highlight - Solr patternreplacefilterfactory gives unmatching values -


i setup field type configured as

<fieldtype name="text" class="solr.textfield" positionincrementgap="100">           <analyzer type="index">           <charfilter class="solr.patternreplacecharfilterfactory"                  pattern="#(\w+)" replacement="htag.$1 $1"/>             <tokenizer class="solr.uax29urlemailtokenizerfactory"/>             <filter class="solr.commongramsfilterfactory" words="stopwords.txt" ignorecase="true"/>             <filter class="solr.stopfilterfactory" ignorecase="true" words="stopwords.txt" enablepositionincrements="true"/>             <filter class="solr.lowercasefilterfactory"/>           </analyzer>           <analyzer type="query">               <charfilter class="solr.patternreplacecharfilterfactory"                  pattern="#(\w+)" replacement="htag.$1"/>             <tokenizer class="solr.uax29urlemailtokenizerfactory"/>             <filter class="solr.commongramsfilterfactory" words="stopwords.txt" ignorecase="true"/>             <filter class="solr.stopfilterfactory" ignorecase="true" words="stopwords.txt" enablepositionincrements="true"/>             <filter class="solr.lowercasefilterfactory"/>           </analyzer>         </fieldtype>

my aim index both words , #words. #usopen indexed both usopen , #usopen.

and query parameters in addition hl factors hl.fl=text&hl.fragsize=0&hl.simple.pre=<tag>hl.simple.post=</tag>&hl.requirefieldmatch=true.

when query usopen, highlighting text value shown #usope<tag>n</tag> , when querying #usopen, text value shown <tag>usope</tag>n.

whats issue in above configuration , me fix it.

the charfilter changes offsets in indexed string not match original.

try using tokenfilter instead (patternreplacefilterfactory).

also, may make more sense normalize #something=>something both during indexing , searching , not try keeping both forms. long matches, care about.


-

Comments

Post a Comment

Popular posts from this blog

serialization - Convert Any type in scala to Array[Byte] and back -

i have following question: i have variable value in program declared value. i want convert value byte array.. how can serialize byte array , back? found examples related other types such double or int, not any. this should need. it's pretty similar how 1 in java. import java.io.{bytearrayinputstream, bytearrayoutputstream, objectinputstream, objectoutputstream} object serialization extends app { def serialise(value: any): array[byte] = { val stream: bytearrayoutputstream = new bytearrayoutputstream() val oos = new objectoutputstream(stream) oos.writeobject(value) oos.close stream.tobytearray } def deserialise(bytes: array[byte]): = { val ois = new objectinputstream(new bytearrayinputstream(bytes)) val value = ois.readobject ois.close value } println(deserialise(serialise("my test"))) println(deserialise(serialise(list(1)))) println(deserialise(serialise(map(1 -> 2)))) println(deserialise(seri
Read more

matplotlib support failed in PyCharm on OSX -

any idea how fix ? how can debug might problem ? not seem : https://youtrack.jetbrains.com/issue/py-15520 i in console: python 2.6.9 (unknown, jul 14 2015, 19:46:31) [gcc 4.2.1 compatible apple llvm 6.0 (clang-600.0.39)] on darwin >>> import matplotlib.pyplot plt matplotlib support failed the backend : >>> import matplotlib >>> print(matplotlib.rcparams['backend']) macosx
Read more

python - Matplotlib: TypeError: 'AxesSubplot' object is not callable -

i new programming please pardon me if rookie mistake. i writing python script have defined functions create subplots of distributions. i have slider widget end user change value of variables(mu , sigma) , see effect on distribution. here's code import numpy np print np.__version__ import scipy print scipy.__version__ scipy.stats import norm, lognorm, uniform import matplotlib print matplotlib.__version__ import matplotlib.pyplot plt matplotlib.widgets import slider, button, radiobuttons, checkbuttons #####calculating mean , standard deviation##### global mu_a1 mu_a1 = 1 global mu_b1 mu_b1 = 10 global mu_c1 mu_c1 = -13 global sigma_a1 sigma_a1 = 0.14 global sigma_b1 sigma_b1 = 1.16 global sigma_c1 sigma_c1 = 2.87 mu_x01 = -11 sigma_x01 = 1.9 #####_____##### #####generating random data##### a1 = 0.75*mu_a1 + (1.25 - 0.75)*sigma_a1*np.random.sample(10000) b1 = 8*mu_b1 + (12 - 8)*sigma_b1*np.random.sample(10000) c1 = -12*mu_c1 + 2*sigma_c1*np.random.sample(10000)
Read more