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Group 3 Chad Mills Esad Suskic Wee Teck Tan. Outline  System and Data  Document Retrieval  Passage Retrieval  Results  Conclusion.

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Presentation on theme: "Group 3 Chad Mills Esad Suskic Wee Teck Tan. Outline  System and Data  Document Retrieval  Passage Retrieval  Results  Conclusion."— Presentation transcript:

1 Group 3 Chad Mills Esad Suskic Wee Teck Tan

2 Outline  System and Data  Document Retrieval  Passage Retrieval  Results  Conclusion

3 System and Data DevelopmentTesting TREC 2004 TREC 2005  System: Indri  Data:

4 Document Retrieval  Baseline: Remove “?” Add Target String  MAP: 0.307

5 Document Retrieval  Attempted Improvement 1: Settings From Baseline Rewrite “When was…” questions as “[target] was [last word] on” queries  MAP: Best so far: 0.307

6 Document Retrieval  Attempted Improvement 2: Settings From Baseline Remove “Wh” words Remove Stop Words Replaced Pronoun with Target String  MAP: Best so far: “Wh” / Stop WordsWhat, Who, Where, Why, How many, How often, How long, Which, How did, Does, is, the, a, an, of, was, as Pronounhe, she, it, its, they, their, his

7 Document Retrieval  Attempted Improvement 3: Settings From Improvement 2 Index Stemmed (Krovetz Stemmer)  MAP: Best so far: 0.319

8 Document Retrieval  Attempted Improvement 4: Settings From Improvement 3 Remove Punctuations Remove Non Alphanumeric Characters  MAP: Best so far: 0.336

9 Document Retrieval  Attempted Improvement 5: Settings From Improvement 4 Remove Duplicate Words  MAP: Best so far: 0.374

10 Passage Retrieval  Baseline: Out-of-the-box Indri Same Question Formulation Changed “#combine(“ to “#combine[passageX:Y](” Passage Window, Top 20, No Re-ranking X=40 Y=20 Strict0.126 Lenient0.337 X=200 Y=100 Strict0.414 Lenient0.537

11 Passage Retrieval  Attempted Re-ranking Mallet MaxEnt Classifier Training Set TREC 2004 ○ 80% Train : 20% Dev ○ Split by Target ○ Avoid Cheating e.g. Question 1.* all in either Train or Dev Labels: + Passage has Correct Answer - Passage doesn’t have Answer

12 Passage Retrieval  Features used: For both Passage and Question+Target: ○ unigram, bigram, trigram ○ POS tags – unigram, bigram, trigram Question/Passage Correspondence: ○ # of Overlapping Terms (and bigrams) ○ Distance between Overlapping Terms  Tried Top 20 Passages from Indri, and Expanding to Top 200 Passages

13 Passage Retrieval  Result: all attempts were worse than before Example confusion matrix:  Many negative examples, 67-69% accurate on all feature combinations tried

14  Indri was very good to start with  E.g. Q10.1 Passage Re-Ranking Indri RankHas Answer 1Yes 2No 3Yes 4 5No Our RankHas AnswerP(Yes)P(No)Indri Rank 1No No Yes No Yes  Our first 2 were wrong, only 1 of Indri’s top 5 in our top 5  If completely replacing rank, must be very good  Many low confidence scores (e.g. 7.6% P(Yes) was best)  Slight edit to Indri ranking less bad, but no good system found  E.g. bump high-confidence Yes to top of list, leave others in Indri order

15 Results  TREC 2004:  TREC 2005: MAP0.377 Strict MRR0.414 Lenient MRR0.537 MAP0.316 Strict MRR0.366 Lenient MRR0.543

16 References  Fang – “A Re-examination of Query Expansion Using Lexical Resources”  Tellex – “Quantitative Evaluation of Passage Retrieval Algorithms for Question Answering”

17 Conclusions  Cleaned Input  Small Targeted Stop Word List  Minimal Setting  Indri Performs PR Well OOTB  Re-ranking Implementation Needs to be Really Good Feature Selection didn’t Help Slight Adjustment Instead of Whole Different Ranking Might Help


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