I would like to implement full-text-search in my off-line (android) application to search the user generated list of notes.
I would like it to behave just like Google (since most people are already used to querying to Google)
My initial requirements are:
Fast: like Google or as fast as possible, having 100000 documents with 200 hundred words each.
Searching for two words should only return documents that contain both words (not just one word) (unless the OR operator is used)
Case insensitive (aka: normalization): If I have the word 'Hello' and I search for 'hello' it should match.
Diacritical mark insensitive: If I have the word 'así' a search for 'asi' should match. In Spanish, many people, incorrectly, either do not put diacritical marks or fail in correctly putting them.
Stop word elimination: To not have a huge index meaningless words like 'and', 'the' or 'for' should not be indexed at all.
Dictionary substitution (aka: stem words): Similar words should be indexed as one. For example, instances of 'hungrily' and 'hungry' should be replaced with 'hunger'.
Phrase search: If I have the text 'Hello world!' a search of '"world hello"' should not match it but a search of '"hello world"' should match.
Search all fields (in multifield documents) if no field specified (not just a default field)
Auto-completion in search results while typing to give popular searches. (just like Google Suggest)
How may I configure a full-text-search engine to behave as much as possible as Google?
(I am mostly interested in Open Source, Java and in particular Lucene)
I think Lucene can address your requirements. You should also consider using Solr, which has similar functionality and is much easier to set up.
I will discuss each requirement separately, using Lucene. I believe Solr has similar mechanisms.
Fast: like Google or as fast as possible, having 100000 documents with 200 hundred words each.
This is a reasonable index size both for Lucene and Solr, enabling retrieval at several tens of milliseconds per query.
Searching for two words should only return documents that contain both words (not just one word) (unless the OR operator is used)
You can do that using a BooleanQuery with MUST as default in Lucene.
The next four requirements can be handled by customizing a Lucene Analyzer:
Case insensitive (aka: normalization): If I have the word 'Hello' and I search for 'hello' it should match.
A LowerCaseFilter can be used for this.
Diacritical mark insensitive: If I have the word 'así' a search for 'asi' should match. In Spanish, many people, incorrectly, either do not put diacritical marks or fail in correctly putting them.
This requires Unicode normalization followed by diacritic removal. You can build a custom Analyzer for this.
Stop word elimination: To not have a huge index meaningless words like 'and', 'the' or 'for' should not be indexed at all.
A StopFilter removes stop words in Lucene.
Dictionary substitution (aka: stem words): Similar words should be indexed as one. For example, instances of 'hungrily' and 'hungry' should be replaced with 'hunger'.
Lucene has many Snowball Stemmers. One of them may be appropriate.
Phrase search: If I have the text 'Hello world!' a search of '"world hello"' should not match it but a search of '"hello world"' should match.
This is covered by the Lucene PhraseQuery specialized query.
As you can see, Lucene covers all of the required functionality. To get a more general picture, I suggest the book Lucene in Action, The Apache Lucene Wiki or The Lucid Imagination Site.
A lot of these behaviors are default for Lucene. The first (including all terms) is not, but you can force this behavior by setting the default operator:
MultiFieldQueryParser parser = new MultiFieldQueryParser(fields, new StandardAnalyzer());
parser.setDefaultOperator(QueryParser.AND_OPERATOR);
I know that items 2, 4, and 6 are possible, and IIRC, they happen by default. I'm not sure about items 3 and 5, but Lucene offers a ton of customization options, so I'd suggest implementing a proof-of-concept with your data to see if it meets these requirements as well.
Buy a Google Search Appliance. Or, as the comments say, use Lucene like you already mentioned.
HyperSQL is a pure-java SQL implementation that can be ran quite easily, as can SQLite. You could use their full-text capabilities and querying to re-create the wheel, but as the other commenters have pointed out an existing implementation is probably best.
Unless you buy a search engine, you have Lucene, Nutch, Apache Solr and few others.
Related
Here is the Scenario of Lucene Searching,
In Lucene document contains two fields having values "Red" and "Red Feather".
I want to search Red Feathar.
It is returning "Red" as first result. But I want "Red Feather" as first result.
I tried using fuzzy query term matches. but its makes my application slow.
