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COMP61332 Text Mining syllabus 2018-2019

COMP61332 Text Mining

Level 6
Credits: 15
Enrolled students: 102

Course leader: Riza Batista-Navarro

Additional staff: view all staff

Assessment methods

  • 50% Written exam
  • 50% Coursework
Sem 2 P3 Lecture 2.19 Tue 09:30 - 12:30 -
Sem 2 P3 Lab 2.25 (A+B) Tue 13:30 - 16:30 -
Sem 2 P3 Lab 2.25 (A+B) Tue 16:30 - 17:30 -
Themes to which this unit belongs
  • Making Sense of Complex Data
  • Computer Science units for ACSwITM students (semester 2)
  • Information Management 2


We naturally record and communicate much of our knowledge in textual form. However, for many years now the rate of growth of textual information has been such that the individual struggles to keep up to date in his fields of interest. Well before the advent of the Web, people suffered from information overload and information overlook, and today the ease of electronic publication has only exacerbated these problems. It has also long been realised that the vast archives of text at our disposal contain hidden, unsuspected, potentially valuable information: nowhere explicitly stated, but only discoverable through (until recently) serendipity, or painstaking manual identification and linking of often disparate chunks of knowledge.

Text mining has evolved in recent years as a way of mitigating information overload and information overlook, and of helping us discover new knowledge from old. To do this, it employs a battery of techniques from information retrieval, natural language processing and data mining. Although the holy grail of text mining is the discovery of previously unsuspected knowledge, text mining techniques find application in a wide number of areas, to do essentially with the organising, selecting, filtering, combining, association and exploitation of information. Text mining goes far beyond, and is not to be confused with, classic information retrieval (conventional search engine technology).

What makes text mining challenging is the combination of: the core problems of natural language processing (how to make sense of unstructured data (text), how to deal with the ambiguity inherent in language that humans naturally cope with); the problems that arise when dealing with very large amounts of electronic text and the even larger amounts of representations derived from these during processing; the problems of integrating different components, with different input/output specifications and different intermediate representations, in text mining workflows to accomplish sophisticated tasks; and the non-trivial problem of matching actual system capabilities to user expectations and requirements.

Applications of text mining are many and varied: systems to find promising targets for drug discovery, to support systematic reviews, to match CVs to job profiles, to carry out business news analysis for competitive intelligence, to aid discovery of disease-gene associations, to monitor reports of terrorist activity, to help generate hypotheses for scientific research, to direct customer queries to appropriate support staff, to discover positive and negative opinions on topics of interest, to discover hot topics and trends, ...


This course unit aims to provide students with an understanding of principles, issues, techniques and solutions connected with text mining, and to enable them to gain knowledge of how recent advances in text mining relate to innovative approaches to organising, characterising, finding and exploiting large scale textual information in the search for new knowledge.


Introduction: background, motivation, dealing with information overload and information overlook, unstructured vs. (semi-)structured data, evolving information needs and knowledge management issues, enhancing user experience of information provision and seeking, the business case for text mining.

The text mining pipeline: information retrieval, information extraction and data mining.

Fundamentals of natural language processing: linguistic foundations, levels of linguistic analysis.

Approaches to text mining: rule-based vs. machine learning based vs. hybrid; generic vs. domain specific; domain adaptation.

Dealing with real text: text types, document formats and conversion, character encodings, markup, low-level processes (sentence splitting, tokenisation, part of speech tagging, chunking).

Information extraction: term extraction, named entity recognition, relation extraction, fact and event extraction; partial analysis vs. full analysis.

Data mining and visualisation of results from text mining.

Evaluation of text mining systems: evaluation measures, role of evaluation challenges, usability evaluation.

Resources for text mining: annotated corpora, computational lexica, ontologies, computational grammars; design, construction and use issues.

Issues in large scale processing of text: distributed text mining, scalable text mining systems.

A sampler of text mining applications and services; case studies.

Teaching methods


15 hours of lectures.


15 hours of labs.


5 hours of consultation

Feedback methods

  • Oral feedback in class.
  • Email.
  • Course Web site.

Study hours

  • Assessment written exam (2 hours)
  • Lectures (15 hours)
  • Practical classes & workshops (20 hours)

Transferable skills

Employability skills

  • Analytical skills
  • Problem solving
  • Research

Employability skills

  • Analytical skills
  • Problem solving
  • Research

Learning outcomes

Programme outcomeUnit learning outcomesAssessment
A1Demonstrate a requisite understanding of selected concepts, terminology and issues related to text mining
  • Examination
A2Demonstrate a requisite understanding of the fundamental techniques for text mining
  • Examination
A1Demonstrate a requisite understanding of the relationship between text mining techniques and those of related areas (information retrieval, data mining)
  • Examination
A1Demonstrate a requisite understanding of relevant (de facto) standards supporting text mining
  • Examination
B3Explain the general principles of text mining and discuss the content and role of relevant key publications and (de facto) standards
  • Examination
B2 B3Explain the difficulty of analysing different types of content in relation to user needs
  • Examination
B1 B2 B3Explain how techniques for characterising the meaning of content and for semantic search are applied
  • Examination
B3Discuss, critically analyse and evaluate current approaches in the field
  • Individual coursework
  • Examination
C1 C3 C4Be able to use the power of text mining for content analysis, search, personalisation and enterprise applications
G4Appreciate issues of communication of information and knowledge discovery.
G4Ability to support enterprise knowledge management activities.

Reading list

Text mining handbook: advanced approaches in analyzing unstructured dataFeldman, Ronen and James Sanger9780521836579CUP2008
Introduction to information retrievalManning, Christopher D. and Prabhakar Raghavan and Hinrich Schutze9780521865715Cambridge University Press2008
Linked Lexical Knowledge Bases: foundations and applicationsGurevych, Iryna and Judith Eckle-Kohler and Michael Matuschek9781627059749Morgan & Claypool2016
Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition (2nd edition)Jurafsky, Daniel and James H. Martin9780135041963Pearson International2009
Handbook of linguistic annotation: volumes I and IIIde, Nancy and James Pustejovsky (eds.).9789402408799Springer2017
Text mining: classification, clustering and applicationsSrivastava, Ashok and Mehran Sahami (eds.).9781420059403Chapman & Hall2009
Text mining for biology and biomedicineAnaniadou, Sophia and John McNaught (eds.).158053984XArtech House2006

Additional notes

Course unit materials

Links to course unit teaching materials can be found on the School of Computer Science website for current students.