laparra@arizona.edu
Laparra, Egoitz
Rsrcher/Scientist IV
laparra
Currently Teaching
INFO 555 – Applied Natural Language Processing
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.
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.
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.