So is there any another way to search Phonetic misspelled word in Lucene?
Thanks,
There are Phonetic matching methods in Lucene, but I think a FuzzyQuery is the right choice for this case. There are a few ways to enhance it's performance:
Are you using Lucene 4.0 or later? FuzzyQuery performance got a huge boost in 4.0.
Set a prefixLength. This forces the first x characters to directly match, which narrows searching for matching terms significantly, and takes better advantage of lucene's indexing methods, leading to significantly improved performance.
If you are using 3.6 or earlier (and can't switch to a more recent version), make sure you set minimumSimilarity
If you are using 4.0 or later, you could try setting maxEdits to 1
I'm creating a mini search engine in Java which basically grabs all of the RSS feeds that a user specifies and then allows him or her to choose a single word to search for. Since the RSS feed documents are fairly limited in number, I'm thinking about processing the documents first before the user enters his or her search term. I want to process them by creating hashmaps linking certain keywords to a collection of records which contain the articles themselves and the number of times the word appears in the article. But, how would I determine the keywords? How can I tell which words are meaningless and which aren't?
The concept of "what words should I ignore?" is generally named stopwords. The best search engines do not use stopwords. If I am a fan of the band "The The", I would be bummed if your search engine couldn't find them. Also, searching for exact phrases can be screwed up by a naive stopwords implementation.
By the way, the hashmap you're talking about is called an inverted index. I recommend reading this (free, online) book to get an introduction to how search engines are built: http://nlp.stanford.edu/IR-book/information-retrieval-book.html
In Solr, I believe these are called 'stopwords'.
I believe they just use a text file to define all the words that they will not search on.
A small extract re. stopwords from NLTK from Ch. 2:
There is also a corpus of stopwords, that is, high-frequency words
like the, to and also that we sometimes want to filter out of a
document before further processing. Stopwords usually have little
lexical content, and their presence in a text fails to distinguish it
from other texts.
>>> from nltk.corpus import stopwords
>>> stopwords.words('english')
['a', "a's", 'able', 'about', 'above', 'according', 'accordingly', 'across',
'actually', 'after', 'afterwards', 'again', 'against', "ain't", 'all', 'allow',
'allows', 'almost', 'alone', 'along', 'already', 'also', 'although', 'always', ...]
Stopwords are one thing you should use. Lots of stopword lists are available on the web.
However I'm writing an answer because the previous ones didn't mention TF-IDF which is a metric for how important a word is in the context of your corpus of documents.
A word is more likely to be a keyword foe a document if it appears a lot in it (term frequency) and doesn't appear frequently in other documents (inverse document frequency). This way words like a, the, where, are naturally ignored, because they appear in every document.
P.S. On a related topic, you'll probably be interested in other lists, i.e. swearwords :)
P.P.S. Hashmaps are a good thing, but you should also check suffix trees for your task.
I have a large set of documents stored inside a Lucene index and I am using a customAnalyzer which basically does tokenization and stemming for the documents content.
Now, if I search inside the documents for the word "love", I get results where love is being used either as a noun or a verb, while I want only those documents which use love only as a verb.
How can such s feature be implemented where I could also mention the part-of-speech of the word along with the word so that the results have only love used as a verb and not as a noun?
I can think of a way to initially part-of-speech tag each word of the document and store it by appending the POS with the word with a '_' or something and then to search accordingly, but wanted to know if there is a smarter way to do this in Lucene.
I can think of following approaches.
Approach 1
Just like you mentioned: Recognize and append the part-of-speech tag to the actual term while indexing. Do the same while querying.
I would like to discuss the cons associated.
Cons:
1) Future requirements might demand you to get results irrespective of part-of-speech. The Index that contains modified terms won't work.
2) You might want to execute a BooleanQuery like "term: noun or adjective". You've to write the query expander yourself.
Approach 2
Try using Payloads feature of Lucene.
Here is a brief tutorial on Lucene Payloads.
Steps to address your use-case.
1) Store the part-of-speech tag in the form of a Payload.
2) Have custom Similarity classes for each part-of-speech tag.
3) Based on the query, assign the corresponding CustomSimilarity to the IndexSearcher. For example, assign NounBoostingSimilarity for a noun query.
