Course Schedule
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INFO 555 – Applied Natural Language Processing
GIDP: Second Lang. Acquisition & Teaching (SLAT)
Most of the data available on the web or managed by institutions and businesses consists of unstructured text. Natural language processing tools help to organize such texts, extract relevant information from them, and automatize time-consuming processes. This course will teach the fundamental knowledge necessary to design and develop end-to-end natural language understanding applications, drawn from examples such as question answering, sentiment analysis, information extraction, automated inference, machine translation, chatbots, etc. We will use several natural language processing toolkits and libraries.
Most of the data available on the web or managed by institutions and businesses consists of unstructured text. Natural language processing tools help to organize such texts, extract relevant information from them, and automatize time-consuming processes. This course will teach the fundamental knowledge necessary to design and develop end-to-end natural language understanding applications, drawn from examples such as question answering, sentiment analysis, information extraction, automated inference, machine translation, chatbots, etc. We will use several natural language processing toolkits and libraries.
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- Section: 002
- Instructor: Laparra, Egoitz
- Days: MoWe
- Time: 03:30 PM - 04:45 PM
- Dates: Aug 25 - Dec 10
- Status: Open
- Enrollment: 51 / 60
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- Section: 102
- Instructor: Laparra, Egoitz
- Days:
- Time:
- Dates: Oct 16 - Dec 10
- Status: Open
- Enrollment: 22 / 60
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- Section: 202
- Instructor: Laparra, Egoitz
- Days:
- Time:
- Dates: Oct 16 - Dec 10
- Status: Open
- Enrollment: 22 / 60
INFO 555 – Applied Natural Language Processing
GIDP: Second Lang. Acquisition & Teaching (SLAT)
Most of the data available on the web or managed by institutions and businesses consists of unstructured text. Natural language processing tools help to organize such texts, extract relevant information from them, and automatize time-consuming processes. This course will teach the fundamental knowledge necessary to design and develop end-to-end natural language understanding applications, drawn from examples such as question answering, sentiment analysis, information extraction, automated inference, machine translation, chatbots, etc. We will use several natural language processing toolkits and libraries.
Most of the data available on the web or managed by institutions and businesses consists of unstructured text. Natural language processing tools help to organize such texts, extract relevant information from them, and automatize time-consuming processes. This course will teach the fundamental knowledge necessary to design and develop end-to-end natural language understanding applications, drawn from examples such as question answering, sentiment analysis, information extraction, automated inference, machine translation, chatbots, etc. We will use several natural language processing toolkits and libraries.
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- Section: 401
- Instructor: Purdie, Cameron
Brown, Holly B
Bates, Jennifer Michelle
Carton, Kevin
Acuna, Manuel De Jesus
Minson, Sarah
- Days:
- Time:
- Dates: Jul 18 - Sep 7
- Status: Open
- Enrollment: 0 / 120