Abstract: In recent years, with the rapid development of the national economy, the investment in power construction projects has increased rapidly. Both the number of the associated tenders and the corresponding workload of evaluation have soared. The conventional manual method for assessment is time-consuming, costly, and inefficient. For improving the efficiency of the bid review and reducing the related costs, it is ideal to take advantage of automatic or semi-automatic analysis. Among the adoption of machine-assisted, the entity identification in the tender text, definitely, plays an essential role in information extraction and text summarization. Since there are many complex and a hybrid combination of words in text like location names, the existing recognition technology does not perform well. In this paper, we propose an application-friendly critical information extraction method based on the conditional random fields (CRF), which realizes the automatic and rapid processing of tenders and accelerates the re-assessment of various engineering construction projects and data sharing. Our proposed mechanism has got an experimental verification of efficiency. It has been employed to the automatic transactions in the power sector.

Key words: electric bidding documents, conditional random field model, entity identification, automatically read, supervised learning