A Knowledge Extraction Method for Unstructured Text Data Based on Deep Learning

The extraction of structured entities, relationships, events, and other knowledge from unstructured text data is a complex task. Traditional methods, which rely on rule templates and statistical machine learning, struggle to grasp the complexity and diversity of text semantics. Even a single deep learning model faces challenges due to its limited capabilities. To effectively counteract these shortcomings in knowledge extraction, this paper presents a deep learning-based approach for joint knowledge extraction from unstructured text data. The method employs a multi-task learning (MTL) framework, with the pre-trained language model ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements Accurately) serving as the central encoder. It utilizes Span-based CRF (Conditional Random Field), multi-head selection mechanisms, and template generation + pointer networks for the decoder architecture. Additionally, it integrates modules such as bidirectional long short-term memory (BiLSTM), gating mechanisms, and adaptive loss balancing to construct an end-to-end joint extraction model for entities, relations, and events, known as GEM (Gated ELECTRA Multitask model). This method adapts to the varying feature requirements of different extraction tasks, capturing semantic features at various levels, including word-level entities, sentencelevel relations, and discourse-level event structures. It addresses long-distance dependencies hierarchically, avoiding the error accumulation common in traditional pipeline methods, and provides an efficient solution for knowledge extraction from largescale unstructured text data. Experiments demonstrate that the GEM model offers significant advantages over traditional methods in the joint extraction of entities, relationships, and events from unstructured text.