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Lucene.Net fails to search keyword "The"

I am using Lucene.Net (Version Lucene 3.0.3). While searching for keyword "The",
it gives less than 5 results when there is plenty of records matching that keyword.
It works fine for all other keywords.
Does lucene have any problems with 'The' ??? :-)

As stated in the comments, your problem is the Analyzer you are using.
StandardAnalyzer does various things to do with grammar, but also removes a default set of english stop-words (a, and, or, then etc) "the" is one of these.
You can create it like
var a new StandardAnalyzer(version, new HashSet<string>());
Giving it an empty hashset says "there are no stop words"

Lucene doesn't search text having '_' [duplicate]

This question already has answers here:
Closed 11 years ago.
Possible Duplicate:
Lucene search and underscores
I am using Lucene full text search for searching in my application.
But for example, if I search for 'Turbo_Boost' it returns 0 results.
For other text it works fine.
Any Idea?

Assuming you are using the StandardTokenizer, it will split on the underscore character.
You can get around this by providing your own Tokenizer which will keep the underscore in the Token that's returned (either through a combination of Filter instances or TokenFilter instances).

A general rule of thumb with Lucene is to tokenize your search queries using the same Tokenizer/Analyzer you used to index the data.
see http://wiki.apache.org/lucene-java/LuceneFAQ#Why_is_it_important_to_use_the_same_analyzer_type_during_indexing_and_search.3F

I can only think of a few reasons why your query would fail:
First, and probably the least likely, considering other text searches fine, you didn't set the document's field to be analyzed. It won't be tokenized, so you can only search against the exact value of the whole field. Again, this one is probably not your issue.
The second (related to the third), and fairly likely, would depend on how you're executing the search. If you are not using the QueryParser (which analyzes your text the same way you index it if constructed properly) and instead say you are using a TermQuery like:
var tq = new TermQuery("Field", "Turbo_Boost");
That could cause your search to possibly fail. This has to do with the Analyzer you used to index the document splitting or changing the case of "Turbo_Boost" when it was indexed, causing the string comparison at search-time to f
The third, and even more likely, has to do with the Analyzer class you're using to index your items, versus the one you're using to search with. Using the same analyzer is important, because each analyzer uses a different Tokenizer that splits the text into searchable terms.
Let me give you some examples using your own Turbo_Boost query on how each analyzer will split the text into terms:
KeywordAnalyzer, WhitespaceAnalyzer -> Field:Turbo_Boost
SimpleAnalyzer, StopAnalyzer -> Field:turbo Field:boost
StandardAnalyzer -> Field:turbo Field:boost
You'll notice some of the Analyzers are splitting the term on the underscore character, while KeywordAnalyzer keeps it. It is extremely important that you use the same analyzer when you search, because you may not get the same results. It can also cause issues where sometimes the query will find results and other times it won't, all this depending on the query used.
As a side note, if you are using the StandardAnalyzer, it's also important that you pass it the same Version to the IndexWriter and QueryParser, because there are differences in how the parsing is done depending on which version of Lucene you expect it to emulate.
My guess your issue is one of those above reasons.

Howto perform a 'contains' search rather than 'starts with' using Lucene.Net

We use Lucene.NET to implement a full text search on a clients website. The search itself works already but we now want to implement a modification.
Currently all terms get appended a * which leads Lucene to perform what I would classify as a StartsWith search.
In the future we would like to have a search that performs something like a Contains rather than a StartsWith.
We use
Lucene.Net 2.9.2.2
StandardAnalyzer
default QueryParser
Samples:
(Title:Orch*) matches: Orchestra
but:
(Title:rch*) does not match: Orchestra
We want the first and the second one to both match Orchestra.
Basically I want the exact opposite of what was asked in this question, I'm not sure why for this person Lucene performed a Contains and rather than a StartsWith by default:
Why is this Lucene query a "contains" instead of a "startsWith"?
How can we make this happen?
I have the feeling it has something to do with the Analyzer but I'm not sure.

