Applied Natural Language Processing
Much of the most valuable information available online today resides in textual form, but natural language is notoriously difficult to process automatically. Applied natural language processing -- also known as automated content analysis and language engineering -- can provide partial solutions. This course will examine the state-of-the-art in applied NLP, with an emphasize on how well the algorithms work and how they can be used (or not) in applications. Topics will include text summarization, text mining, question answering, information extraction, text categorization, author and genre recognition, word sense disambiguation, and lexical and ontological acquisition (including the Semantic Web), and text analysis for social applications such as Blogs and social networks. The course will consist of lectures, homeworks, in-class exercises, and a final project.
This course is now offered as INFO 256.