Changes/Updates to the Project Specifications If we require any (hopefully small-scale) changes or clarifications to the project specifications, they will be posted on the LMS. Any addendums will supersede information included in this document. Academic Misconduct For most people, collaboration will form a natural part of the undertaking of this project. However, it is still an individual task, and so reuse of ideas or excessive influence in algorithm choice and de- velopment will be considered cheating. We will be checking submissions for originality and will invoke the University’s Academic Misconduct policy (http://academichonesty.unimelb. edu.au/policy.html) where inappropriate levels of collusion or plagiarism are deemed to have taken place.

Critical Analysis: (50% of the marks available) You will explain the practical behaviour of your systems, referring to the theoretical behaviour where appropriate. You will support your observations with evidence, in terms of illustrative examples and evaluation metrics. You will derive some knowledge about the problem of tweet normalisation. Report Quality: (30% of the marks available) You will produce a formal report, which is commensurate in style and structure with a (short) research paper. You must express your ideas clearly and concisely, and remain within the word limit (1000-1600 words). You will include a short summary of related research. We will post a marking rubric to indicate what we will be looking for in each of these categories when marking.

The opinions expressed within the tweets in no way express the official views of the University of Melbourne or any of its employees; using the data in a teaching capacity does not constitute endorsement of the views expressed within. The University accepts no responsibility for offence caused by any content contained within this data. If you object to these Terms, please contact us as soon as possible. Assessment Criteria Method: (20% of the marks available) You will attempt a representative sample of approximate matching techniques, which is adequate for deriving some knowledge about the problem of tweet normalisation. You will evaluate your method(s) formally.

Terms of Use By using this data, you are becoming part of the research community — consequently, as part of your commitment to Academic Honesty, you must cite the curators of the dataset in your report, as the following publication: Bo Han and Timothy Baldwin (2011) Lexical normalisation of short text messages: Makn sens a #twitter. In Proceedings of the 49th Annual Meeting of the Association for Compu- tational Linguistics, Portland, USA. pp. 368–378. Reports that do not cite this work constitute plagiarism, and will be correspondingly assigned a mark of 0. Please note that the dataset is a sub-sample of actual data posted to Twitter, with almost no filtering whatsoever. Unfortunately, the Internet is a place where freedom of speech is both empowering and harmful: consequently, some of the information expressed in the tweets is undoubtedly in poor taste. We would ask you to please look beyond this to the task at hand, as much as possible. (For example, it is generally not necessary to actually read the tweets themselves.)