A survey of methods for revealing and overcoming weaknesses of data-driven Natural Language Understanding.
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- Natural Language Engineering, 2023, v. 29, n. 1, p. 1, doi. 10.1017/S1351324922000171
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- Article
Works matching IS 13513249 AND DT 2023 AND VI 29 AND IP 1
Results: 7
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Authorship attribution using author profiling classifiers.
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- Natural Language Engineering, 2023, v. 29, n. 1, p. 110, doi. 10.1017/S1351324921000383
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- Article
3
Compositional matrix-space models of language: Definitions, properties, and learning methods.
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- Natural Language Engineering, 2023, v. 29, n. 1, p. 32, doi. 10.1017/S1351324921000206
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- Article
4
NLP startup funding in 2022.
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- Natural Language Engineering, 2023, v. 29, n. 1, p. 162, doi. 10.1017/S1351324923000013
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- Article
5
Statistics in Corpus Linguistics: A New Approach by Sean Wallis.
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- Natural Language Engineering, 2023, v. 29, n. 1, p. 177, doi. 10.1017/S1351324921000413
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- Publication type:
- Article
6
Reducing repetition in convolutional abstractive summarization.
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- Natural Language Engineering, 2023, v. 29, n. 1, p. 81, doi. 10.1017/S1351324921000309
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- Publication type:
- Article
7
Efficient bilingual lexicon extraction from comparable corpora based on formal concepts analysis.
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- Natural Language Engineering, 2023, v. 29, n. 1, p. 138, doi. 10.1017/S135132492100022X
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- Publication type:
- Article