analyzer

问题 I have a Java project that I've been working on for a while. The design started out pretty good but slowly degraded as changes were made. I'm looking for some sort of tool that will analyze the project. It'd be really nice to have a map of the dependencies of different classes/methods. I feel like certain methods are only in there to fulfill a very specific goal. I'd like to eliminate unnecessary code and make my design better. Any suggestions would be great! Thanks! 回答1: You may want to

问题 I'd like to specify an analyzer, name it, and use that name in a mapping while creating an index. I'm lost, my ES instance always returns me an error message. This is, roughly, what I'd like to do: "settings": { "mappings": { "alfedoc": { "properties": { "id": { "type": "string" }, "alfefield": { "type": "string", "analyzer": "alfeanalyzer" } } } }, "analysis": { "analyzer": { "alfeanalyzer": { "type": "pattern", "pattern":"\\s+" } } } } But this does not seem to work; the ES instance always

问题 I want to build my own analyzer that uses both filters/tokenizers. I mean, the same field is Keyword (entire stream as a single token) and lowercase If KeywordAnalyzer use only, the value of field keeps the case-insensitive. If I use LowerCaseTokenizer or LowerCaseFilter I have to combine them with other analyzers that do the same thing KeywordAnalyzer (separated by no letter, by spaces, remove stop-words, etc.) The question is : Is there any way to make that field as Keyword (entire stream

问题 How do I use sklearn CountVectorizer with both 'word' and 'char' analyzer? http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html I could extract the text features by word or char separately but how do i create a charword_vectorizer ? Is there a way to combine the vectorizers? or use more than one analyzer? >>> from sklearn.feature_extraction.text import CountVectorizer >>> word_vectorizer = CountVectorizer(analyzer='word', ngram_range=(1, 2),

问题 I'm having trouble understanding the concept of analyzers in elasticsearch with tire gem. I'm actually a newbie to these search concepts. Can someone here help me with some reference article or explain what actually the analyzers do and why they are used? I see different analyzers being mentioned at elasticsearch like keyword, standard, simple, snowball. Without the knowledge of analyzers I couldn't make out what actually fits my need. 回答1: Let me give you a short answer. An analyzer is used

问题 We have an OData-compliant API that delegates some of its full text search needs to an Elasticsearch cluster. Since OData expressions can get quite complex, we decided to simply translate them into their equivalent Lucene query syntax and feed it into a query_string query. We do support some text-related OData filter expressions, such as: startswith(field,'bla') endswith(field,'bla') substringof('bla',field) name eq 'bla' The fields we're matching against can be analyzed , not_analyzed or

问题 We are building a Solr plug-in to link our proprietary engine. The intended use is replacing the standard tokenizer altogether. (This is the background: Hybrid search and indexing: words and token metadata in Solr) When trying to index a test document in the Solr Admin: id,title 12345,A test title I am getting an exception where, I suppose, my tokenizer is kicking in. The configuration changes (schema.xml) are: <fieldType name="text_general" class="solr.TextField" positionIncrementGap="100">

问题 I want to write a custom code analyzer in Visual Studio 2015 for a C# ConsoleApplication. For this reason I don't want to create a seperate "Analyzer with Code Fix" project from template, because this requires to add this analyzer in my projects as nuget package. Is it possible, to add a analyzer reference manually? I would like to reference the analyzer without nuget. 回答1: If you add an analyzer as Nuget and check the content of your project, you'll see that only an <Analyzer Include="..." /

问题 Just double-checking on this: I assume this is not possible and that if you want to keep such info somehow bundled up with the index files in your index directory you have to work out a way to do it yourself. Obviously you might be using different Analyzers for different directories, and 99% of the time it is pretty important to use the right one when constructing a QueryParser: if your QP has a different one all sorts of inaccuracies might crop up in the results. Equally, getting the wrong

问题 I am using Elasticsearch in-built Simple analyzer https://www.elastic.co/guide/en/elasticsearch/reference/1.7/analysis-simple-analyzer.html, which uses Lower Case Tokenizer. and text apple 8 IS Awesome is tokenized in below format. "apple", "is", "awesome" You can clearly see, that it misses to tokenize the number 8 , hence now if I just search with 8 , my message will not appear in search. I went through all the available analyzer available with ES but couldn't find any suitable analyzer