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Module Descriptor School of Computer Science and Statistics

Module CodeCS7IS4
Module NameText Analytics
Module Short Title
ECTS5
Semester TaughtHT (2nd Semester)
Contact Hours

2 lecture hours per week

Module PersonnelAssociate Professor Carl Vogel
Learning Outcomes

On successful completion of this module, students should be able to::

IS4LO1 Grasp the scope and limitations of finite state methods;

IS4LO2 Apply concepts from model theorywithin content analytics;

IS4LO3 Analyze, using qualitative and quantitative methods, entailments in natural language texts, distinguishing entailments from suggestions and associations;

IS4LO4 Comprehend and apply methods of sentiment analysis and metaphor understanding;

IS4LO5 Demonstrate ability to collaborate within a designated team;

IS4LO6 Provide constructive criticism within a scholarly peer review exercise;

IS4LO7 Collaboratively compose a scholarly research article informed by the literature, novel exercises in text analytics and responding to peer review.

Learning Aims

This module aims to provide students with a deep appreciation of  the theory and practice of  text analysis techniques.

Module Content

Specific topics addressed in this module include:

  • Finite state methods for representation and reasoning, model theoretic semantics
  • Meaning preserving syntactic alternations, text-entailment, text-associations
  • Formal language theory
  • Statistical Language Processing
  • Sentiment and metaphor analysis
Recommended Reading List

Ido Dagan, Dan Roth, Mark Sammons, Fabio Massimo Zanzotto (2013)

Recognizing Textual Entailment: Models and Applications. Morgan

Claypool.

Vivi Nastase, Preslav Nakov, Diarmuid Ó Séaghdha, Stan Szpakowicz (2013)

Semantic Relations Between Nominals. Morgan Claypool

Beth Levin (1993) English Verb Classes and Alternations: A Preliminary

Investigation. University of Chicago Press

Bing Liu (2014). Sentiment Analysis and Opinion Mining. Cambridge: Cambridge University Press

CD Manning and H. Schutze, Foundations of Statistical Natural Language Processing. Cambridge, MA: MIT Press, 1999

Module Prerequisites
Assessment Details

Coursework: 100%

An essay on an agreed topic will be due at the start of the final week of the semester. The essay will be constructed as a candidate submission to the International Workshop on Computational Semantics.

Assessment in the Supplemental session will be based on 100% coursework.

Module Website
Academic Year of Data2018/19