WiSe 2023/24

**Description:**

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.

For more information on LSF: https://lsf.hhu.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=245927&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung

**Description:**
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.

Objectives
On completion of this course, you should be able to:
• Describe the articulatory processes involved in producing speech sounds of English and other languages.
• Gain a basic fluency with reading and writing the International Phonetic Alphabet
• Perform a phonemic analysis on phonetic data
• Identify similarities and differences among phonetic/phonological systems of a range of languages
• Analyse data provided from natural languages to extract phonological generalizations, and construct arguments from such data to support or to weaken proposals for particular analyses

Have you ever been curious about why Voice Assistants (Siri, Amazon Echo and Google Assistant) sometimes struggle to understand your English accent? As linguists, we can play a crucial role in evaluating these sophisticated systems. 

In this class, you will assess the performance of Automatic Speech Recognition systems (ASR) by evaluating them against your own recorded speech and other existing speech recordings. You will learn how to use recording equipment in a laboratory setting as well as how to cut and prepare audio for further processing.

Throughout the course, you will gain the knowledge in the following areas: 

• Fundamentals of ASR
• Techniques of making recordings in a laboratory setting
• Dealing with audios
• Evaluating ASRs with your own voice
• Assessing ASRs on other English accent speech recordings

The AP is a documentation of the work you have done in the class. As you progress through the course, there will be writing tasks that summarize the work you have done in the previous session(s). Using the assignment feedback, you can draft the relevant sections of your term paper. The final AP will then comprise an improved and expanded version of these write-ups into a 6-7 page term paper.

Pre-requisites: None, except the willingness and enthusiasm to learn about language technologies and linguistics. Please bring your laptop for each session.