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(shū)目名稱(chēng)Web and Big Data影響因子(影響力)




書(shū)目名稱(chēng)Web and Big Data影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Web and Big Data網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Web and Big Data網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Web and Big Data被引頻次




書(shū)目名稱(chēng)Web and Big Data被引頻次學(xué)科排名




書(shū)目名稱(chēng)Web and Big Data年度引用




書(shū)目名稱(chēng)Web and Big Data年度引用學(xué)科排名




書(shū)目名稱(chēng)Web and Big Data讀者反饋




書(shū)目名稱(chēng)Web and Big Data讀者反饋學(xué)科排名

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