A Retrieval Model of Engineering Consulting Report Based on Joint Semantic and Association Matching
A text retrieval model for engineering consulting reports is proposed, combining semantic and association matchings to achieve accurate and efficient retrieval of titles and paragraphs, and effectively assisting the writing of engineering consulting reports.Based on text retrieval corpus for engineering consulting reports, the comparative learning model is fine-tuned by the corpus set.Then the vanilla bidirectional encoder representations from transformers model ( Vanilla BERT) is initialized, the textual data is then trained through the Vanilla BERT model and a linear layer to obtain semantic matching score.At the same time, we build vector representations of semantic primitives for textual and keyword information, and obtain the association matching score through the deep text interaction model.The obtained semantic matching score and association matching score are normalized and then weighted and fused to acquire the final matching score, and the text retrieval between the title and the paragraph is completed.Compared with the optimal comparative model, a combination of contextual vector representation and text interaction matching methods is incorporated, which improves the evaluation index of P@20 by 7.49％ and effectively enhances the effects of text retrieval.
text retrievaljoint rankingword vectorcharacter vectorsememe
NETL
NSTL
万方数据