First off, I assume you're using StandardAnalyzer, or something similar. Your linked question fail to understand that you search for terms, and his case a* will match "Fleet Africa" because it's tokenized into "fleet" and "africa".
You need to call QueryParser.SetAllowLeadingWildcard(true) to be able to write queries like field:*value*. Are you actually changing the string that's passed to QueryParser?
You could parse the query as usual, and then implement a QueryVisitor that rewrites all TermQuery into WildcardQuery. That way you still support phrase searches.
I see no good things in rewriting queries into prefix- or wildcard-queries. There is very little shared between an orc, or a chest, and an Orchestra, but both words will match. Instead, hook up your customer with an analyzer that supports stemming, synonyms, and provide a spell correction feature to fix simple searching mistakes.

#Simon Svensson probably gave the better answer (i.e. you don't need this), but if you do, you should use a Shingle Filter.
Note that this will make your index massively larger, since instead of just storing "orchestra", you will store "orc", "rch", "che", "hes"... But just having a plain term query with leading wildcards will be massively slow. It will essentially have to look through every single term in your corpus.

How to cut specified words from string

There is a list of banned words ( or strings to be more general) and another list with let's say users mails. I would like to excise all banned words from all mails.
Trivial example:
foreach(string word in wordsList)
{
foreach(string mail in mailList)
{
mail.Replace(word,String.Empty);
}
}
How I can improve this algorithm?
Thanks for advices. I voted few answers up but I didn't mark any as answer since it was more like discussion than solution. Some people missed banned words with bad words. In my case I don't have to bother about recognize 'sh1t' or something like that.

Simple approaches to profanity filtering won't work - complex approaches don't work, for the most part, either.
What happens when you get a work like 'password' and you want to filter out 'ass'? What happens when some clever person writes 'a$$' instead - the intent is still clear, right?
See How do you implement a good profanity filter? for extensive discussion.

You could use RegEx to make things a little cleaner:
var bannedWords = #"\b(this|is|the|list|of|banned|words)\b";
foreach(mail in mailList)
var clean = Regex.Replace(mail, bannedWords, "", RegexOptions.IgnoreCase);
Even that, though, is far from perfect since people will always figure out a way around any type of filter.

You'll get best performance by drawing up a finite state machine (FSM) (or generate one) and then parsing your input 1 character at a time and walking through the states.
You can do this pretty easily with a function that takes your next input char and your current state and that returns the next state, you also create output as you walk through the mail message's characters. You draw the FSM on a paper.
Alternatively you could look into the Windows Workflow Foundation: State Machine Workflows.
In that way you only need to walk each message a single time.

Constructing a regular expression from the words (word1|word2|word3|...) and using this instead of the outer loop might be faster, since then, every e-mail only needs to be parsed once. In addition, using regular expressions would enable you to remove only "complete words" by using the word boundary markers (\b(word1|word2|word3|...)\b).
In general, I don't think you will find a solution which is orders of magnitude faster than your current one: You will have to loop through all mails and you will have to search for all the words, there's no easy way around that.

A general algorithm would be to:
Generate a list of tokens based on the input string (ie. by treating whitespace as token separators)
Compare each token against a list of banned words
Replace matched tokens
A regular expression is convenient for identifying tokens, and a HashSet would provide quick lookups for your list of banned words. There is an overloaded Replace method on the Regex class that takes a function, where you could control the replace behavior based on your lookup.
HashSet<string> BannedWords = new HashSet<string>(StringComparer.InvariantCultureIgnoreCase)
{
"bad",
};
string Input = "this is some bad text.";
string Output = Regex.Replace(Input, #"\b\w+\b", (Match m) => BannedWords.Contains(m.Value) ? new string('x', m.Value.Length) : m.Value);

Replacing it with * is annoying, but less annoying than something that removes the context of your intention by removing the word and leaving a malformed sentence. In discussing the Battle of Hastings, I'd be irritated if I saw William given the title "Grand ******* of Normandy", but at least I'd know I was playing in the small-kids playground, while his having the title of "Grand of Normandy" just looks like a mistake, or (worse) I might think that was actually his title.
Don't try replacing words with more innocuous words unless its funny. People get the joke on 4chan, but yahoo groups about history had confused people because the medireview and mediareview periods were being discussed when eval (not profanity, but is used in some XSS attacks that yahoo had been hit by) was replaced with review in medieval and mediaeval (apparantly, medireview is the American spelling of mediareview!).

