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Syracuse University Part-Time Instructor - Natural Language Processing and Text Mining in Syracuse, New York

These are two different courses offered through the School of Information Studies. This is currently a "shadow" position for the first semester, but could turn into a solo position depending on student demand for summer 2021 and fall 2021 semesters. See below for individual descriptions of each class. Be able to teach graduate level text analysis courses, such as text mining and Natural Language Processing (NLP). If teaching NPL, explain / teach how NLP can process written text and produce a linguistic analysis via techniques such as linguistic analysis, tokenization, word-level semantics, part-of-speech tagging, syntax, semantics and deep learning. Apply NLP in areas such as information retrieval, question answering, sentiment analysis, summarization, and dialogue systems. The course leverages the Natural Language Toolkit (NLTK) and Python. If teaching text mining, be able to explain / teach the key concepts and methods of text mining technologies rooted from machine learning, deep learning, natural language processing, and statistics as well as discuss the applications of text mining technologies in information organization and access, business intelligence, social behavior analysis, and digital humanities. The text mining class uses a Python-based command line tool call scikit-learn. IST 664: Natural Language Processing: This course is designed to develop an understanding of how natural language processing (NLP) can process written text and produce a linguistic analysis that can be used in other applications. This goal will be achieved through readings, lectures, class discussions, lab exercises, assignments, and studies of real-world applications that incorporate substantive NLP modules. The course primarily covers the techniques of NLP in the levels of linguistic analysis, going through tokenization, word-level semantics, part-of-speech tagging, syntax, semantics, and on up to the discourse level. It also includes the use of the NLP techniques, such as information retrieval, question answering, sentiment analysis, summarization, and dialogue systems, in applications. Course learning objectives include 1. Demonstrate the levels of linguistic analysis, the computational techniques used to understand text at each level, and what the challenges are for those techniques. 2. Process text through the language levels using the resources of the Natural Language Toolkit (NLTK) and some rudimentary use of the programming language Python. 3. Describe how NLP is used in many types of real-world applications. IST 736: Text Mining: 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. The main goal of this course is to increase student awareness of the power of large amounts of text data and computational methods to find patterns in large text corpora. This course is designed as a general introductory level course for all students who are interested in text mining. Programming skill is preferred but not required in this class. This course will introduce the concepts and methods of text mining technologies rooted from machine learning, natural language processing, and statistics. This course will also showcase the applications of text mining technologies in (1) information organization and access, (2) business intelligence, (3) social behavior analysis, and (4) digital humanities. Course learning objectives include: 1. Describe basic concepts and methods in text mining, for example text representation, text classification and clustering, and topic modeling; 2. Use the text mining concepts and methods to model real-world problems into text mining tasks, develop technical solutions, and evaluate the effectiveness of the solutions. 3. Communicate text mining process, result, and major findings to various audience including both experts and laypersons.

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