I'm processing some Indonesian texts in a Java application, and I need to stem them.
Currently I am using lucene indonesian stemmer.
org.apache.lucene.analysis.id.IndonesianAnalyzer;
but results are not satisfactory.
Could anyone suggest me different stemmer?
"enang" is a stem. Stems need not be actual words. For instance, in English, "argue" "argues" and "arguing" reduce to the stem "argu". "argu" isn't an english word, but it is a meaningful stem. This is how stemmers work. As long as you apply the stemmer the same way to the indexed data and the query, it should work well.
If you don't want behavior like that, it doesn't make any sense to use a stemmer at all.
Aside from the stemmer, IndonesianAnalyzer is fairly easily replicated. It's other components just involve a StandardTokenizer, StandardFilter, LowercaseAnalyzer, and a StopFilter. That's just a StandardAnalyzer with an Indonesian stopword set, when you get right down to it, so you can create an Indonesiananalyzer without the stemmer as simply as:
//If you are using the default stopword location defined in the IndonesianAnalyzer you could load them like this.
CharArraySet defaultStopSet = StopwordAnalyzerBaseloadStopwordSet(false, IndonesianAnalyzer.class, IndonesianAnalyzer.DEFAULT_STOPWORD_FILE, "#");
Analyzer analyzer = new StandardAnalyzer(Version.LUCENE_43, defaultStopSet);
I'm not sure whether you would run into problems just passing a reader on the default stop word file into the StandardAnalyzer constructor.
Related
While indexing my document using lucene Standard Analyzer I got a plroblem.
For example:
my document had a word "plag-iarism" ... here this analyzer indexed it as "plag" and "iarism". But I want like "plagiarism". What I have to do to get a whole word?
StandardAnalyzer delegates tokanization to StandardTokenizer.
You create your own tokanizer to match your exact needs (you could base it on StandardTokenizer).
Alternatively, if you prefer, you could do a dirty hack of a String.replace(), with the relevant regular expression, just the analyzer runs. Yeah. Ugly.
I have a question about searching process in lucene/.
I use this code for search
Directory directory = FSDirectory.GetDirectory(#"c:\index");
Analyzer analyzer = new StandardAnalyzer();
QueryParser qp = new QueryParser("content", analyzer);
qp.SetDefaultOperator(QueryParser.Operator.AND);
Query query = qp.Parse(search string);
In one document I've set "I want to go shopping" for a field and in other document I've set "I wanna go shopping".
the meaning of both sentences is same!
is there any good solution for lucene to understand meaning of sentences or kind of normalize the scentences ? for example save the fields like "I wanna /want to/ go shopping" and remove the comment with regexp in result.
Lucene provides filter to normalize words and even map similar words.
PorterStemFilter -
Stemming allows words to be reduced to their roots.
e.g. wanted, wants would be reduced to root want and search for any of those words would match the document.
However, wanna does not reduce to root want. So it may not work in this case.
SynonymFilter -
would help you to map words similar in a configuration file.
so wanna can be mapped to want and if you search for either of those, the document must match.
you would need to add the filters in your analysis chain.
Can someone please explain the difference between the different analyzers within Lucene? I am getting a maxClauseCount exception and I understand that I can avoid this by using a KeywordAnalyzer but I don't want to change from the StandardAnalyzer without understanding the issues surrounding analyzers. Thanks very much.
In general, any analyzer in Lucene is tokenizer + stemmer + stop-words filter.
Tokenizer splits your text into chunks, and since different analyzers may use different tokenizers, you can get different output token streams, i.e. sequences of chunks of text. For example, KeywordAnalyzer you mentioned doesn't split the text at all and takes all the field as a single token. At the same time, StandardAnalyzer (and most other analyzers) use spaces and punctuation as a split points. For example, for phrase "I am very happy" it will produce list ["i", "am", "very", "happy"] (or something like that). For more information on specific analyzers/tokenizers see its Java Docs.
