The course takes place in period

3 (2022-01-01 to 2022-03-13)

Level/category

Professional studies

Teaching language

English

Type of course

Compulsory

Cycle/level of course

First

Recommended year of study

3

Total number of ECTS

5 cr

Competency aims

This study unit focuses on the following
competences:

  • Analysis of unstructured data
  • Development of software that applies relevant
    algorithms within the field of text analysis.

Learning outcomes

At the end of the study unit:

  • You are aware of important questions and basic
    terminology, methods and techniques within the
    field of text analytics
  • You develop practical experiences within
    processing natural languages
  • You learn NLP programming
  • You are able to build a practical application
    within the field of NLP through a course project

Course contents

Basic Text Processing: Regular expressions, Text
Normalization, Edit Distances

Language Modelling with N-Grams

Naïve Bayes, Text Classification

Logistic Regression + Lexicons for sentiment

Information Retrieval and Query processing

Topic Modelling and Word Embeddings/Vectors

Neural Network Models for NLP + Deep learning
models: BERT and Transformer models

Prerequisites and co-requisites

  • Data processing

Recommended or required reading

Jurafsky, Dan, and James H. Martin. "Speech and
language processing. Vol. 3." US: Prentice Hall
(2014). (online copy available:
https://web.stanford.edu/~jurafsky/slp3/ External link )

Christopher D. Manning, Prabhakar Raghavan and
Hinrich Schütze. Introduction to information
retrieval. Vol. 39. Cambridge: Cambridge
University Press, 2008.
https://nlp.stanford.edu/IR External link-
book/html/htmledition/irbook.html

Study activities

  • Lectures - 40 hours
  • Project- and production work/artistic activities - 95 hours

Workload

  • Total workload of the course: 135 hours
  • Of which autonomous studies: 135 hours
  • Of which scheduled studies: 0 hours

Mode of Delivery

Participation in tuition

Assessment methods

Course Project

Assessment requirements

Course project

Teacher

Mukkamala Raghava

Examiner

Karlsson Jonny

Group size

No limit (34 students enrolled)

Assignments valid until

12 months after course has ended

Course enrolment period

2021-12-22 to 2022-01-14

Assessment methods

Date will be announced later - Other assignments

Room reservations
Date Time Room Title Description Organizer
2022-01-31 08:30 - 12:30 Deskriptiv analytik - Data/Text mining Lektionen hålls online i Zoom. Länk publiceras på Itslearning före lektionens början.
2022-02-01 08:30 - 12:30 Deskriptiv analytik - Data/Text mining Lektionen hålls online i Zoom. Länk publiceras på Itslearning före lektionens början.
2022-02-14 08:30 - 12:30 Deskriptiv analytik - Data/Text mining Lektionen hålls online i Zoom. Länk publiceras på Itslearning före lektionens början.
2022-02-15 08:30 - 12:30 Deskriptiv analytik - Data/Text mining Lektionen hålls online i Zoom. Länk publiceras på Itslearning före lektionens början.
2022-02-21 08:30 - 12:30 Deskriptiv analytik - Data/Text mining Lektionen hålls online i Zoom. Länk publiceras på Itslearning före lektionens början.
2022-02-22 08:30 - 12:30 Deskriptiv analytik - Data/Text mining Lektionen hålls online i Zoom. Länk publiceras på Itslearning före lektionens början.
2022-02-28 08:30 - 12:30 Deskriptiv analytik - Data/Text mining Lektionen hålls online i Zoom. Länk publiceras på Itslearning före lektionens början.
2022-03-01 08:30 - 12:30 Deskriptiv analytik - Data/Text mining Lektionen hålls online i Zoom. Länk publiceras på Itslearning före lektionens början.
2022-03-04 08:30 - 12:30 Deskriptiv analytik - Data/Text mining Lektionen hålls online i Zoom. Länk publiceras på Itslearning före lektionens början.
2022-03-07 08:30 - 12:30 Deskriptiv analytik - Data/Text mining Lektionen hålls online i Zoom. Länk publiceras på Itslearning före lektionens början.
2022-03-10 08:30 - 12:30 Deskriptiv analytik - Data/Text mining Lektionen hålls online i Zoom. Länk publiceras på Itslearning före lektionens början.

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