2014年12月9日 (二) 09:44 Lr

2014-12-09T09:44:41Z

Caoli：/* test result */

2014-12-09T07:27:46Z

‎test result

Caoli：/* test result */

2014-12-09T07:17:50Z

‎test result

Caoli：/* test result */

2014-12-09T07:13:39Z

‎test result

Caoli：/* test result */

2014-12-09T07:05:38Z

‎test result

Lr：以“=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...”为内容创建页面

2014-12-09T02:03:31Z

以“=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...”为内容创建页面

新页面

=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：[http://lucene.apache.org/core/4_0_0/core/org/apache/lucene/search/similarities/TFIDFSimilarity.html]
[[文件:QQ截图20141128164958.png]]
:* 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==
* 计算公式
:* score(Q)=score(q_PTN)+score(q_ANS)+score(q_STD)
:* querynorm(Q),Q=q_PTN+q_ANS+q_STD
::* sumOfSquaredWeights = ∑{q.getBoost()*q.getBoost()*∑( idf(t) *t.getBoost() )^2},q={q_PTN , q_STD, q_ANS}
::* queryNorm(Q)= 1/sqrt(sumOfSquaredWeights)
:* field patern
::* 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_PTN)=querynorm(Q)*boost(q_PTN)
::* Norm
:*
*detail
:* filed: answer+pattern
score(q,filed-pattern)+score(q,filed-answer)

doc=0 score=0.15459718 shardIndex=-1|0.1545972 = (MATCH) product of:
0.23189577 = (MATCH) sum of:[all]
0.108532876 = (MATCH) product of:[filed:pattern]
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:[field:answer]
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)

=test result=
[[z-mert]]

@@ 第114行： / 第114行： @@
 =Z-MERT test result=
-[Z-MERT]
+[[Z-MERT]]
-*Test conditions
-:*(../res/corpus/20141016凉山州/3文本/testJ.txt)
-:*Only Lucene
-−
-*The default argument(patern:1.0 sq:1.0)
-:*test result:0.6697994987468672
-*Use MERT method and get the argument(patern:1.811676798378926, sq:1.0)
-:*test result:0.6779448621553885

@@ 第113行： / 第113行： @@
 .6666667 = coord(2/3)
-=test result=
+=Z-MERT test result=
 [Z-MERT]
 *Test conditions

@@ 第115行： / 第115行： @@
 =test result=
 [Z-MERT]
 *The default argument(patern:1.0 sq:1.0)
 :*test result:0.6697994987468672
 *Use MERT method and get the argument(patern:1.811676798378926, sq:1.0)
 :*test result:0.6779448621553885

←上一版本		2014年12月9日 (二) 07:13的版本
第114行：		第114行：

	=test result=		=test result=
−	[z-~~mert~~]	+	[Z-MERT]
		+	*The default argument(patern:1.0 sq:1.0)
		+	:*test result:0.6697994987468672
		+	*Use MERT method and get the argument(patern:1.811676798378926, sq:1.0)
		+	:*test result:0.6779448621553885

←上一版本		2014年12月9日 (二) 07:05的版本
第114行：		第114行：

	=test result=		=test result=
−	[[z-mert]]	+	[z-mert]

Multi query in multi field - 版本历史

2014年12月9日 (二) 09:44 Lr

Caoli：/* test result */

Caoli：/* test result */

Caoli：/* test result */

Caoli：/* test result */

Lr：以“=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...”为内容创建页面