文本处理方法、训练文本处理模型的方法及装置
By combining feature extraction networks and pointer networks, a sequence of input text and prompt text is constructed, which solves the problem of poor performance of general text processing models in information extraction tasks. It achieves efficient processing of complex text processing tasks and is applicable to a variety of information extraction and text classification tasks.
CN116975596BActive Publication Date: 2026-07-17ALIBABA (CHINA) CO LTD
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ALIBABA (CHINA) CO LTD
- Filing Date
- 2023-07-04
- Publication Date
- 2026-07-17
AI Technical Summary
Technical Problem
Existing general text processing models perform poorly in information extraction tasks, have poor transferability, and are difficult to effectively handle various types of text understanding tasks.
Method used
By combining feature extraction networks and pointer networks, a unified processing of information extraction tasks is achieved by constructing sequences of input text and prompt text. Siamese networks are used for feature extraction and fusion, supporting multi-round text processing tasks.
Benefits of technology
It improves the performance of general text processing models for information extraction tasks, effectively handles complex text processing tasks, and is suitable for various information extraction and text classification tasks.
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Abstract
本申请实施例公开了一种文本处理方法、训练文本处理模型的方法及装置。包括：获取输入文本；将包含输入文本和提示文本的文本序列输入文本处理模型，提示文本包括指示文本处理任务类型的信息，获取文本处理模型依据指示文本处理任务类型的信息输出的输入文本对应的目标内容；其中文本处理模型包括：特征提取网络对输入的文本序列进行特征提取，得到包含输入文本的第一子文本序列的特征表示；指针网络利用第一子文本序列的特征表示，预测输入文本对应的目标内容。本申请在文本处理模型中引入指针网络，通过构造“输入文本+提示文本”的文本序列，将自然语言理解任务统一为抽取范式，从而提高通用文本处理模型对信息抽取类任务的信息抽取效果。
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