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Kees van Deemter Generation of Referring Expressions: a crash course

Kees van Deemter Generation of Referring Expressions: a crash course. Background information and Project HIT 2010. 1. Background Information. A survey paper (draft!) on Generation of Referring Expressions, which I’m writing with Emiel Krahmer (University of Tilburg).

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Kees van Deemter Generation of Referring Expressions: a crash course

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  1. Kees van DeemterGeneration of Referring Expressions: a crash course Background information and Project HIT 2010

  2. 1. Background Information A survey paper (draft!) on Generation of Referring Expressions, which I’m writing with Emiel Krahmer (University of Tilburg). Go to the course web page:http://www.csd.abdn.ac.uk/~kvdeemte/Harbin-2010/ then click on Survey (draft, in pdf format) Particularly relevant for you: Sections 1,2,5.

  3. 2. Project:Describing objects in a Knowledge Base

  4. After the lecture and some reading, you will know about reference • To refer to an object is to identify it • GRE algorithms produce descriptions that refer in this strict sense • In daily life, it is often important not just to identify an object, but to say what’s special about it • Example: Given a database of digital cameras, where one camera is highlighted, describe this camera.

  5. Example 1 Price Resol Weight Nikon 300p 20mp 660gr Canon 200p 11mp 650gr Kodak 50p 6mp 670gr Identifying the Nikon is easy, e.g. “660gr” But“660gr” is less useful than“300p”, or “most expensive”, or “most expensive, highest resolution”

  6. PROJECT 1: Describing individual items • Choose a domain of products and a KB that contains products of this kind (e.g., digital cameras, chosen from an existing web page) • Construct an NLG program that can describe any given item in the KB, in such a way that it is likely to be useful to a customer • Invent an evaluation procedure for your algorithm. Purpose: test the usefulness of the descriptions generated

  7. PROJECT 1 • Your program should work on any KB that has the required format • But some objects may be difficult to describe in a useful way (e.g., if some other objects are very similar to the highlighted object) • I want you to focus on content determination (i.e., choice of properties is more important than choice of words) • I don’t expect you to have time to actually perform the evaluation

  8. PROJECT 2: Compare & Contrast • Similar to PROJECT 1, but • Given a KB with m entities in it, and0≤n≤m of these m entities are highlighted:compare and contrast these n • Special cases to watch out for:n=1n=m

  9. Example 2 Price Resol Weight Nikon 300p 20mp 660gr Canon 200p 11mp 650gr Kodak 50p 6mp 670gr In English: “These are the two most expensive cameras in this KB. Of these two, the Nikon has the highest resolution.” Focussing on content: Common: most expensive Contrast: Resol(Nikon) > Resol(Canon)

  10. Your report should contain • What types of inputs your program accepts • A description of your algorithm • Examples of output texts (+ the inputs from which they were generated) • Discussion of pros and cons of your approach • What would you do differently next time? • What would you do next if you had time? • Installation & usual manuals for your program

  11. Summing up • This project comes in two different flavours • In each case, you’ll be looking at a possible generalisation of GRE: • Project 1: Descriptions that do more (and probably less!) than identification • Project 2: Compare & Contrast • Questions, or alternative ideas? Let me know!

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