Determine the best match for a token

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Reference no: EM131635118

Knowledge Technologies Project: Lexical Normalisation of Twitter Data

Overview -

The goal of this Project is to assess the performance of some spelling correction methods on the problem of tweet normalisation, and to express the knowledge that you have gained in a technical report. This aims to reinforce concepts in approximate matching and evaluation, and to strengthen your skills in data analysis and problem solving.

Deliverables

1. One or more programs, implemented in one or more programming languages, which must:

Determine the best match(es) for a token, with respect to a reference collection (dictionary)
Process the data input ?le(s), to determine the best match for each token
Evaluate the matches, with respect to the truly intended words, using one or more evaluation metrics

2. A README that brie?y details how your program(s) work(s). You may use any external re- sources for your program(s) that you wish: you must indicate these, and where you obtained them, in your README. The program(s) and README are required submission elements, but will not typically be directly assessed.

3. A technical report, of 1000-1600 words, which must:

Give a short description of the problem and data set
Brie?y summarise some relevant literature
Brie?y explain the approximate matching technique(s), and how it is (they are) used
Present the results, in terms of the evaluation metric(s) and illustrative examples
Contextualise the system's behaviour, based on the (admittedly incomplete) understanding from the subject materials
Clearly demonstrate some knowledge about the problem

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 Computational 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Reports that do not cite this work constitute plagiarism, and will be correspondingly assigned a mark of 0.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 ?ltering 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.)

The opinions expressed within the tweets in no way express the of?cial 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.

Attachment:- Assignment Files.rar

Reference no: EM131635118

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Reviews

len1635118

9/9/2017 6:38:46 AM

Australian Client, Message if(Any):- Lexical Normalisation of Twitter Data. Submission materials: Source code, README; PDF Report. Please go through them well! I need this as work is incomplete. The instructions mentioned to submit a READ ME file. It is not submitted. Please submit that as well urgently. 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.

9/9/2017 6:38:37 AM

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. 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.

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