Human-Language-Technology-2-Fall-2024

Course information

This intermediate-level course is a continuation of LING 529 and covers the basics of information retrieval, focusing on both search and classification.

Course objectives

This course presents fundamental considerations and concepts for text-based search in the context of a simple keyword search function with Boolean connectives. We’ll then refine our methods for effective search—with the goal of returning the best results, satisfying a user’s actual information need—by exploring ways to represent similarity and to use term weighting. We’ll round out or view of search by thinking about how to determine how well our search application is meeting this goal. In the second half of the course, we’ll use some of these concepts that we developed in the context of search to explore methods for document classification, comparing both statistical approaches and vectorspace approaches.

Learning outcomes

Successful students in this course will...

1. use Python and tools commonly used in commercial applications to perform simple but realistic information retrieval tasks.

2. parse and prepare a corpus for search and classification tasks.

3. create an index for searching a corpus.

4. implement similarity and evaluation measures for searching a corpus.

5. implement classification models using statistical and vectorspace methods.

6. compare and evaluate these search and classification methods and the results they yield.

Learning outcomes #1 and #2 relate to Linguistics HLT program outcomes #1 and #3. Learning outcomes #3-6 relate to Linguistics HLT program outcome #2.

HLT learning outcomes addressed in this course

By completion of the HLT program, students will be able to:

1. write, debug, and document readable and efficient code in programming lan- guages commonly used to develop, implement, and evaluate Natural Language Pro- cessing models, as demonstrated through course projects and a professional internship.

2. select and apply appropriate algorithms and core concepts in Natural Lan- guage Processing to perform common tasks and solve realistic problems, as demon- strated through course projects and a professional internship.

3. utilize common tools and libraries used in NLP by integrating them into course projects and real-world applications or workflows, as demonstrated through course projects and a professional internship.