方法对比
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| 机器阅读理解 (MRC)× | 文本分类× | |
|---|---|---|
| 领域 | 文本挖掘 | 文本挖掘 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2016 | — |
| 提出者≠ | Rajpurkar, Zhang, Lopyrev & Liang (SQuAD) | — |
| 类型≠ | NLP question-answering task | Supervised NLP classification task |
| 开创性文献≠ | Rajpurkar, P., Zhang, J., Lopyrev, K. & Liang, P. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP, 2383-2392. DOI ↗ | Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer. DOI ↗ |
| 别名 | MRC, question answering over passages, extractive question answering, Makine Okuma Anlama (MRC) | text categorization, document classification, topic classification, metin sınıflandırma |
| 相关≠ | 3 | 4 |
| 摘要≠ | Machine reading comprehension (MRC), popularised by the SQuAD benchmark of Rajpurkar, Zhang, Lopyrev and Liang (2016), is a natural-language-processing task in which a model reads a given passage and answers multiple-choice or open-ended questions about it. It turns a passage plus a question into a machine-generated answer, supporting information retrieval, educational technology, and querying research databases. | Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples. |
| ScholarGate数据集 ↗ |
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