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<Paper uid="W05-0101">
  <Title>Teaching Applied Natural Language Processing: Triumphs and Tribulations</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> In Fall 2005 I introduced a new graduate level course called Applied Natural Language Processing.1 The goal of this course was to acquaint students with the state-of-the-art of the field of NLP with an emphasis on applications. The intention was for students to leave the class with an understanding of what is currently feasible (and just on the horizon) to expect from content analysis, and how to use and extend existing NLP tools and technology. The course did not emphasize the theoretical underpinnings of NLP, although we did cover the most important algorithms. A companion graduate course on Statistical NLP was taught by Dan Klein in the Computer Science department. Dan's course focused on 1Lecture notes, assignments, and other resources can be found at http://www.sims.berkeley.edu/courses/is290-2/f04/ . foundations and core NLP algorithms. Several computer science students took both courses, and thus learned both the theoretical and the applied sides of NLP. Dan and I discussed the goals and content of our respective courses in advance, but developed the courses independently.</Paragraph>
  </Section>
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