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

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发表于 2025-3-21 17:53:39 | 显示全部楼层 |阅读模式
书目名称Natural Language Processing and Information Systems
副标题26th International C
编辑Elisabeth Métais,Farid Meziane,Epaminondas Kapetan
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Natural Language Processing and Information Systems; 26th International C Elisabeth Métais,Farid Meziane,Epaminondas Kapetan Conference pro
描述.This book constitutes the refereed proceedings of the 26th International Conference on Applications of Natural Language to Information Systems, NLDB 2021, 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 media; linking documents; multimodality; applications. .
出版日期Conference proceedings 2021
关键词artificial intelligence; classification; computational linguistics; data mining; databases; information r
版次1
doihttps://doi.org/10.1007/978-3-030-80599-9
isbn_softcover978-3-030-80598-2
isbn_ebook978-3-030-80599-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

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发表于 2025-3-21 22:10:42 | 显示全部楼层
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
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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.
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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.
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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.
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