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Chapter 3 Retrieval Evaluation Modern Information Retrieval Ricardo Baeza-Yates Berthier Ribeiro-Neto Hsu Yi-Chen, NCU MIS 88423043.

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Presentation on theme: "Chapter 3 Retrieval Evaluation Modern Information Retrieval Ricardo Baeza-Yates Berthier Ribeiro-Neto Hsu Yi-Chen, NCU MIS 88423043."— Presentation transcript:

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

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

3 Introduction Type of evaluation Functional analysis phase, and Error analysis phase Performance evaluation Performance of the IR system Performance evaluation  Response time/space required Retrieval performance evaluation  The evaluation of how precise is the answer set

4 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

5 Retrieval Performance Evaluation(Cont.) 評估以 batch query 為主的 IR 系統 collection Relevant Docs In Answer Set |Ra| Relevant Docs |R| Answer Set |A| Recall=|Ra|/|R| Precision=|Ra|/|A| Sorted by relevance

6 Precision versus recall curve R q ={d 3,d 5,d 9,d 25,d 39,d 44,d 56,d 89,d 123 } Ranking for query q: 1.d 123 * 2.d 84 3.d 56 * 4.d 6 5.d 8 6.d 9 * 7.d d d d 25 * 11.d d d d d 3 * 100% at10% 66% at 20% 50% at 30% Usally based on 11 standard recall levels:0%,10%,..100%

7 Precision versus recall curve For a single query Fig3.2

8 計算多個 query 的平均效能 P(r)= Σ (P i (r)/N q ) P(r)=average precision at the recall leval N q =number of queries used P i (r)= the precision at recall level r for the i-th query i=1 i=Nq

9 Interpolated precision R q ={d 3,d 56,d 129 } Let r j,j={0,1,2, …,10},be a reference to the j-th standard recall level P(r j )=max r i ≦ r ≦ r j+1 P(r)

10 兩個不同演算法的 Average recall versus precision figure

11 Single Value Summaries 之前的 average precision versus recall: 比較 retrieval algorithms over a set of example queries But! Individual query 的 performance 也很重要, 因為 : Average precision 可能會隱藏演算法中不正常的部分 可能需要知道, 兩個演算法中,對某特定 query 的 performance 為何 解決方法 : 考慮每一個 query 的 single precision value The single value should be interpreted as a summary of the corresponding precision versus recall curve 通常,single value summary 被用來當作某一個 recall level 的 precision 值

12 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 )

13 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 R q ) 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

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

15 Precision Histograms(cont.)

16 Summary Table Statistics 將所有 query 相關的 single value summary 放 在 table 中 如 the number of queries, total number of documents retrieved by all queries, total number of relevant documents were effectively retrieved when all queries are considered Total number of relevant documents retrieved by all queries …

17 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

18 Alternative Measures The Harmonic Mean, 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) 1 + F( j )= 1+b 2 r (j) b2b2 P (j) 1 + E( j )=1-

19 User-Oriented Measure 假設: Query 與使用者有相關, 不同使用 者有不同的 relevant docs coverage=|R k |/|U| Novelty=|R u |/|R u |+|R k | Coverage 越高, 系統找 到使用者期望的文件越 多 Noverlty 越高, 系統找 到許多使用者之前不知 道相關的文件越多

20 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 (|R u |+|R k |)/ |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|/|R k |

21 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

22 IR system 遇到的批評 Lacks a solid formal framework as a basic foundation 無解 ! 一個文件是否與查詢相關,是相當主觀的 ! Lacks robust and consistent testbeds and benchmarks 較早,發展實驗性質的小規模測試資料 1990 後, TREC 成立,蒐集上萬文件,提供給研究 團體作 IR 系統評量之用

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

24 The Documents Collection 由 SGML 編輯 WSJ AT&T Unveils Services to Upgrade Phone Networks Under Global Plan Janet GuyonWSJ Staff) New York American Telephone & Telegrapj Co. introduced the first of a newgeneration of phone service with broad… WSJ AT&T Unveils Services to Upgrade Phone Networks Under Global Plan Janet GuyonWSJ Staff) New York American Telephone & Telegrapj Co. introduced the first of a newgeneration of phone service with broad…

25 The Example Information Requests(Topics) 用自然語言將資訊需求描述出來 Topic number: 給不同類型的 topics Number:168 Topic:Financing AMTRAK Description: ….. Narrative:A …..

26 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

27 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

28 Other tasks: *Chinese Filtering Interactive *NLP(natural language procedure) Cross languages High precision Spoken document retrieval Query Task(TREC-7)

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

30 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,d b ] Bibliographic coupling connections:a list of triples[d 1,d 2,n cited ] Number of co-citations for each pair of articles[d 1,d 2,n citing ] A unique environment for testing retrieval algorithms which are based on information derived from cross-citing patterns

31 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

32 The Cystic Fibrosis Collection 有關於 “ 囊胞性纖維症 ” 的文件 Topics 和相關文件由具有此方面在臨床或 研究的專家所產生 Relevance scores 0:non-relevance 1:marginal relevance 2:high relevance

33 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

34 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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