In some circumstance is possible to improve it:
Just for fun:
u can use SortedList, if ur mailing list is mailing list (because u have a delimiter like ";") u can do as bellow:
first calculate ur running time algorithm:
Words: n item. (each item has an O(1) length).
mailing list: K item.
each item in mailing list average length of Z.
each sub item in mailing list item average length of Y so the average number of subitems in mailing list items is m = Z/Y.
ur algorithm takes O(n*K*Z). // the best way with knut algorithm
1.now if u sort the words list in O(n log n).
2.1- use mailingListItem.Split(";".ToCharArray()) for each mailing list item: O(Z).
2.2- sort the items in mailing list: O(m * log m)
total sorting takes O(K * Z) in worth case with respect to (m logm << Z).
3- use merge algorithm to merge items of bad word and specific mailing list: O((m + n) * k)
total time is O((m+n)*K + m*Z + n^2) with respect to m << n, total algorithm running time is O(n^2 + Z*K) in worth case, which is smaller than O(n*K*Z) if n < K * Z ( i think so).
So if performance is very very very important, u can do this.

You might consider using Regex instead of simple string matches, to avoid replacing partial content within words. A Regex would allow you to assure you are only getting full words that match. You could use a pattern like this:
"\bBADWORD\b"
Also, you may want to iterate over the mailList on the outside, and the word list on the inner loop.

Wouldn't it be easier (and more efficient) to simply redact them by changing all their characters to * or something? That way no large string needs to be resized or moved around, and the recipents are made more aware what happened, rather than getting nonsensical sentences with missing words.

Well, you certainly don' want to make the clbuttic mistake of naive string.Replace() to do it. The regex solution could work, although you'd either be iterating or using the pipe alternator (and I don't know if/how much that would slow your operation down, particularly for a large list of banned words). You could always just...not do it, since it's entirely futile no matter what--there are ways to make your intended words quite clear even without using the exact letters.
That, and it's ridiculous to have a list of words that "people find offensive" in the first place. There's someone who will be offended by pretty much any word
/censorship is bullshit rant

I assume that you want to detect only complete words (separated by non-letter characters) and ignore words with a filter-word substring (like a p[ass]word example). In that case you should build yourself a HashSet of filter-words, scan the text for words, and for each word check its existence in HashSet. If it's a filter word then build resulting StringBuilder object without it (or with an equal number of asterisks).

I had great results using this algorithm on codeproject.com better than brute force text replacments.

C# Code/Algorithm to Search Text for Terms

We have 5mb of typical text (just plain words). We have 1000 words/phrases to use as terms to search for in this text.
What's the most efficient way to do this in .NET (ideally C#)?
Our ideas include regex's (a single one, lots of them) plus even the String.Contains stuff.
The input is a 2mb to 5mb text string - all text. Multiple hits are good, as in each term (of the 1000) that matches then we do want to know about it. Performance in terms of entire time to execute, don't care about footprint. Current algorithm gives about 60 seconds+ using naive string.contains. We don't want 'cat' to provide a match with 'category' or even 'cats' (i.e. entire term word must hit, no stemming).
We expect a <5% hit ratio in the text. The results would ideally just be the terms that matched (dont need position or frequency just yet). We get a new 2-5mb string every 10 seconds, so can't assume we can index the input. The 1000 terms are dynamic, although have a change rate of about 1 change an hour.

A naive string.Contains with 762 words (the final page) of War and Peace (3.13MB) runs in about 10s for me. Switching to 1000 GUIDs runs in about 5.5 secs.
Regex.IsMatch found the 762 words (much of which were probably in earlier pages as well) in about .5 seconds, and ruled out the GUIDs in 2.5 seconds.
I'd suggest your problem lies elsewhere...Or you just need some decent hardware.