4) Boost or "Reduce" the score of a document based on Payload. Example given in the above tutorial.
5) Write a custom collector to filter out the documents with scores not conforming to above score-boosting logic.
Pros of this approach is that the Index remains compatible for any other normal search.
Cons:
1) Maintenance overhead : have to maintain multiple IndexSearchers for each similarity.
2) Somewhat complicated-to-code solution.
To be frank, I'm not satisfied with my own solution, but just wanted to let you know that there exists another way. It all depends on your scenario, whether the project is an academic one-time project or a commercial one, etc.
I am new to Lucene and my project is to provide specialized search for a set
of booklets. I am using Lucene Java 3.1.
The basic idea is to help people know where to look for information in the (rather
large and dry) booklets by consulting the index to find out what booklet and page numbers match their query. Each Document in my index represents a particular page in one of the booklets.
So far I have been able to successfully scrape the raw text from the booklets,
insert it into an index, and query it just fine using StandardAnalyzer on both
ends.
So here's my general question:
Many queries on the index will involve searching for place names mentioned in the
booklets. Some place names use notational variants. For instance, in the body text
it will be called "Ship Creek" on one page, but in a map diagram elsewhere it might be listed as "Ship Cr." or even "Ship Ck.". What I need to know is how to approach treating the two consecutive words as a single term and add the notational variants as synonyms.
My goal is of course to search with any of the variants and catch all occurrences. If I search for (Ship AND (Cr Ck Creek)) this does not give me what I want because other words may appear between [ship] and [cr]/[ck]/[creek] leading to false positives.
So, in a nutshell I probably still need the basic stuff provided by StandardAnalyzer, but with specific term grouping to emit place names as complete terms and possibly insert synonyms to cover the variants.
For instance, the text "...allowed from the mouth of Ship Creek upstream to ..." would
result in tokens [allowed],[mouth],[ship creek],[upstream]. Perhaps via a TokenFilter along
the way, the [ship creek] term would expand into [ship creek][ship ck][ship cr].
As a bonus it would be nice to treat the trickier text "..except in Ship, Bird, and
Campbell creeks where the limit is..." as [except],[ship creek],[bird creek],
[campbell creek],[where],[limit].
This seems like a pretty basic use case, but it's not clear to me how I might be able to use existing components from Lucene contrib or SOLR to accomplish this. Should the detection and merging be done in some kind of TokenFilter? Do I need a custom Analyzer implementation?
Some of the term grouping can probably be done heuristically [],[creek] is [ creek]
but I also have an exhaustive list of places mentioned in the text if that helps.
Thanks for any help you can provide.
You can use Solr's Synonym Filter. Just set up "creek" to have synonyms "ck", "cr" etc.
I'm not aware of any existing functionality to solve your "bonus" problem.
I've been using the (Java) Highlighter for Lucene (in the Sandbox package) for some time. However, this isn't really very accurate when it comes to matching the correct terms in search results - it works well for simple queries, for example searching for two separate words will highlight both code fragments in the results.
However, it doesn't act well with more complicated queries. In the simplest case, phrase queries such as "Stack Overflow" will match all occurrences of Stack or Overflow in the highlighting, which gives the impression to the user that it isn't working very well.
I tried applying the fix here but that came with a lot of performance caveats, and at the end of the day was just plain unusable. The performance is especially an issue on wildcard queries. This is due to the way that the highlighting works; instead of just working on the querystring and the text it parses it as Lucene would and then looks for all the matches that Lucene has made; unfortunately this means that for certain wildcard queries it can be looking for matches to 2000+ clauses on large documents, and it's simply not fast enough.
Is there any faster implementation of an accurate highlighter?
There is a new faster highlighter (needs to be patched in but will be part of release 2.9)
https://issues.apache.org/jira/browse/LUCENE-1522
and a back-reference to this question
You could look into using Solr. http://lucene.apache.org/solr
Solr is a sort of generic search application that uses Lucene and supports highlighting. It's possible that the highlighting in Solr is usable as an API outside of Solr. You could also look at how Solr does it for inspiration.
I've been reading on the subject and came across spanQuery which would return to you the span of the matched term or terms in the field that matched.