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Neural Network Methods for Natural Language Processing
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Deep Residual Learning for Image Recognition
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“That NLI is also sufficient for NLU is sometimes explicitly stated, for example by Goldberg as “solving [the natural language inference task] perfectly entails human level understanding of language” (Goldberg, 2017, p. 141).”
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“[27], [29].”
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“The dominant approach in Natural Language Processing relies on neural metrics [11], such as BERTScore [7] and cosine similarity [12], which map text into vector spaces.”
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Metadata
- Authors
- Goldberg, Y.
- Affiliations
- Bar-Ilan University (IL)
- Corresponding author
- Goldberg, Yoav
- ORCID
- Goldberg, Yoav
- Venue
- Synthesis Lectures on Human Language Technologies
- Publisher
- Springer International Publishing
- Published
- 2017
- ISSN
- 1947-4040 (ISSN-L)
- 1947-4059
- OpenAlex
- W2606089314
- Semantic Scholar
- 6c27d9844fcb94dfcd6decab7df559134d6d4fd7
- Cited by
- 821
- References
- 75
- Citations by year
- 2026: 38 · 2025: 72 · 2024: 60 · 2023: 101 · 2022: 111 · 2021: 134 · 2020: 142 · 2019: 92 · 2018: 64 · 2017: 6
- FWCI
- 37.46
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Synthesis lectures on human language technologies (publishedVersion)
- CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- artificial neural networks, natural language processing, machine learning models
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