentially. Web page categorization is required for searching and exploring relevant web pages based on users\u2019 queries and is a tedious task. The majority of web page categorization techniques ignore semantic features and the contextual knowledge of the web page. This paper proposes a web page categorization method that categorizes web pages based on semantic features and contextual knowledge. Initially, the GloVe model is applied to capture the semantic features of the web pages. Thereafter, a Stacked Bidirectional long short-term memory (BiLSTM) with symmetric structure is applied to extract the contextual and latent symmetry information from the semantic features for web page categorization. The performance of the proposed model has been evaluated on the publicly available WebKB dataset. The proposed model shows superiority over the existing state-of-the-art machine learning and deep learning methods.<\/jats:p>","DOI":"10.3390\/sym13101772","type":"journal-article","created":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T23:08:31Z","timestamp":1632784111000},"page":"1772","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Semantic Features with Contextual Knowledge-Based Web Page Categorization Using the GloVe Model and Stacked BiLSTM"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1168-0032","authenticated-orcid":false,"given":"Amit Kumar","family":"Nandanwar","sequence":"first","affiliation":[{"name":"Computer Science & Engineering Department, Maulana Azad National Institute of Technology, Bhopal 462003, Madhya Pradesh, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaytrilok","family":"Choudhary","sequence":"additional","affiliation":[{"name":"Computer Science & Engineering Department, Maulana Azad National Institute of Technology, Bhopal 462003, Madhya Pradesh, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"79024","DOI":"10.1109\/ACCESS.2020.2990972","article-title":"A Study of Optimizing Search Engine Results Through User Interaction","volume":"8","author":"Chen","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","unstructured":"Li, C., and Liu, K. 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