Erick, I am not sure when you say "the only available terms are "not" and "necessarily"" is totally correct. I go into the schema browser and I can see that there are two terms "not" and "not necessarily" with the correct count. Unless these are not the terms you are talking about. Can you explain to me what these are exactly.
http://imgur.com/m82CH2f I see what you are saying, it may be best for me to do the entity extraction separately, and put the terms into a special field, although I would like the terms to be highlighted (or have some type of position so I can highlight it). Regards, Kevin On Mon, Dec 28, 2015 at 12:49 PM, Erick Erickson <erickerick...@gmail.com> wrote: > bq: so I cannot copy this field to a text field with a > keywordtokenizer or strfield > > 1> There is no restriction on whether a field is analyzed or not as far as > faceting is concerned. You can freely facet on an analyzed field > or String field or KeywordTokenized field. As Binoy says, though, > faceting on large analyzed text fields is dangerous. > > 2> copyField directives are not chained. As soon as the > field is received, before _anything_ is done the raw contents are > pushed to the copyField destinations. So in your case the source > for both copyField directives should be "content". Otherwise you > get into interesting behavior if you, say, copyField from A to B and > have another copyField from B to A. I _suspect_ this is > why you have no term info available, but check.... > > 3> This is not going to work as you're trying to implement it. If you > tokenize, the only available terms are "not" and "necessarily". There > is no "not necessarily" _token_ to facet on. If you use a String > or KeywordAnalylzed field, likewise there is no "not necessarily" > token, there will be a _single_ token that's the entire content of the > field > (I'm leaving aside, for instance, WordDelimiterFilterFactory > modifications...). > > One way to approach this would be to recognize and index synthetic > tokens representing the concepts. You'd pre-analyze the text, do your > entity recognition and add those entities to a special "entity" field or > some such. This would be an unanalyzed field that you facet on. Let's > say your entity was "colon cancer". Whenever you recognized that in > the text during indexing, you'd index "colon_cancer", or "disease_234" > in your special field. > > Of course your app would then have to present this pleasingly, and > rather than the app needing access to your dictionary the "colon_cancer" > form would be easier to unpack. > > The fragility here is that changing your text file of entities would > require > you to re-index to re-inject them into documents. > > You could also, assuming you know all the entities that should match > a given query form facet _queries_ on the phrases. This could get to be > quite a large query, but has the advantage of not requiring re-indexing. > So you'd have something like > facet.query=field:"not necessarily"&facet.query=field:certainly > etc. > > Best, > Erick > > > On Mon, Dec 28, 2015 at 9:13 AM, Binoy Dalal <binoydala...@gmail.com> > wrote: > > 1) When faceting use field of type string. That'll rid you of your > > tokenization problems. > > Alternatively do not use any tokenizers. > > Also turn doc values on for the field. It'll improve performance. > > 2) If however you do need to use a tokenized field for faceting, make > sure > > that they're pretty short in terms of number of tokens or else your app > > will die real soon. > > > > On Mon, 28 Dec 2015, 22:24 Kevin Lopez <kevin.lopez...@gmail.com> wrote: > > > >> I am not sure I am following correctly. The field I upload the document > to > >> would be "content" the analyzed field is "ColonCancerField". The > "content" > >> field contains the entire text of the document, in my case a pubmed > >> abstract. This is a tokenized field. I made this field untokenized and I > >> still received the same results [the results for not instead of not > >> necessarily (in my current example I have 2 docs with not and 1 doc with > >> not necessarily {not is of course in the document that contains not > >> necessarily})]: > >> > >> http://imgur.com/a/1bfXT > >> > >> I also tried this: > >> > >> http://localhost:8983/solr/Cytokine/select?&q=ColonCancerField > >> :"not+necessarily" > >> > >> I still receive the two documents, which is the same as doing > >> ColonCancerField:"not" > >> > >> Just to clarify the structure looks like this: *content (untokenized, > >> unanalyzed)* [copied to]==> *ColonCancerField *(tokenized, analyzed) > then I > >> browse the ColonCancerField and the facets state that there is 1 > document > >> for not necessarily, but when selecting it, solr returns 2 results. > >> > >> -Kevin > >> > >> On Mon, Dec 28, 2015 at 10:22 AM, Jamie Johnson <jej2...@gmail.com> > wrote: > >> > >> > Can you do the opposite? Index into an unanalyzed field and copy into > >> the > >> > analyzed? > >> > > >> > If I remember correctly facets are based off of indexed values so if > you > >> > tokenize the field then the facets will be as you are seeing now. > >> > On Dec 28, 2015 9:45 AM, "Kevin Lopez" <kevin.lopez...@gmail.com> > wrote: > >> > > >> > > *What I am trying to accomplish: * > >> > > Generate a facet based on the documents uploaded and a text file > >> > containing > >> > > terms from a domain/ontology such that a facet is shown if a term > is in > >> > the > >> > > text file and in a document (key phrase extraction). > >> > > > >> > > *The problem:* > >> > > When I select the facet for the term "*not necessarily*" (we see > there > >> > is a > >> > > space) and I get the results for the term "*not*". The field is > >> tokenized > >> > > and multivalued. This leads me to believe that I can not use a > >> tokenized > >> > > field as a facet field. I tried to copy the values of the field to a > >> text > >> > > field with a keywordtokenizer. I am told when checking the schema > >> > browser: > >> > > "Sorry, no Term Info available :(" This is after I delete the old > index > >> > and > >> > > upload the documents again. The facet is coming from a field that is > >> > > already copied from another field, so I cannot copy this field to a > >> text > >> > > field with a keywordtokenizer or strfield. What can I do to fix > this? > >> Is > >> > > there an alternate way to accomplish this? > >> > > > >> > > *Here is my configuration:* > >> > > > >> > > <copyField source="ColonCancerField" dest="cytokineField"/> > >> > > > >> > > <field name="cytokineField" indexed="true" stored="true" > >> > > multiValued="true" type="Cytokine_Pass"/> > >> > > <fieldType name="Cytokine_Pass" class="solr.TextField"> > >> > > <analyzer> > >> > > <tokenizer class="solr.KeywordTokenizerFactory" /> > >> > > </analyzer> > >> > > </fieldType> > >> > > > >> > > <field name="ColonCancerField" type="ColonCancer" indexed="true" > >> > > stored="true" multiValued="true" > >> > > termPositions="true" > >> > > termVectors="true" > >> > > termOffsets="true"/> > >> > > <fieldType name="ColonCancer" class="solr.TextField" > >> > > sortMissingLast="true" omitNorms="true"> > >> > > <analyzer> > >> > > <filter class="solr.ShingleFilterFactory" > >> > > minShingleSize="2" maxShingleSize="5" > >> > > outputUnigramsIfNoShingles="true" > >> > > /> > >> > > <tokenizer class="solr.WhitespaceTokenizerFactory"/> > >> > > <filter class="solr.LowerCaseFilterFactory"/> > >> > > <filter class="solr.SynonymFilterFactory" > >> > > synonyms="synonyms_ColonCancer.txt" ignoreCase="true" expand="true" > >> > > tokenizerFactory="solr.KeywordTokenizerFactory"/> > >> > > <filter class="solr.KeepWordFilterFactory" > >> > > words="prefLabels_ColonCancer.txt" ignoreCase="true"/> > >> > > </analyzer> > >> > > </fieldType> > >> > > <copyField source="content" dest="ColonCancerField"/> > >> > > > >> > > Regards, > >> > > > >> > > Kevin > >> > > > >> > > >> > > -- > > Regards, > > Binoy Dalal >
