SCAMSEEK
Australia's largest Language Technology research project
The Scamseek project has a $1million budget to build a surveillance tool for
identifying financial scams on the Internet. This is Australia's largest
research project in language technology. The project aims to perform document
classification of internet pages. There are two principle types of documents
of concern. Those that give financial advice by unregistered advisors, and
unregistered investment schemes. The system that we are building has two major
features. Firstly, documents of known scams are to be analysed by linguists to
identify the features that make them distinctive. Secondly, machine-learning
strategies will used to analyse the documents to derive other features that
may be useful in classification and to extract named entities. The results of
the linguistic and machine learning investigations will be combined to create
a unified document classifier. The classifier will be fed by a web spider that
performs a 24hour/7day week search of the Internet for potential scam sites.
Partners
Capital Markets Co-operative Research Centre (CMCRC)
Australian Securities and Investment Commission (ASIC)
University of Sydney
University of Macquarie
SMARTS
General Application Information:
Intending applicants are encouraged to seek further information from the
contact person before submitting a formal application.
Applications should quote the reference no, address the selection criteria,
and include a CV, the names, addresses, e-mail, fax and phone number of two
confidential referees.
Forwarding Applications:
Address to:
The Personnel Officer, College of Sciences and Technology,
Carslaw Building, (F07)
The University of Sydney
NSW 2006
The University is a non-smoking workplace and is committed to the policies
and principles of equal employment opportunity and cultural diversity. The
University reserves the right not to proceed with any appointment for
financial or other reasons.
Software Engineer (2 positions)
Reference No. A07/003519
Two software engineers are required to develop to an acceptable industrial
standard computer system for the classification of documents. The successful
applicants would be expected to have superior skills at system design,
programming and testing, and excellent communication skills. Also essential
are: experience with the architecture, design, testing and formal
specifications of analytic software, experience in UNIX/LINUX and Windows
Operating Systems and integration of systems running on UNIX/LINUX server and
Microsoft clients. A degree in Information Technology with emphasis on
software engineering or equivalent is essential.
Computational Linguist
Reference No. A07/003520
A Computational Linguist is required to research appropriate machine learning
solutions for document classification, and the incorporation of semantic
analysis into a document classification system. The successful applicant would
be expected to have superior programming skills, excellent communication
skills and research and investigative skills. Also essential are: experience
in Software systems development, the development of computational linguistics
systems and experience in UNIX/LINUX and Windows Operating Systems. A degree
in Information Technology with associated studies in linguistics or languages,
or an Arts degree with associated studies in Information Technology (or
equivalent) is essential.
Linguist
Reference No. A07/003521
A Linguist is required to conduct linguistic analysis (semantic and
lexico-grammatical) of documents for the purpose of identifying the
distinctive elements in the texts that can be used in an industrial standard
computer system for the classification of documents. The successful applicants
would be expected to be able to analyse the semantic and lexico-grammatical
features of text. Also essential are: experience in the analysis of text for
identifying specific semantic phenomena, use of software tools in linguistic
analysis. A degree in Linguistics (or equivalent) is essential.
The positions are full-time fixed term for six months, subject to the
completion of a satisfactory probation period for new appointees. For further
information and a copy of the duty statement for the above positions, contact
Professor Jon Patrick on 9351 3524, fax 9351 3838, or e-mail
jonpat@it.usyd.edu.au. Further information can also be found at
http://www.infosys.usyd.edu.au
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