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Applications of Natural Language Processing covers various applications of human language technology in greater depth than in CSCI 544 or CSCI 562. There are three complementary linked courses: the first covers Machine Translation; the second covers Information Retrieval, and the third covers Information Extraction and Text Summarization. These are taught successively, once per year. This course discusses Information Retrieval.

CSCI 572: Information Retrieval and Web Search Engines, Spring 2012 is a related course to CSCI 599: Information Retrieval. Whereas we cover fundamental topics of information retrieval, CSCI 572 has its sole emphasis on search engines. Both courses may be taken as there is only a small overlap in content.

Goals

Information Retrieval (IR) is the science of searching for and making sense of information from large collections of text. It synthesizes topics from computer science, mathematics, linguistics, and psychology. As the amount of digital content continues to grow, there is an increased need to be able to effectively and efficiently search, organize, and understand it. While Web search engines (e.g., Bing, Google, and Yandex) are the best-known IR applications, there are many other areas to which IR can be applied. These include targeted advertising, recommender systems (e.g., Amazon user suggestions), cross-lingual search, and spam filtering. Two important areas where IR saw significant growth in recent years are e-discovery and medical search. The former deals with automatic organization and sense-making of legal documents. The latter operates in the domain of medical articles and focuses on the development of techniques for automatic knowledge extraction. Constantly emerging application areas means that there are many employment opportunities in the field of IR. The job market is poised to grow as more and more companies and organizations accumulate large collections of digital content.

Location & Time

Tue & Thu, 3:30-4:50pm, GFS 118.

Instructors

Anton Leuski and Don Metzler. Office hours immediately follow each lecture.

Prerequisites

Permission of instructor. Students should have familiarity with natural language processing and be comfortable with medium-sized programming projects.