Language and linguistics today: Corpus linguistics with python programming language 2510-f1ENG2W-LLT-CLP

https://moodle.umk.pl/course/view.php?id=3833

The aim of the course is to introduce students to the use of programming in corpus linguistics. The course will involve familiarising students with the syntax and semantics of Python and its application in analysing large linguistic datasets.

The course puts a strong emphasis on obtaining programming skills with a view to applying them in future academic research. During the course, students will acquire skills related to manipulating data structures, processing raw text files to obtain informative output, defining functions and other skills that aid quantitative linguistic analysis.

Content distribution:

- Introduction
- Corpus linguistics, computational linguistics and natural language processing
- Introducing Python: Python among other programming languages, basic concepts
- Basic data structures in Python
- Operators, expressions, control flow, loops
- Working with text files, regular expressions
- Tokenisation, part-of-speech tagging
- Phrase-structure and dependency parsing
- Text classification: topic modelling and sentiment analysis

Term 2022/23Z:

As in part A

Total student workload

Participation in the seminar (30 hrs - 1 ECTS)

Learning outcomes - knowledge

The student can define basic concepts and issues related to corpus linguistics (K_W03).

Learning outcomes - skills

The student can process text files to obtain relevant output (K_U01). The student can differentiate between corpus linguistics and natural language processing and can use basic concepts from these disciplines (K_U06) The student can search for relevant information in order to solve common problems in computational linguistics, corpus linguistics and natural language processing (K_U10)

Learning outcomes - social competencies

The student understands the constant need to acquire new skills in the area of computational linguistics (K_K01)

Expository teaching methods

- description

Exploratory teaching methods

- seminar
- classic problem-solving
- project work
- experimental

Prerequisites

- interest in linguistics - basic computer skills

Course coordinators

Marek Placiński

Assessment criteria

- active participation in the seminar (20 %) [K_U01, K_K01]
- end-of-the semester project (80 %) [K_W03, K_U01, K_U06, K_U10]

Evaluation criteria:

fail – 0 – 59%
satisfactory - 60 – 69%
satisfactory plus - 70 – 75%
good - 76 – 85%
good plus - 86 – 90%
very good - 91 – 100%

Bibliography

Bird, S., Klein, E. & E. Loper. 2009. "Natural Language Processing with Python", available here: https://www.nltk.org/book/

Jurafsky, D. & J.H. Martin. "Speech and Language Processing", available here: https://web.stanford.edu/~jurafsky/slp3/

Term 2022/23Z:

As in part A

Notes

Term 2022/23Z:

As in part A

Additional information

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of 2510-f1ENG2W-LLT-CLP in USOSweb