Stemmers are used to get the base of a word in question. It heavily depends on the language used. For example, for previous phrase in English there will be something like ["i", "be", "veri", "happi"] produced, and for French "Je suis très heureux" some kind of French analyzer (like SnowballAnalyzer, initialized with "French") will produce ["je", "être", "tre", "heur"]. Of course, if you will use analyzer of one language to stem text in another, rules from the other language will be used and stemmer may produce incorrect results. It isn't fail of all the system, but search results then may be less accurate.
KeywordAnalyzer doesn't use any stemmers, it passes all the field unmodified. So, if you are going to search some words in English text, it isn't a good idea to use this analyzer.
Stop words are the most frequent and almost useless words. Again, it heavily depends on language. For English these words are "a", "the", "I", "be", "have", etc. Stop-words filters remove them from the token stream to lower noise in search results, so finally our phrase "I'm very happy" with StandardAnalyzer will be transformed to list ["veri", "happi"].
And KeywordAnalyzer again does nothing. So, KeywordAnalyzer is used for things like ID or phone numbers, but not for usual text.
And as for your maxClauseCount exception, I believe you get it on searching. In this case most probably it is because of too complex search query. Try to split it to several queries or use more low level functions.
In my perspective, I have used StandAnalyzer and SmartCNAnalyzer. As I have to search text in Chinese. Obviously, SmartCnAnalyzer is better at handling Chinese. For diiferent purposes, you have to choose properest analyzer.
I am using the Lucene standard analyzer to parse text. however, it is returning prepositions as well as words like "i", "the", "and" etc...
Is there an Analyzer I can use that will not return these words?
Thanks
StandardAnalyzer uses StopFilter.
By default the words in the STOP_WORDS_SET are excluded. If this is not sufficient, there are constructors which allow you to pass in a list of stop words which should be removed from the token stream. You can provide the list using a File, a Set, or a Reader.
Am using MultiFieldQueryParser for parsing strings like a.a., b.b., etc
But after parsing, its removing the dots in the string.
What am i missing here?
Thanks.
I'm not sure the MultiFieldQueryParser does what you think it does. Also...I'm not sure I know what you're trying to do.
I do know that with any query parser, strings like 'a.a.' and 'b.b.' will have the periods stripped out because, at least with the default Analyzer, all punctuation is treated as white space.
As far as the MultiFieldQueryParser goes, that's just a QueryParser that allows you to specify multiple default fields to search. So with the query
title:"Of Mice and Men" "John Steinbeck"
The string "John Steinbeck" will be looked for in all of your default fields whereas "Of Mice and Men" will only be searched for in the title field.
What analyzer is your parser using? If it's StopAnalyzer then the dot could be a stop word and is thus ignored. Same thing if it's StandardAnalyzer which cleans up input (includes removing dots).
(Repeating my answer from the dupe. One of these should be deleted).
The StandardAnalyzer specifically handles acronyms, and converts C.F.A. (for example) to cfa. This means you should be able to do the search, as long as you make sure you use the same analyzer for the indexing and for the query parsing.
I would suggest you run some more basic test cases to eliminate other factors. Try to user an ordinary QueryParser instead of a multi-field one.
Here's some code I wrote to play with the StandardAnalyzer:
StringReader testReader = new StringReader("C.F.A. C.F.A word");
StandardAnalyzer analyzer = new StandardAnalyzer();
TokenStream tokenStream = analyzer.tokenStream("title", testReader);
System.out.println(tokenStream.next());
System.out.println(tokenStream.next());
System.out.println(tokenStream.next());
The output for this, by the way was:
(cfa,0,6,type=<ACRONYM>)
(c.f.a,7,12,type=<HOST>)
(word,13,17,type=<ALPHANUM>)
Note, for example, that if the acronym doesn't end with a dot then the analyzer assumes it's an internet host name, so searching for "C.F.A" will not match "C.F.A." in the text.