{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T14:29:49Z","timestamp":1779114589066,"version":"3.51.4"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"13","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>Ambiguity in business English contracts (e.g., lexical polysemy, syntactic nesting) seriously undermines accurate understanding and execution efficiency. To address this, we propose a Fine-Tuned BERT-CRF joint labeling model, with the following key methodological details: 1) Corpus Construction: A 12,800- sample contract corpus (covering 8 business domains) is built, with unified labeling for 4 ambiguity types (lexical, syntactic, reference, pragmatic) and 9,750 annotated ambiguity instances. 2) Model Design: BERT is adaptively fine-tuned via \u201cterm-aware masking\u201d (prioritizing contract-specific terms); CRF is integrated as a sequence labeling layer with a domain-prior transition matrix to optimize label generation. 3) Training Strategies: Adversarial training (FGSM\/PGD perturbations) enhances robustness, and incremental online learning (importance sampling) enables dynamic adaptation. Experimental results demonstrate: For lexical ambiguity, the model achieves 67.5% accuracy, 78.9% recall, and 72.8% F1; boundary labeling accuracy for the clause \u201cUSD 90.1 million\u201d improves from 56% to 79%; syntactic ambiguity F1 is 62.3%. Lightweight optimization (hidden layer dimension=34) reduces parameters by 45% (to 12.78 M) but requires 89 training rounds to balance performance. This model outperforms generic BERT-CRF by 9.2% in F1 for complex contract processing, verifying its effectiveness in resolving contractual syntactic and lexical ambiguity.<\/jats:p>","DOI":"10.31449\/inf.v50i13.10364","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T13:54:48Z","timestamp":1779112488000},"source":"Crossref","is-referenced-by-count":0,"title":["Research on Fine-Tuned BERT-CRF Joint Labeling Model for Contractual Syntactic and Lexical Ambiguity Resolution in Business"],"prefix":"10.31449","volume":"50","author":[{"given":"Xiaohong","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Tao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,5,18]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/10364\/6704","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/10364\/6704","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T13:54:48Z","timestamp":1779112488000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/10364"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,18]]},"references-count":0,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2026,5,18]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i13.10364","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5,18]]}}}