Titlebook: Natural Language Processing and Information Systems; 26th International C Elisabeth Métais,Farid Meziane,Epaminondas Kapetan Conference pro

習(xí)慣

書目名稱Natural Language Processing and Information Systems影響因子(影響力)




書目名稱Natural Language Processing and Information Systems影響因子(影響力)學(xué)科排名




書目名稱Natural Language Processing and Information Systems網(wǎng)絡(luò)公開度




書目名稱Natural Language Processing and Information Systems網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Natural Language Processing and Information Systems被引頻次




書目名稱Natural Language Processing and Information Systems被引頻次學(xué)科排名




書目名稱Natural Language Processing and Information Systems年度引用




書目名稱Natural Language Processing and Information Systems年度引用學(xué)科排名




書目名稱Natural Language Processing and Information Systems讀者反饋




書目名稱Natural Language Processing and Information Systems讀者反饋學(xué)科排名

無(wú)所不知

Conference proceedings 20212021, held online in July 2021...The 19 full papers and 14 short papers were carefully reviewed and selected from 82 submissions. The papers are organized in the following topical sections: role of learning; methodological approaches; semantic relations; classification; sentiment analysis; social me

adroit

incarcerate

Iatrogenic

Sequence-Based Word Embeddings for?Effective Text Classificationh the goal of performing different Natural Language Processing (NLP) tasks. Our experiments demonstrated that DiVe is able to outperform existing (more complex) machine learning approaches, while preserving simplicity and scalability.

dominant

心胸開闊

基因組

NumER: A Fine-Grained Numeral Entity Recognition Datasetinstructions. To demonstrate the use of this dataset, we train a numeral BERT model to detect and categorize numerals in documents. Our baseline model achieves an F1-score of 95% and hence demonstrating that the model can capture the semantic meaning of the numeral tokens.

誰(shuí)在削木頭

Multiword Expression Features for?Automatic Hate Speech Detection tweet corpora with different MWE categories and with two types of MWE embeddings, word2vec and BERT. Our experiments demonstrate that the proposed HSD system with MWE features significantly outperforms the baseline system in terms of macro-F1.

業(yè)余愛好者