Inherent risks identification in a contract document through automated rule generation

초록

Due to the limited time available during the bidding process, construction companies may fail to identify risky terms in the contract document before submission. This paper proposes a method that uses natural language processing (NLP) models, such as dependency parser and bidirectional encoder representations from transformers (BERT), to disassemble and simplify sentences in a contract document and automatically generate rules for identifying risk sentences. The sentence disassembly process is conducted in the following order: adjunct separation, parallel structure separation, and subject-verb-object separation. Subsequently, risk sentence identification rules are automatically generated through the input of risk terms. The performance of the proposed method is verified using the generated rules. In the experiments, the accuracies of sentence disassembly and risk sentence identification were 95.5 % and 91.3 %, respectively. The proposed method can assist experts in reviewing contracts, significantly reducing the time required to generate new identification rules.

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