Computational Linguistics, MS (Not Admitting Students)

PROGRAM NO LONGER ADMITTING STUDENTS - AS OF FALL 2021

Jaklin Kornfilt

kornfilt@syr.edu 340 HBC

315-443-5375

Faculty

Jaklin Kornfilt, Maria Emma Ticio Quesada, Lu Xiao, Bei Yu

Computational Linguistics (also called Natural Language Processing, abbreviated as NLP) is a field of vital importance in the information age. With growing amounts of speech and text data, the demand keeps increasing for automated tools to understand human language and NLP specialists to develop and operate these tools.

In industry, Computational Linguistics techniques are being widely used in search engines, digital libraries, speech recognition systems, and data mining toolkits. The leading data analysis companies like SAS and SPSS all have added text analysis components to their products. Many open-source NLP toolkits have also been available. Companies with large amount of text data need NLP specialists to develop in-house tools or use off-the-shelf tools to analyze their corpora.

Computational Linguistics also plays a critical role in the latest data-driven scholarship in computational social sciences and digital humanities. Humanist scholars and social scientists are increasingly using large corpora to make robust inferences in their research. Scientific literature, government documents, and user-generated content in social media are just a few examples of commonly used corpora. Students and scholars in sociology, journalism, and communication fields also need to learn to use NLP tools to slice and dice large document collections, identify the main themes and opinions of different parties.

Syracuse University is home to the Syracuse University Forensic and National Security Science Institute (FNSSI), which provides critical leadership for the protection of our nation in the areas of defense and security. The tools and techniques described above are also widely used in national defense and security agencies, as well as law enforcement agencies at the local, national, and international levels. The knowledge of such tools and their development and use is becoming more critical to employees in these fields, which is another reason SU is a strong candidate for a computational linguistics program.

Student Learning Outcomes

1. Apply techniques that are being widely used in search engines, digital libraries, speech recognition systems, and NLP data mining toolkits

2. Engage in recent data-driven scholarship in computational social sciences and digital humanities

3. Use NLP tools to analyze and create large document collections, identify the main themes and opinions of different parties

4. Apply syntactic and semantic analysis to natural language

Requirements

In order to receive the Masters of Science in Computational Linguistics, students must complete at least 36-credit hours of coursework, which includes 3 or 6 credits in an internship, and earn a cumulative grade point average of at least 3.0.

Nine courses (five 3-credit LIN courses in linguistics, two 3 credit CPS courses in computational science, and two 3 credit IST courses in information studies) plus a 3 or 6 credit IST internship, all offered on a yearly basis, will be required of all those interested in receiving the degree. The first of these courses, LIN 601 - Introductory Linguistic Analysis , will provide essential grounding in the mechanics of language, e.g. the sound system, word structure, sentence structure, and meaning. Through the use of examples from a range of languages, students will learn about similarities and differences across languages, which will allow them to understand the various possible manifestations of natural language. LIN 641 - Syntactic Analysis , LIN 651 - Morphological Analysis , and LIN 611 - Semantics of Human Languages , build on the principles learned in LIN 601 to provide students with a deeper understanding of the three areas of linguistics that are most important to the field of computational linguistics. LIN 741 - Advanced Syntax , builds upon the principles of syntactic analysis which are introduced in LIN 641 .

Two additional required courses are in information studies: The foundational courses IST 664 - Natural Language Processing /CIS 668 - Natural Language Processing and IST 736 - Text Mining . A third required course is the internship course IST 973 . This internship can be taken for three or six credits, if taken for three credits, an elective from the courses below for three credits needs to be added. IST 664 - Natural Language Processing introduces concepts and methods in processing text including tokenization, syntactic parsing, and semantic and pragmatic level analysis. IST 736 introduces concepts and methods for knowledge discovery from large amount of text data, and the application of text mining techniques for business intelligence, digital humanities, and social behavior analysis.

Two additional required courses are in computational science CPS 681 - Explorations in Computing and Programming and CPS 688 - Algorithms for Computational Journalism and Linguistics . Students who demonstrate sufficient knowledge in these areas may test out of the courses and replace them with elective courses from the list below.

Elective Courses

The courses that follow are generally offered yearly. Students can select among them in completing the remaining credits required for completion of the degree, based on professional need and academic interest. Substitutions may be made with the permission of the director of the degree program.

CIS 623 - Assured Programming with Formal Methods 3 credit(s)
CIS 667 - Introduction to Artificial Intelligence 3 credit(s)
CSD 616 - Introduction to Applied Phonetics 3 credit(s)
CSE 684 - Introduction to Artificial Intelligence 3 credit(s)
IST 631 - Theory of Classification and Subject Representation 3 credit(s)
IST 649 - Human Interaction with Computers 3 credit(s)
IST 652 - Scripting for Data Analysis 3 credit(s)
IST 707 - Applied Machine Learning 3 credit(s)
LIN 612 - Meaning and Communication 3 credit(s)
LIN 631 - Phonological Analysis 3 credit(s)
PHI 651 - Logic and Language 3 credit(s)
IST 659 - Data Administration Concepts and Database Management 3 credit(s)
IST 687 - Introduction to Data Science 3 credit(s)
IST 718 - Big Data Analytics 3 credit(s)
IST 719 - Information Visualization 3 credit(s)

Admission Requirements

Completed Syracuse University Graduate School Application
Resume
Personal Statement - include background and interest in the program
Official transcripts - from graduate and undergraduate studies
3 Letters of Recommendation
Application Fee
GRE Scores: Required
TOEFL Scores: 580 (written test), 237 (computer-based test), 92-93 (internet-based test) minimum for unconditional admissions

Additional Information

Partial tuition scholarships may be available. Please contact the Director for further information.