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NLP from scratch : solving the cold start problem for natural language processing /
"Michael Johnson and Norris Heintzelman (Lockheed Martin) share several techniques they've implemented to build classification and NER models from scratch. They lead a tour through this space as it applies to NLP and demonstrate their approach and architecture for the following techniques:...
| Clasificación: | Libro Electrónico |
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| Formato: | Electrónico Video |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
O'Reilly Media,
2019.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
| Sumario: | "Michael Johnson and Norris Heintzelman (Lockheed Martin) share several techniques they've implemented to build classification and NER models from scratch. They lead a tour through this space as it applies to NLP and demonstrate their approach and architecture for the following techniques: Weak supervision for news documents: Using rules base classification alongside deep learning system for text classification; Active learning and human in the loop: Explaining how breakthroughs in transfer learning for NLP have impacted their active learning framework for building an LSTM-based relevance model; Creative training sets: Identifying and cleaning already-labeled datasets, training classifier on "only" positive examples; NER adjudication: Combining knowledge from several annotation sources that leverages the strengths of each source. For each of these topics, Michael and Norris outline the theoretical foundation, the implementation architecture, and tools used and discuss the problems they encountered, so you can avoid making the same mistakes."--Resource description page |
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| Notas: | Title from title screen (viewed January 10, 2020). |
| Descripción Física: | 1 online resource (1 streaming video file (43 min., 17 sec.)) |