Natural Language Processing Model for Paragraph Adjacency Prediction
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Solution Overview
Problem
Existing technologies face challenges in processing text information, particularly when it is paragraphed, as they require manual labor for model training and struggle with insufficient sample generation, making it difficult to determine adjacent paragraphs and replace words effectively.
Innovation Solution
A method and apparatus that utilize word segmentation to acquire word sequences with specified identifiers, input these into a natural language processing model to generate word vectors, and employ a preset processing layer with an encoder and decoder to predict paragraph adjacency and replaced words, training the model using reference information to improve prediction accuracy without manual labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual labeling is used to train natural language processing models, then model training can be performed, but it requires a lot of manpower and time consumption increases
Solution Approach 1:
The system performs self-service by automatically generating training data through machine reading comprehension tasks. The model generates its own training samples by answering questions about documents, eliminating the need for manual labeling while maintaining training quality through automated evaluation metrics.
Solution Approach 2:
The system creates synthetic training data by copying and transforming existing documents into question-answer pairs. Instead of manually creating new labeled data, the system generates synthetic samples that replicate the structure and content of real training data, significantly reducing manual effort while maintaining data quality.
2Quantity of substance
If manual labeling is used to generate training samples, then adequate training data can be obtained, but it is difficult to generate samples and trained models are difficult to obtain
Solution Approach 1:
The system performs preliminary actions by pre-processing documents into structured formats and pre-generating question templates before actual training. This preparation work automates the sample generation process, making it easier to produce large quantities of training data without manual intervention while maintaining high model generation efficiency.
3Ease of operation
If existing text processing methods are used for paragraphed text, then text can be processed, but prediction accuracy for paragraph adjacency and replaced words is insufficient
Solution Approach 1:
The system adds another dimension to text processing by incorporating paragraph adjacency information and contextual relationships into the model's feature space. This dimensional expansion allows the model to capture subtle linguistic patterns and structural relationships, significantly improving prediction accuracy for paragraph adjacency and replaced words while maintaining ease of operation.
Data Source
AI summary
A method and an apparatus for processing information are provided. The method can include: acquiring a word sequence obtained by performing word segmentation on two paragraphs in a text; inputting the word sequence into a to-be-trained natural language processing model to generate a word vector corresponding to a word in the word sequence; inputting the word vector into a preset processing layer of the to-be-trained natural language processing model; predicting whether the two paragraphs are adjacent, and a replaced word in the two paragraphs; and acquiring reference information of the two paragraphs, and training the to-be-trained natural language processing model to obtain a trained natural language processing model, based on the prediction result and the reference information.