Titlebook: Knowledge Science, Engineering and Management; 16th International C Zhi Jin,Yuncheng Jiang,Wenjun Ma Conference proceedings 2023 The Editor

使長(zhǎng)胖

Chinese Relation Extraction with?Bi-directional Context-Based Lattice LSTM entities in Chinese texts, thereby enhancing the accuracy of natural language understanding. Although existing hybrid methods can overcome some of the shortcomings of character-based and word-based methods, they still suffer from polysemy ambiguity, which results in inaccuracy when representing the

刪減

dermatomyositis

Multi-display Graph Attention Network for?Text Classificationxt classification is on extracting effective features from text, as accurate information extraction is crucial for the task. However, the current utilization of text information in text classification is not optimal, and thus, effective extraction of text information remains an important research to

ligature

Debiased Contrastive Loss for?Collaborative Filteringbinary cross-entropy or bayesian personalized ranking are usually employed as the loss function to optimize model parameters. Recently, the sampled softmax loss has been proposed to enhance the sampling efficiency, which adopts an in-batch sample strategy. However, it suffers from the sample bias is

obstruct

ParaSum: Contrastive Paraphrasing for?Low-Resource Extractive Text Summarizationdata. However, PLM-based methods are known to be data-hungry and often fail to deliver satisfactory results in low-resource scenarios. Constructing a high-quality summarization dataset with human-authored reference summaries is a prohibitively expensive task. To address these challenges, this paper

DAMN

枯燥

防御

LIEN

A Session Recommendation Model Based on Heterogeneous Graph Neural Network information. As a result, recommendation systems greatly improve user experience and retention by efficiently matching the content or products that users are interested in. However, most existing models only consider long-term preferences, ignoring the dynamic users’ preferences. To address this is

租約

Dialogue State Tracking with?a?Dialogue-Aware Slot-Level Schema Graph Approachhema graph are both proposed to capture slot relations based on prior knowledge or human experience, avoiding the independent prediction of slot values. However, they fall short in modeling the correlations among slots across domain, and the dialogue history encoding method injects noises into the s