Titlebook: CCKS 2021 - Evaluation Track; 6th China Conference Bing Qin,Haofen Wang,Jiangtao Zhang Conference proceedings 2022 Springer Nature Singapor

武器

Knowledge-Enhanced Retrieval: A Scheme for Question Answering,th predefined templates, as well as the . module to capture the best path. Extensive validations demonstrate the effectiveness of our KERQA framework, which achieved an F1 score of 78.78% on the final leaderboard of the CCKS 2021 KBQA contest.

香料

交響樂

https://doi.org/10.1007/978-3-642-67520-1ng methods 3) Target instances constructed by similarity 4) Validation set retraining with a small learning rate 5) Medical word vector combined with Easy Data Augmentation (EDA) method for text data augmentation. Our innovative approach has improved by an average of 1.164% compared to the baseline.

Thyroid-Gland

Hyperalimentation bei Krebspatienten,m the patient. The experimental results show that our framework achieves higher accuracy in disease diagnosis, which won the fourth place in the 2021 medical dialogue generation task containing Chinese.

Palpable

Debrief

Anonymous

A Dual-Classifier Model for General Fine-Grained Event Detection Task,

Cytokines

A Biaffine Attention-Based Approach for Event Factor Extraction,sed an approach with the biaffine attention mechanism to finish the task. The solution combines the state-of-the-art BERT-like base models and the biaffine attention mechanism to build a two-stage model, one stage for event trigger extraction and another for event role extraction. Besides, we apply

怎樣才咆哮

Method Description for CCKS 2021 Task 3: A Classification Approach of Scholar Structured Informatiosearching task and recommendation system. Therefore, extracting, tagging and statistical analysis the precision facts of experts and scholar can be applied in academic searching. In this paper, a structured information extraction and match approach for structured scholar portrait from HTML web pages

憤怒歷史

A Joint Training Framework Based on Adversarial Perturbation for Video Semantic Tags Classification understanding framework into two related tasks, namely the multi-classes video cate classification (VCC) task and the multi-label video tag classification (VTC) task. Meanwhile we propose a joint training framework for VCC task and VTC task based on adversarial perturbations strategy. In the final