# Chapter 3 Retrieval Evaluation

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Chapter 3 Retrieval Evaluation
Modern Information Retrieval Ricardo Baeza-Yates Berthier Ribeiro-Neto Hsu Yi-Chen, NCU MIS

Outline Introduction Retrieval Performance Evaluation
Recall and precision Alternative measures Reference Collections TREC Collection CACM&ISI Collection CF Collection Trends and Research Issues

Introduction Type of evaluation Performance of the IR system
Functional analysis phase, and Error analysis phase Performance evaluation Performance of the IR system Response time/space required Retrieval performance evaluation The evaluation of how precise is the answer set 依照設計的目的，評估系統功能是否有達成需求 :測試系統每一個功能是否符合需求

Retrieval performance evaluation for IR system
Goodness of retrieval strategy S = the similarity between Set of retrieval documents by S Set of relevant documents provided by specialists quantified by Evaluation measure

Retrieval Performance Evaluation(Cont.)

Precision versus recall curve
Rq={d3,d5,d9,d25,d39,d44,d56,d89,d123} Ranking for query q: 11.d38 12.d48 13.d250 14.d11 15.d3* 1.d123* 2.d84 3.d56* 4.d6 5.d8 6.d9* 7.d511 8.d129 9.d187 10.d25* 100% at10% 66% at 20% 50% at 30% Usally based on 11 standard recall levels:0%,10%,..100%

Precision versus recall curve
For a single query Fig3.2

P(r)=average precision at the recall leval Nq=number of queries used Pi(r)=the precision at recall level r for the i-th query i=Nq i=1

Interpolated precision
Rq={d3,d56,d129} Let rj,j={0,1,2,…,10},be a reference to the j-th standard recall level P(rj)=max ri≦ r≦ rj+1P(r)

Single Value Summaries

Average Precision at Seen Relevant Documents
Averaging the precision figures obtained after each new relevant document is observed. F3.2,( )/5=0.57 此方法對於很快找到相關文件的系統是相當有利的(相關文件被排在越前面,precision值越高)

R-Precision Computing the precision at the R-th position in the ranking(在R 篇文章中出現相關文章數目的比例) R:the total number of relevant documents of the current query(total number in Rq) Fig3.2:R=10,value=0.4 Fig3.3,R=3,value=0.33 易於觀察每一個單一query的演算法performance

Precision Histograms 利用長條圖比較兩個query的R-precision值
RPA/B(i )=RPA(i )-RPB(i ) RPA(i),RPB(i):R-precision value of A,B for i-th query Compare the retrieval performance history of two algorithms through visual inspection

Precision Histograms(cont.)

Summary Table Statistics

Precision and Recall 的適用性
Maximum recall值的產生，需要知道所有文件相關的背景知識 Recall and precision是相對的測量方式，兩者要合併使用比較適合。 Measures which quantify the informativeness of the retrieval process might now be more appropriate Recall and precision are easy to define when a linear ordering of the retrieved documents is enforced

Alternative Measures The Harmonic Mean The E Measure-加入喜好比重 F(j)=
,介於0,1 The E Measure-加入喜好比重 b=1,E(j)=F(j) b>1,more interested in precision b<1,more interested in recall 2 r(j) 1 P(j) + F(j)= 1+b2 r(j) b2 P(j) 1 + E(j)=1-

User-Oriented Measure

User-Oriented Measure(cont.)
relative recall:系統找到的相關文章數佔使用者預期找到的文章數比例 (|Ru|+|Rk|)/ |U| Recall effort:使用者期望找到的相關文章數佔符合使用者期望的相關文章數(the number of documents examined in an attempt to find the expected relevant documents) |U|/|Rk|

Reference Collection 用來作為評估IR系統reference test collections
TIPSTER/TREC:量大，實驗用 CACM,ISI:歷史意義 Cystic Fibrosis :small collections,relevant documents由專家研討後產生

IR system遇到的批評 Lacks a solid formal framework as a basic foundation

TREC (Text REtrieval Conference)
Initiated under the National Institute of Standards and Technology(NIST) Goals: Providing a large test collection Uniform scoring procedures Forum 7th TREC conference in 1998: Document collection:test collections,example information requests(topics),relevant docs The benchmarks tasks

The Documents Collection

The Example Information Requests(Topics)

The relevant Documents for Each Example Information Request
The set of relevant documents for each topic obtained from a pool of possible relevant documents Pool:由數各參與的 IR系統中所找到的相關文件，依照相關性排序後的前K個文章。 K通常為100 最後透過人工鑑定，判斷是否為相關文件 ->pooling method 相關文件有數個組合的pool取得 不在pool內的文件視為不相關文件

The (Benchmark)Tasks at the TREC Conferences
ad hoc task: Receive new requests and execute them on a pre-specified document collection routing task Receive test info. Requests,two document collections first doc:training and tuning retrieval algorithm Second doc:testing the tuned retrieval algorithm

*NLP(natural language procedure) Cross languages High precision Spoken document retrieval Query Task(TREC-7)

Evaluation Measures at the TREC Conferences
Summary table statistics Recall-precision Document level averages* Average precision histogram

The CACM Collection Small collections about computer science literature Text of doc structured subfields word stems from the title and abstract sections Categories direct references between articles:a list of pairs of documents[da,db] Bibliographic coupling connections:a list of triples[d1,d2,ncited] Number of co-citations for each pair of articles[d1,d2,nciting] A unique environment for testing retrieval algorithms which are based on information derived from cross-citing patterns

The ISI Collection ISI 的test collection是由之前在ISI(Institute of Scientific Information) 的Small組合而成 這些文件大部分是由當初Small計畫中有關cross-citation study中挑選出來 支持有關於terms和cross-citation patterns的相似性研究

The Cystic Fibrosis Collection

Characteristics of CF collection
Relevance score均由專家給定 Good number of information requests(relative to the collection size) The respective query vectors present overlap among themselves 利用之前的query增加檢索效率

Trends and Research Issues
Interactive user interface 一般認為feedback的檢索可以改善效率 如何決定此情境下的評估方式(Evaluation measures)? 其它有別於precise,recall的評估方式研究

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