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Your proposal should be 3-5 pages in length. It should clearly present your goals and how you plan to achieve them.

  • Title page: Include a title, state which of the four types of projects you will do, identify the participants, and give a short abstract. Only one proposal is needed per team.
  • Intellectual Merit:* Clearly and precisely state the goals and motivations of your project. Distinguish your project from what has been done in previous work. This section should answer the questions, what problem are you trying to solve? Why is it important? What do we hope to learn from your efforts? What results are you striving for?
  • Resources: Your proposal should describe the resources (data, systems, knowledge resources, etc.) you will use and your plans for acquiring or creating them.
  • Research plan:* Describe your methodology and plans for evaluation. Define the tasks that need to be done. Each task should be numbered and described in detail. State which tasks each of you is responsible for. For example, if you are performing experiments, what is each experiment? What do you hope to learn from that experiment, i.e., why are you doing it? What evaluation metrics will you use? What is an appropriate baseline for comparison? For each task, estimate its data of completion. Deliverables:* Describe the delieverables you will produce. E.g., annotated data, annotation manual, learned model, feature-vector representations of the data, software tools, evaluation results, etc., as appropriate for your project.
  • Citations and Bibliography: The body of your proposal should include citations to relevant work which you have or plan to read. Include a formal bibliography of your references. Your references should include at least one meaningful citation to a paper or article from the assigned class readings to ensure that you work on something related to the course. Be sure to cite references properly.
  • Your proposal should also discuss any related work you are doing for another class or research project, and contrast that work with what you plan to do for your course project. It is fine to do something related to an existing project, but your course project may not be work or results you (or a partner) are already performing for another class or research project. For example, if you are already performing an annotation study, you could propose to develop an annotation scheme for related opinion/sentiment/modality information, and add your new types of annotations to your existing data. Or, if you are already working on an NLP system, you could propose to augment it with opinion/sentiment/modality information that you did not already plan to add this semester.

Below are specific comments about each type of project which are in addition to the specifications above.

* Option 1: Specify as clearly as you can exactly what type of thing you plan to annotate. You must have clear, declarative, specific statements of what you will annotate. If you can't do this, you won't be able to come up with an annotation scheme that works. Trust me! Identify the corpus you will annotate. Specify how much data you plan to annotate, and what work, if any, will be involved in preparing the corpus for annotation. Give a few examples of annotations of real naturally occuring sentences from the corpus. You won't be sure of your categories yet, so just use your current, initial ideas. It's fine (and expected) if your categories and definitions change between the proposal and final draft. Identify the tool you will use for annotation. Will you use an annotation system like Gate?
* Options 2 and 3: Specify the input and output of the system. Also, specify the type of strategy your system will use to perform its task, and the types of knowledge your system will use to perform it. If you are implementing an existing algorithm, include comments about which aspects you may focus on more than others, or other ways you may modify the algorithm or its use. If your experiments are new (algorithm, task, or resources) please state your hypothesis or hypotheses about what your experiments will show.
* Option 4: Specify the application system, the opinion/sentiment/modality knowledge the system will use, how it will use it, and your hypotheses about how the knowledge will benefit the application system. Also, specify how the system will acquire the opinion knowldge: will manual annotations be given as input? If so, which ones? If it will acquire the knowledge automatically, please specify how.

* Thanks to Dr. Litman for the ideas in this section.