Natural Language Processing plays a big role in our digital lives. We will demystify some of these everyday tasks that involve natural language processing: such as spelling and grammar correction, document classification, dialogue systems, machine translation, and forensic linguistics. On the practical side, we will focus on applying off-the-shelf tools that are often used in computational modelling of language data. Armed with these skills, you will be able to model language data quantitatively and ask measurable research questions. By the end of the course, you will learn how to perform i) pre-processing of text files (cleaning up raw text files), ii) automatic linguistic annotation, such as Part of Speech tagging (automatically adding labels such as Noun, Adjective to each word), Name Entity Recognition (identifying proper names, time, date, places, events) and Sentiment (fear, anger, happy, surprise…) iii) the basics of classifying documents, authors and sentiment. Students will get insight into how these systems work (and why it is still so difficult to do natural language processing well). We also consider social and ethical considerations such as privacy, job creation and loss due to language technologies, and the nature of consciousness and machine intelligence.
This course aims to fill a gap between the students’ knowledge in phonetics and phonology and their ability to applying that knowledge to ask non-trival research questions using a large amount of speech and lexical data. It would cover corpus compilation, semi-automatic annotation (phonetic transcription and forced-alignment), extraction of phonetic and phonological variables and the basics of statistical analyses of corpus data. It complements other courses such as advanced phonetics, quantitative and experimental methods, and corpus/computational linguistics. The course will involve the use of programming languages (such as Python, R and unix commands) and they will be introduced as needed. While we won't be using a single textbook, we will likely sample from the following textbook: Harrington, J. (2010). Phonetic analysis of speech corpora. John Wiley & Sons.
Laboratory Phonology is the approach to studying phonology--the sound patterns in language--by using experiments. In this course, we will be particularly interested in answering questions about phonology using data from phonetics. Students will work together to conduct an original experimental study. Throughout the course, students will work collaboratively to design the project, collect data, analyze the data, and interpret results. Coronavirus guidelines permitting, this course will take place entirely in-person.By the end of the course, students will gain experience working together on a complex project, formulate a hypothesis and empirical predictions, design and implement an experimental protocol, analyze data using appropriate software, e.g. Praat, R, visualize data, interpret the hypothesis in light of the results Following the collaborative BN course, each student will write their own paper for the AP component of the Methodenmodul. All stages of the research and writing will be supported. The goal is for students to learn these skills in the course. For this reason, no background in phonetics, phonology, or statistics is assumed (other than the Basismodul).
This course provides you with an elementary introduction to English phonetics and phonology, designed for those who have no previous knowledge whatsoever of the subject. It begins with a very elementary introduction to articulatory phonetics, and then proceeds to introduce the student to a very simplified account of some of the main aspects of the phonological structure of present-day English. Languages other than English will also be examined to compare and contrast the linguistic structural differences and gain insights on linguistic generalisation.
It is as necessary to be numerate as it is to be literate, but students in the field of humanities are often not as numerate as they are literate. They will need to evaluate evidence that are based on probability-based models or statistical results in many of the courses that they take in university, as they consider the efficacy of vaccination and the severity of the pandemic, as they begin to vote in local and national elections, as they search for employment on the job market after graduating, and so on. With an increasingly digital world filled with big data, a command of statistical reasoning is more important than ever. In this course, we will learn numeracy through linguistics, specifically through phonetics and phonology by learning to analyse the sounds of languages quantitatively. How do we analyse the sounds of languages quantitatively? This course, Analysing the sounds of languages, covers the basics of quantitative methods using real data taken from the field of phonetics and phonology. We will provide a gentle introduction to the statistical program R (www.r-project.org) -- a program that is used by data scientists in the tech. industry and academic researchers. The course will consist of a combination of lectures, and plenty of hands-on exercises. We introduce research questions, such as “Do Southerners in the US really talk more slowly?” or “Why do we expect scholarly words to be longer than familiar words?” as a framework for introducing the numerical concepts required to answer research questions such as these. In this course, statistical methods are introduced with a research question and a solid understanding of the data, which is why we use real data and questions that are relevant to anyone who commands a spoken language. A good amount of space is also devoted to illustrating how to formulate and answer a research question, and hypothesis development and testing.
The ability to use digital tools is increasingly valuable to students in all fields, including the humanities. This course is intended for students with no programming background whatsoever who are interested in taking the first steps towards writing industry-grade software. We’ll begin by exploring computer architecture and move on to understand how programs work behind the scenes by writing simple and useful programs. These skills will allow students to think like a Computer Programmer. Participants will gain familiarity with the Unix command line along with a Code editor (Vim). The language used for the course will be Python because of its beginner-friendly syntax. Basics of Databases, Networking and Cloud computing will also be emphasized. After taking this course, students will become familiar with general concepts in computer science, gain an understanding of the general concepts of programming, and obtain a solid foundation in the use of Python. There is no specific textbook for this course. Learning resources consist of articles and chapters available online.
Is technology really as innocent and as objective as they are said to be? As machine learning (ML) and Artificial Intelligence (AI) becomes more prominent in our life from speech and voice recognition by Alexa to automatic fake news warnings of social media posts, issues with social bias and fairness in language technology become more pertinent than ever before. Negative impacts that biased ML and AI could have for various social identities such as race, gender and culture.We first introduce the concept of bias in language technology, and the different types of biases such as racial, gender, cultural biases. To begin to understand the cause of these biases, we will cover the basic underlying structure of some of the technologies such as Automatic Speech Recognition, hate speech detection and word association. To evaluate these biases, we will learn to generate test cases that can be used to evaluate trained systems, and the metrics that are used for measuring bias/fairness. Finally, we will cover the basics of bias mediation and techniques.
Students will work together to design, conduct, and interpret an original experimental study. The particular study will be developed by the class, but will involve acoustic analysis of probabilistic phonetic reduction. The course will be structured as a research project with the following components: (1) Motivation: students will read several papers around a single topic (phonetic reduction) in order to frame the research project. (2) Predictions: working in groups, students will formulate one or more sets of predictions. (3) Experimentation: working in groups, we will design and conduct one or more experiments. (4) Analysis and Interpretation: we will interpret the results in light of the predictions. Coronavirus guidelines permitting, students will use equipment in the new Computational Phonetics lab in order to conduct experiments. Students should expect to work in collaboration with classmates, and active participation in every stage of the course is required for BN credit. Students interested in writing a paper for an AP can use the class project as the basis for their own project.
In this course we will be discussing the basic notions, terminology and methodology of modern linguistics. We will focus on core areas of English linguistics. In this first part of the Introduction to English language and linguistics we will deal with the structure of language (in particular of the English language) and acquire the terminology and methodology of modern linguistics. The focus will be on the core areas of linguistics: the study of sound structure (phonology), word structure (morphology), and sentence structure (syntax). In the second part of the Introduction to English language and linguistics, the formal levels of linguistic analysis (phonology, morphology, syntax) will be viewed in relation to their meaning(s) as well as to their contexts of use. Topics therefore include semantics, pragmatics, and sociolinguistics.
This is the second half of the two-semester introduction to linguistics. In this semester we will turn to the analysis of meaning and language use, including topics such as semantics, pragmatics, historical linguistics and sociolinguistics.