# Long Text Truncation Algorithm Based on Label Embedding in Text Classification
## 3 Method
### Long Text Filtering Based on Similarity Calculation
#### 3.2.2 Sentence Vector Filtering and Text Reorganization
However, such models are only pretrained on a general corpus, so they are not capable of representing text data in specific fields, especially professional datasets such as the Arxiv dataset. Therefore, the ability of pretraining without a prefix to represent label words is also insufficient. 4 On the one hand, the LTTA-LE algorithm builds a pretraining prefix template to realize the interaction between long text and label words, so as to use the label word information to filter the noise information in the long text. On the other hand, the LTTA-LE algorithm builds a label word mapping prefix template to limit the semantic scope of label words so as to alleviate the problem of polysemy. #### 4.3.5 The Results of iFLYTEK Text Classification Challenge

iFLYTEK is a well-known intelligent voice service listed company in the Asia-Pacific region mainly researching intelligent voice, NLP, computer vision and other directions. It held an academic paper classification challenge in July 2021. The task of the competition was to use the title and abstract to predict the specific category of the paper. The competition dataset consisted of 60,000 papers in total, including 50,000 in the training set and 10,000 in the test set. The competition used the accuracy as the evaluation index, and a total of 473 teams participated in the competition. We applied the LTTA-LE algorithm to this academic paper classification competition and achieved an accuracy of 86.32%, which was 0.76 percentage points higher than the second place. ## 5 Conclusions

In this paper, we propose a more efficient method to deal with the long text classification task, namely the LTTA-LE algorithm. Compared with other mainstream long text classification methods, our method makes four major contributions. 1. Two prefix templates are designed and used: a pretraining prefix template and a label word mapping prefix template. With the help of the pretraining prefix template, the model can better learn the interaction information between the long texts and labels. With the help of the label word mapping prefix template, label embedding with more accurate and rich semantic information can be obtained. In addition, through the limitation of prefix templates, the problem of polysemy can be further alleviated. 2. By calculating the cosine similarity between the label embedding and the sentence segment, the sentence segments that are not related to the semantics of the label words are filtered out, avoiding the interference of the noise data to the model. 3. An adaptive filtering threshold equation is designed and used, which can dynamically determine the filtering amount of each long text to avoid the problem of information loss caused by excessive text filtering. 4. A model training method including three stages of \"in-domain pretraining, finetuning and prediction\" is introduced, so that the model can fully learn the context information of specific domain data, thereby improving the classification performance of the model. We test the performance of our LTTA-LE algorithm on three public datasets and find that our proposed method is superior to some commonly used existing algorithms. Meanwhile, we design some experiments to discuss the performance of our algorithm with different max_len settings and label-embedding strategies, and we further quantify the advantages of the LTTA-LE algorithm. Our method was applied to the iFLYTEK text classification challenge and won the first prize. In addition, in order to facilitate readers to have a clearer understanding of the LTTA-LE algorithm, we summarize its advantages and disadvantages in Table 5.

## 6 Future Work

In future work, we plan to apply the LTTA-LE algorithm to the multi-label long text classification task. Based on the idea of the LTTA-LE algorithm, in the multi-label text classification scenario, we need to filter the sentence segments that are not related to the label. Since different label categories correspond to different task scenarios, the key point of the problem is to take into account the needs of each scenario.