# Second-Order Text Matching Algorithm for Agricultural Text
## 2 Related Work
[30] extracted character embeddings obtained from LSTM feature extraction and pre-trained word embeddings concatenated and passed through Bi-LSTM to obtain hidden state representations for each time step of paired texts. Then, it used four methods to match all time steps from both forward and backward directions. Finally, Bi-LSTM was used to aggregate matching vectors of the two sequences into a fixed-length matching vector for modeling. In the agricultural domain, there has been a growing awareness of the importance of text matching work in recent years. [PERSON] et al. [31] addressed many issues in the rice question and answer system, including the existence of many similar expressions with the same semantics, the sparsity of the problem text, real-time requirements, and non-standard problems. They proposed the Co-attention-Dense-GRU rice question classification model, consisting of text preprocessing, Dense-GRU layer, co-attention layer, and interactive classification layer. Experimental results demonstrated the model's excellent accuracy. The precision and F1 values of the proposed model were 96.3% and 96.9%, respectively. [PERSON] et al. [32] tackled the identification difficulty of word boundaries and insufficient embedding of vocabulary information in crop pest and disease issues. They introduced AgrCQS model for similarity matching in Chinese agricultural disease and pest problems. Experimental verification showed that MM-CNN model and self-attention mechanism improved the classification performance of the model. In their research, the performance of the AgrCQS on the CCDIP is verified, and three benchmark datasets, namely, AFQMC, LCQMC, and BQ, are used. The accuracy rates are 93.92%, 74.42%, 86.3%, and 83.05%, respectively, which are higher than that of baseline systems without using any external knowledge. [PERSON] et al. [33] addressed issues like segmentation errors and data sparsity in rice-related questions. They proposed a Siamese-based bidirectional BiLSTM-CNN rice question semantic matching model. The model utilized character-level Word2 Vec for question representation, employed Siamese neural networks to learn semantic features, extracted semantic temporal features using BiLSTM-CNN, and calculated question similarity at the semantic level with a cosine function. The experimental results indicated that the BiLSTM-CNN model had effectively extracted features of various granularities from texts, enhancing the rice question similarity matching performance. On the dataset that was constructed, the accuracy and F1 score of the BiLSTM-CNN model surpassed those of other text matching models, achieving 98.2% and 88.75%, respectively. [PERSON] et al. [34] focused on challenges in agricultural question and answer communities, such as limited vocabulary, numerous specialized terms, poor sentence standardization, and strong feature sparsity. They presented a text matching model integrating multiple semantic features. The model constructed a word co-occurrence relationship network in the text preprocessing layer, calculating weights for each word co-occurrence pair to enrich short-text features from various perspectives. The experimental results showed that the model could extract text features more comprehensively, with an accuracy of 94.15%. [PERSON] et al. [35] addressed the issue of automatically and accurately discovering repetitive semantic questions in China's agricultural technology dissemination question and answer community. They proposed an agricultural question similarity matching model based on BERT-Attention-Dense-Bi-GRU. The model, comprising text preprocessing, dense linked Bi-GRU, attention mechanism, and interactive classification layers, outperformed six other deep learning models in precision, recall, and F1 values. The precision and F1 values of the proposed model were 97.2% and 97.6%. Although the aforementioned models have achieved significant success, there are still challenges and shortcomings in text matching algorithms specific to the agricultural domain.