Current postgraduate taught students
COMP61332: Text Mining (2010-2011)
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 spefications 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, ...
The School's Text Mining Research Group is closely linked with the National Centre for Text Mining, also hosted by the School, and this course unit is unique in benefitting from both the expertise and the technology available via the National Centre.
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.
|Programme outcome||Unit learning outcomes||Assessment|
|A1||Demonstrate a requisite understanding of selected concepts, terminology and issues related to text mining|
|A2||Demonstrate a requisite understanding of the fundamental techniques for text mining|
|A1||Demonstrate a requisite understanding of the relationship between text mining techniques and those of related areas (information retrieval, data mining)|
|A1||Demonstrate a requisite understanding of relevant (de facto) standards supporting text mining|
|B3||Explain the general principles of text mining and discuss the content and role of relevant key publications and (de facto) standards|
|B2 B3||Explain the difficulty of analysing different types of content in relation to user needs|
|B1 B2 B3||Explain how techniques for characterising the meaning of content and for semantic search are applied|
|B3||Discuss, critically analyse and evaluate current approaches in the field|
|C1 C3 C4||Be able to use the power of text mining for content analysis, search, personalisation and enterprise applications|
|G4||Appreciate issues of communication of information and knowledge discovery.|
|G4||Ability to support enterprise knowledge management activities.|
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 information retrieval: indexing, classification, query, evaluation.
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.
Architectures for text mining: Apache Unstructured Information Management Architecture; issues in design of common type systems for interoperability.
Evaluation of text mining systems: evaluation measures, role of evaluation challenges, usability evaluation, the U-Compare initiative.
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.
Core TextTitle: Text mining handbook: advanced approaches in analyzing unstructured data
Author: Feldman, Ronen and James Sanger
Supplementary TextTitle: Text mining: classification, clustering and applications
Author: Srivastava, Ashok and Mehran Sahami (eds.).
Publisher: Chapman & Hall
Supplementary TextTitle: Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition (2nd edition)
Author: Jurafsky, Daniel and James H. Martin
Publisher: Pearson International
Supplementary TextTitle: Text mining for biology and biomedicine
Author: Ananiadou, Sophia and John McNaught (eds.).
Publisher: Artech House
Supplementary TextTitle: Introduction to information retrieval
Author: Manning, Christopher D. and Prabhakar Raghavan and Hinrich Schutze
Publisher: Cambridge University Press
Available online at: http://www-csli.stanford.edu/~hinrich/information-retrieval-book.html