Lucene

check the detail of Lucene score

data

 d0 [{如何，怎么}} {办理，办} {户口，户口本} # 到当地派出所办理  # 如何办理户口
 d1 {办理，办} {户口，户口本} [{流程，步骤}] # 到当地派出所办理  # 如何办理户口
 d2 [{如何，怎么}} {办理，办} {身份证，身份} # 到当地派出所办理  # 如何办理身份证
 d3 {办理，办} {身份证} [{流程，步骤}] # 到当地派出所办理  # 如何办理身份证

搜索

query:"如何办理户口"  => question:如何 question:办理户口

result

 doc=0 score=0.114656925 shardIndex=-1|0.114656925 = (MATCH) product of:
   0.22931385 = (MATCH) sum of:
     0.22931385 = (MATCH) weight(question:如何 in 0) [DefaultSimilarity], result of:
       0.22931385 = score(doc=0,freq=1.0 = termFreq=1.0
 ), product of:
       0.4748871 = queryWeight, product of:
         1.287682 = idf(docFreq=2, maxDocs=4)
         0.3687922 = queryNorm
       0.48288077 = fieldWeight in 0, product of:
         1.0 = tf(freq=1.0), with freq of:
           1.0 = termFreq=1.0
         1.287682 = idf(docFreq=2, maxDocs=4)
         0.375 = fieldNorm(doc=0)
  0.5 = coord(1/2)

• 详细计算流score(query,d0)
• 参考公式：[1]

• tf("如何" in d0)=sqrt{frequency}= sqrt{1}=1
• idf("如何")=<math>1+ln( {numDocs}/{docFreq+1})=1+ln( {4}/{2+1} )
• 如何".getboost=1
• coord(如何，d0) : 0.5 = coord(1/2)

   coord(t,d)=overlap  /maxOverlap .
   overlap - the number of query terms matched in the document
   maxOverlap - the total number of terms in the query

• queryNorm(q)= 1/sqrt(sumOfSquaredWeights)=1/sqrt(sum(idf("如何")*1+idf("办理户口")))=1/sqrt(1*(1.287682*1.287682+2.386*2.386))=0.3687.

  sumOfSquaredWeights   =   q.getBoost()*q.getBoost()*∑( idf(t) *t.getBoost() )^2

mutli

data

 d0 [{如何，怎么}} {办理，办} {户口，户口本} # 到当地派出所办理  # 如何办理户口
 d1 {办理，办} {户口，户口本} [{流程，步骤}] # 到当地派出所办理  # 如何办理户口
 d2 [{如何，怎么}} {办理，办} {身份证，身份} # 到当地派出所办理  # 如何办理身份证
 d3 {办理，办} {身份证} [{流程，步骤}] # 到当地派出所办理  # 如何办理身份证

搜索

code BooleanQuery query = new BooleanQuery(); query.add(paternQuery, Occur.MUST); // or Occur.SHOULD if this clause is optional query.add(ansQuery, Occur.SHOULD); // or Occur.MUST if this clause is required query.add(sqQuery, Occur.SHOULD); search:

  +((question:如何 question:办理户口)^0.8) ((answer:如何 answer:办理户口)^0.2) ((standardq:如何 standardq:办理户口)^0.2)

result

 doc=0 score=0.15459718 shardIndex=-1|0.1545972 = (MATCH) product of:
 0.23189577 = (MATCH) sum of:
   0.108532876 = (MATCH) product of:
     0.21706575 = (MATCH) sum of:
       0.21706575 = (MATCH) weight(question:如何 in 0) [DefaultSimilarity], result of:
         0.21706575 = score(doc=0,freq=1.0 = termFreq=1.0
 ), product of:
           0.44952247 = queryWeight, product of:
             1.287682 = idf(docFreq=2, maxDocs=4)
             0.3490943 = queryNorm
           0.48288077 = fieldWeight in 0, product of:
             1.0 = tf(freq=1.0), with freq of:
               1.0 = termFreq=1.0
             1.287682 = idf(docFreq=2, maxDocs=4)
             0.375 = fieldNorm(doc=0)
     0.5 = coord(1/2)
   0.12336289 = (MATCH) sum of:
     0.032918826 = (MATCH) weight(answer:如何 in 0) [DefaultSimilarity], result of:
       0.032918826 = score(doc=0,freq=1.0 = termFreq=1.0
 ), product of:
         0.06779904 = queryWeight, product of:
           0.7768564 = idf(docFreq=4, maxDocs=4)
           0.087273575 = queryNorm
         0.48553526 = fieldWeight in 0, product of:
           1.0 = tf(freq=1.0), with freq of:
             1.0 = termFreq=1.0
           0.7768564 = idf(docFreq=4, maxDocs=4)
           0.625 = fieldNorm(doc=0)
     0.090444066 = (MATCH) weight(answer:办理户口 in 0) [DefaultSimilarity], result of:
       0.090444066 = score(doc=0,freq=1.0 = termFreq=1.0
 ), product of:
         0.11238062 = queryWeight, product of:
           1.287682 = idf(docFreq=2, maxDocs=4)
           0.087273575 = queryNorm
         0.8048013 = fieldWeight in 0, product of:
           1.0 = tf(freq=1.0), with freq of:
             1.0 = termFreq=1.0
           1.287682 = idf(docFreq=2, maxDocs=4)
           0.625 = fieldNorm(doc=0)
 0.6666667 = coord(2/3)