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Cross-Situational Learning Towards Robot Grounding
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In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
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Cross-Situational Learning Towards Robot Grounding
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In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
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AI for mapping multi-lingual academic papers to the United Nations' Sustainable Development Goals (SDGs) ...
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AI for mapping multi-lingual academic papers to the United Nations' Sustainable Development Goals (SDGs) ...
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AI for mapping multi-lingual academic papers to the United Nations' Sustainable Development Goals (SDGs) ...
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AI for mapping multi-lingual academic papers to the United Nations' Sustainable Development Goals (SDGs) ...
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Reproducibility of the Experimental Result of BERT for Evidence Retrieval and Claim Verification ...
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Reproducibility of the Experimental Result of BERT for Evidence Retrieval and Claim Verification ...
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Lexicon-Based vs. Bert-Based Sentiment Analysis: A Comparative Study in Italian
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In: Electronics; Volume 11; Issue 3; Pages: 374 (2022)
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MIss RoBERTa WiLDe: Metaphor Identification Using Masked Language Model with Wiktionary Lexical Definitions
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In: Applied Sciences; Volume 12; Issue 4; Pages: 2081 (2022)
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Detection of Chinese Deceptive Reviews Based on Pre-Trained Language Model
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In: Applied Sciences; Volume 12; Issue 7; Pages: 3338 (2022)
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S-NER: A Concise and Efficient Span-Based Model for Named Entity Recognition
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In: Sensors; Volume 22; Issue 8; Pages: 2852 (2022)
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Abstract:
Named entity recognition (NER) is a task that seeks to recognize entities in raw texts and is a precondition for a series of downstream NLP tasks. Traditionally, prior NER models use the sequence labeling mechanism which requires label dependency captured by the conditional random fields (CRFs). However, these models are prone to cascade label misclassifications since a misclassified label results in incorrect label dependency, and so some following labels may also be misclassified. To address the above issue, we propose S-NER, a span-based NER model. To be specific, S-NER first splits raw texts into text spans and regards them as candidate entities; it then directly obtains the types of spans by conducting entity type classifications on span semantic representations, which eliminates the requirement for label dependency. Moreover, S-NER has a concise neural architecture in which it directly uses BERT as its encoder and a feed-forward network as its decoder. We evaluate S-NER on several benchmark datasets across three domains. Experimental results demonstrate that S-NER consistently outperforms the strongest baselines in terms of F1-score. Extensive analyses further confirm the efficacy of S-NER.
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Keyword:
BERT; named entity recognition; span-based model
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URL: https://doi.org/10.3390/s22082852
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A Multitask Learning Framework for Abuse Detection and Emotion Classification
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In: Algorithms; Volume 15; Issue 4; Pages: 116 (2022)
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Visual and Phonological Feature Enhanced Siamese BERT for Chinese Spelling Error Correction
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4578 (2022)
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An Empirical Comparison of Portuguese and Multilingual BERT Models for Auto-Classification of NCM Codes in International Trade
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In: Big Data and Cognitive Computing; Volume 6; Issue 1; Pages: 8 (2022)
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A Lite Romanian BERT: ALR-BERT
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In: Computers; Volume 11; Issue 4; Pages: 57 (2022)
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Performance Study on Extractive Text Summarization Using BERT Models
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In: Information; Volume 13; Issue 2; Pages: 67 (2022)
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Analyzing COVID-19 Medical Papers Using Artificial Intelligence: Insights for Researchers and Medical Professionals
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In: Big Data and Cognitive Computing; Volume 6; Issue 1; Pages: 4 (2022)
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Leveraging Part-of-Speech Tagging Features and a Novel Regularization Strategy for Chinese Medical Named Entity Recognition
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In: Mathematics; Volume 10; Issue 9; Pages: 1386 (2022)
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Realistic Image Generation from Text by Using BERT-Based Embedding
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In: Electronics; Volume 11; Issue 5; Pages: 764 (2022)
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