Note: All Minicourses will be held on Thursday, January 5.

Big Data: Complex Systems and Text Analysis
Instructors: Allison Burkette (University of Mississippi), Jacqueline Hettel (Arizona State University), Bill Kretzschmar (University of Georgia)
Room: Lone Star A
Time: 10:00 AM - 3:00 PM, with a break for lunch

In this workshop we wish to introduce some basic ideas about complex systems, including A-curves and scaling; talk about corpus creation with either a whole population or with random sampling; and talk about quantitative methods, why “normal” statistics don’t work and how to use the assumption of A-curves to talk about document identification and comparison of language in whole-to-whole or part-to-whole situations like authors or text types. We will start the workshop with a 60 minute (40 minute explanation and demonstration, 20 minute experiential learning) general introduction by Burkette to basic terms in CS such as “states” and “emergence”, and also apply those principles to language in the form of nonlinear frequency distributions and scale-free networks (as from Kretzschmar 2009). The introductory section will acquaint the audience with how the operation of a CS leaves characteristic patterns in language as people use it. We will then organize the workshop in two additional parts: 1) Hettel, CS and Corpus Creation; 2) Kretzschmar, CS and Quantitative Measurement. In each part, we will offer explanation and demonstrations for 40 minutes, and allow 20 minutes for experiential learning. The workshop will thus offer intensely practical, how-to suggestions from our own experience for presentation of CS, respectively, 1) to students in class, 2) for design, implementation, and analysis of corpus studies, and 3) for design, implementation, and analysis of field research in text analysis. The focus of the workshop at all points will be “how-to” what participants can do in their own teaching and research to make use of CS patterns, and to exploit them for their own linguistic purposes.

Register for the Minicourse on Complex Systems and Text Analays



Computing Sentiment, Emotion and Personality
Instructor: Jason Baldridge (University of Texas at Austin)
Room: Lone Star B
Time: 10:00 AM - 3:00 PM, with a break for lunch

Opinion mining is a well-known natural language processing technique that generally focuses on the explicit portion of opinion expression. Given the great volume of text created and readily accessible online, tremendous value can be derived from this level of analysis, especially for marketers, political campaigns, and the like. Opinion mining itself takes many forms depending on the granularity of analysis desired, from the most basic determination of whether a given document is generally positive or negative to much more specific questions such as whether a given individual is strongly in favor of a given political position based on texts they’ve authored, their online behaviors and their social network. This tutorial will dive below the surface of opinion mining in three primary ways. First, we focus on some of the underlying algorithms and the opportunities and challenges for the varied kinds of inputs and outputs involved. In particular, we will discuss semi-supervised learning techniques and their relevance for entity and topic extraction in combination with opinion mining. We will also cover the difference between features used for topic classification and sentiment analysis and those used for stylistic analysis (such as authorship determination). Second, we focus on author modeling, which seeks to understand an individual’s demographic and psychographic attributes based on what they say and how they say it. Third, we look at what additional information might be determined from non-explicit components of linguistic expression, as well as nontextual aspects of the input, such as geography, social networks and images.

Register for the Minicourse on Computing Sentiment, Emotion and Personality



Innovative Pedagogy in the Linguistics Classroom
Instructors: Jon Bakos (Indiana State University), Ann Bunger (Indiana University), Lynn Burley (University of Central Arkansas), Elizabeth Canon (Georgia State University), Gaillynn Clements (Duke University and UNCSchool of the Arts), Sonja Launspach (Idaho State University), Michal Temkin Martinez (Boise State University), Miranda McCarvel (University of Utah)
Room: Lone Star C
Time: 9:00 AM - 2:00 PM, with a break for lunch

This course aims to bridge the pedagogical training gap that often occurs among academics. Much of the pedagogical training that graduate students receive is implicitly acquired during their time as teaching assistants. While there is some explicit instruction on pedagogy, it is often limited to specific courses. In this 4-hour minicourse, participants (ABD to full professor) will learn about incorporating three innovative practices into their undergraduate teaching: online learning, active learning, and service learning.

Register for the Minicourse on Innovative Pedagogy in the Linguisics Classroom



Praat Beyond the Basics
Instructor: Will Styler (University of Michigan)
Room: Lone Star F
Time: 10:00 AM - 3:00 PM, with a break for lunch

The Praat phonetics software package is as powerful as it is ubiquitious, with a world of functionality available to those who are willing to go beyond the basics. In this minicourse, we'll spend the first half of the session talking about some of Praat's more interesting functionality, from data annotation, to manipulating sound files by splicing, modifying pitch and duration or filtering noise, and producing publication-quality graphics from your data. Then, in the second half of the session, we'll talk about the advantages, challenges, pitfalls and practical methods of running and writing Praat scripts, which can help you speed up repetitive tasks, perform more complex analyses, and automate data collection.

Register for the Minicourse on Praat Beyond the Basics