A Named Entity Recognition Method for Bidding Data Based on Pre-trained Model

Abstract

The present invention relates to a bidding data named entity recognition method based on a pre-training model, which specifically includes the following steps: S1: obtaining an open-source pre-training model; S2: obtaining unlabeled corpus and performing data preprocessing; S3: training the pre-trained model in S1 Training model; S4: Use labeled data for supervised training to obtain benchmark model M; S5: Make benchmark model M predict unlabeled data to obtain pseudo-label data; S6: Add pseudo-label data to the training set and real label data To the training set to train together to get the model M ' ; S7: Construct segment decoding network; S8: Input text into model M ' Encoding; S9: Input the text encoding into the segment decoding network; S10: Extract entity segments and their categories. After the pre-training model, the present invention performs model decoding and predicts the beginning and end positions of entities in a fragment recognition manner, which can speed up the decoding speed and obtain entity results with better precision.

Description

technical field [0001] The invention relates to the field of bidding text processing, in particular to a named entity recognition method for bidding data based on a pre-trained model. Background technique [0002] The bidding data refers to the bidding announcement information or the bidding and bidding announcement information disclosed by the tenderer. In the bidding data, the length of the data text is often long (the average length of the whole article is more than 1500 words), and the entity types in the text are more granular (for example, time entities can also be divided into bidding start time, bidding deadline, and bidding opening time). Extracting bidding entity data plays a vital role in analyzing bidding demand or winning bid information in a certain period of time in a certain area, and is an emerging business requirement. If you want to extract the named entity information in the bidding data, the most direct idea is to use the technology related to named ent...

AI Technical Summary

Problems solved by technology

Since CRF considers the global text path, it needs to calculate the scores of all possible paths, and then select the best path according to the score, which leads to a large computational overhead. This kind of extraction method is slow and prone to entity boundary errors.

[0009] On the one hand, the existing model framework for named entity extraction for bidding data is still scarce, and labeled high-quality bidding data is very scarce

On the other hand, bidding data is generally public information, which is relatively easy to obtain, and a large amount of new data will be generated every day, but the obtained data is often the original plain text data, and it will be difficult for supervised models to integrate these unlabeled use the data

Images