COURSE UNIT TITLE

: CORPUS LINGUISTICS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
ELECTIVE

Offered By

Linguistics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR ÖZGÜN KOŞANER

Offered to

Linguistics

Course Objective

This course aims to make students learn the structure of corpora, how they are created, how they are tagged/annotated, and how they can be used in research.

Learning Outcomes of the Course Unit

1   Know the properties and structure of corpora
2   Know how a corpus is created
3   Know how corpora are annotated and perform annotation using software
4   Conduct analyses on corpora using Python

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction, introducing and discussing the syllabus, Vocal organs
2 Introducing POS, morpholgical and syntactic dependency tags used in corpora
3 Data types and tags used in the Universal Dependencies Project
4 Introduction to corpus analysis with Python: Installing Python
5 Data types, variables, lists
6 Libraries required for corpus analysis: nltk, spacy
7 Libraries required for data visualization: numpy, seaborn, scikitlearn, matplotlib, pandas
8 Midterm Exam
9 Lemmatization
10 POS tagging, morphological parsing
11 Syntactic parsing
12 Named entity recognition
13 Information extraction
14 Treebank annotation
15 General Review
16 Final Exam

Recomended or Required Reading

Textbook(s):
Biber Douglas, Conrad Susan & Reppen Randi (1998) Corpus Linguistics: Investigating Structure and Use. Cambridge: Cambridge University Press.

Supplementary Book(s):
Meyer, Charles F. (2002) English Corpus Linguistics: An Introduction. Cambridge: Cambridge University Press.

Planned Learning Activities and Teaching Methods

1. Lecture
2. Presentation
3. Software implementation

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 FIN FINAL EXAM
3 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 + FIN * 0.60
4 RST RESIT
5 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 + RST * 0.60


Further Notes About Assessment Methods

1. 1- Mid-term exam
2. 2- Final exam

Assessment Criteria

1. Learning outcomes 1 and 2 will be evaluated via the questions asked in the mid-term and final exams.
2. Learning outcomes 2 and 4 will be evaluated via data-based questions asked in mid-term and final exams.

Language of Instruction

Turkish

Course Policies and Rules

1- Class attendance of 70% is obligatory.
2- Absence from classes will not be considered as an excuse for the late submission of assignments/projects.
3- Copying and plagiarising in assignments and during exams will be evaluated with a 0 (zero) grade.

Contact Details for the Lecturer(s)

ozgun.kosaner@deu.edu.tr

Office Hours

wednesday: 12:00-12:30

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparation before/after weekly lectures 13 5 65
Preparation for Mid-term Exam 1 16 16
Preparation for Final Exam 1 22 22
Other (please indicate) 1 4 4
Final 1 1,5 2
Mid-term 1 1,5 2
TOTAL WORKLOAD (hours) 153

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14
LO.1555
LO.2555
LO.3555
LO.4555

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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