Titlebook: Web and Big Data; 8th International Jo Wenjie Zhang,Anthony Tung,Hongjie Guo Conference proceedings 2024 The Editor(s) (if applicable) and

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書目名稱Web and Big Data影響因子(影響力)




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護(hù)身符

攤位

CeER: A Nested Name Entity Recognition Model Incorporating Gaze Featuretask, human annotators still have strength in recognition of complex structures and professional fields. In this work, we propose a Cognition-enhancing Entity Recognition model (CeER), which introduces cognition-based data to improve the performance of nested name entity recognition. Specifically, w

Autobiography

A Boundary Feature Enhanced Span-Based Nested Named Entity Recognition Methodes and show poor performance in recognizing Nested Named Entities (NNEs). Towards Nested Named Entity Recognition (NNER), span-based methods, as a mainstream, have been proposed recently. The effectiveness of identifying entity span, which can be regarded as sub-sequences of a sentence, directly aff

Implicit

DRILL

Enhancing NER with?Sentence-Level Entity Detection as?an?Simple Auxiliary Taskver, NER models are traditionally reliant on extensive manual annotations, which is both laborious and costly. To address this challenge, we propose a simple yet effective multi-task learning framework that requires no additional labeling efforts. Our approach leverages the observation that nearly 3

Omniscient

Myocyte

CeER: A Nested Name Entity Recognition Model Incorporating Gaze Featuretask, human annotators still have strength in recognition of complex structures and professional fields. In this work, we propose a Cognition-enhancing Entity Recognition model (CeER), which introduces cognition-based data to improve the performance of nested name entity recognition. Specifically, w

牌帶來(lái)

External Knowledge Enhancing Meta-learning Framework for?Few-Shot Text Classification via?Contrastivrototypical networks and so on. The primary mission of few-shot text classification is to learn a high-quality embedding representation for each class. However, due to the randomness in sample sampling, the representations of class prototypes often tend to be unstable. This paper proposes the SCLAWM

insipid