124.11.lec10

124.11.lec10 - CS 124/LINGUIST 180 From Click to edit...

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Click to edit Master subtitle style 1/10/09 Dan Jurafsky Lecture 10: Information Retrieval III Evaluation and Assorted IR/Web topics Thanks to Chris Manning for these slides from his CS 276 Information Retrieval and Web Search class!
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Slide from Chris Manning's 276 class This lecture How do we know if our results are any good? Evaluating a search engine Benchmarks Precision and recall Remaining assorted IR/Web topics: Results summaries: Making our good results usable to a user Relevance Feedback Searching the Web Slide from Chris Manning's 276
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Click to edit Master subtitle style 1/10/09 Evaluating search engines
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Slide from Chris Manning's 276 class Measures for a search engine How fast does it index Number of documents/hour (Average document size) How fast does it search Latency as a function of index size Expressiveness of query language Ability to express complex information needs Speed on complex queries Uncluttered UI Is it free?
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Slide from Chris Manning's 276 class Measures for a search engine All of the preceding criteria are measurable : we can quantify speed/size; we can make expressiveness precise The key measure: user happiness What is this? Speed of response/size of index are factors But blindingly fast, useless answers won’t make a user happy Need a way of quantifying user happiness
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Slide from Chris Manning's 276 class Measuring user happiness Issue: who is the user we are trying to make happy? Depends on the setting Web engine : user finds what they want and return to the engine Can measure rate of return users eCommerce site : user finds what they want and make a purchase Is it the end-user, or the eCommerce site, whose happiness we measure? Measure time to purchase, or fraction of searchers who become buyers?
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Slide from Chris Manning's 276 class Measuring user happiness Enterprise (company/govt/academic): Care about “user productivity” How much time do my users save when looking for information? Many other criteria having to do with breadth of access, secure access, etc.
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Slide from Chris Manning's 276 class Happiness: elusive to measure Most common proxy: relevance of search results But how do you measure relevance? We will detail a methodology here, then examine its issues Relevant measurement requires 3 elements: 1. A benchmark document collection 2. A benchmark suite of queries 3. A usually binary assessment of either Relevant or Nonrelevant for each query and Slide from Chris Manning's
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Slide from Chris Manning's 276 class Evaluating an IR system Note: the information need is translated into a query Relevance is assessed relative to the information need not the query E.g., Information need : I'm looking for information on whether drinking red wine is more effective at reducing your risk of heart attacks than white wine.
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124.11.lec10 - CS 124/LINGUIST 180 From Click to edit...

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