Why reinvent the wheel? Why not just leverage something like Lucene.NET?

have you considered the following:
do you care about substring? lets say I am looking for the word "cat", nothing more or nothing less. now consider the Knuth-Morris-Pratt algorithm, or string.contains for "concatinate". both of these will return true (or an index). is this ok?
Also you will have to look into the idea of the stemmed or "Finite" state of the word. lets look for "diary" again, the test sentance is "there are many kinds of diaries". well to you and me we have the word "diaries" does this count? if so we will need to preprocess the sentance converting the words to a finite state (diaries -> diary) the sentance will become "there are many kind of diary". now we can say that Diary is in the sentance (please look at the porter Stemmer Algroithm)
Also when it comes to processing text (aka Natrual Langauge Processing) you can remove some words as noise, take for example "a, have, you, I, me, some, to" <- these could be considered as useless words, and can then be removed before any processing takes place? for example
"I have written some C# today", if i have 10,000 key works to look for I would have to scan the entire sentance 10,000 x the number of words in the sentance. removing noise before hand will shorting the processing time
"written C# today" <- removed noise, now there are lots less to look throught.
A great article on NLP can be found here. Sentance comparing
HTH
Bones

A modified Suffix tree would be very fast, though it would take up a lot of memory and I don't know how fast it would be to build it. After that however every search would take O(1).

Here's another idea: Make a class something like this:
class Word
{
string Word;
List<int> Positions;
}
For every unique word in your text you create an instance of this class. Positions array will store positions (counted in words, not characters) from the start of the text where this word was found.
Then make another two lists which will serve as indexes. One will store all these classes sorted by their texts, the other - by their positions in the text. In essence, the text index would probably be a SortedDictionary, while the position index would be a simple List<Word>.
Then to search for a phrase, you split that phrase into words. Look up the first word in the Dictionary (that's O(log(n))). From there you know what are the possible words that follow it in the text (you have them from the Positions array). Look at those words (use the position index to find them in O(1)) and go on, until you've found one or more full matches.

Are you trying to achieve a list of matched words or are you trying to highlight them in the text getting the start and length of the match position? If all you're trying to do is find out if the words exist, then you could use subset theory to fairly efficiently perform this.
However, I expect you're trying to each match's start position in the text... in which case this approach wouldn't work.
The most efficient approach I can think is to dynamically build a match pattern using a list and then use regex. It's far easier to maintain a list of 1000 items than it is to maintain a regex pattern based on those same 1000 items.
It is my understanding that Regex uses the same KMP algorithm suggested to efficiently process large amounts of data - so unless you really need to dig through and understand the minutiae of how it works (which might be beneficial for personal growth), then perhaps regex would be ok.
There's quite an interesting paper on search algorithms for many patterns in large files here: http://webglimpse.net/pubs/TR94-17.pdf

Is this a bottleneck? How long does it take? 5 MiB isn't actually a lot of data to search in. Regular expressions might do just fine, especially if you encode all the search strings into one pattern using alternations. This basically amortizes the overall cost of the search to O(n + m) where n is the length of your text and m is the length of all patterns, combined. Notice that this is a very good performance.
An alternative that's well suited for many patterns is the Wu Manber algorithm. I've already posted a very simplistic C++ implementation of the algorithm.

Ok, current rework shows this as fastest (psuedocode):
foreach (var term in allTerms)
{
string pattern = term.ToWord(); // Use /b word boundary regex
Regex regex = new Regex(pattern, RegexOptions.IgnoreCase);
if (regex.IsMatch(bigTextToSearchForTerms))
{
result.Add(term);
}
}
What was surprising (to me at least!) is that running the regex 1000 times was faster that a single regex with 1000 alternatives, i.e. "/b term1 /b | /b term2 /b | /b termN /b" and then trying to use regex.Matches.Count

How does this perform in comparison? It uses LINQ, so it may be a little slower, not sure...
List<String> allTerms = new List<String>(new String(){"string1", "string2", "string3", "string4"});
List<String> matches = allTerms.Where(item => Regex.IsMatch(bigTextToSearchForTerms, item, RegexOptions.IgnoreCase));
This uses classic predicates to implement the FIND method, so it should be quicker than LINQ:
static bool Match(string checkItem)
{
return Regex.IsMatch(bigTextToSearchForTerms, checkItem, RegexOptions.IgnoreCase);
}
static void Main(string[] args)
{
List<String> allTerms = new List<String>(new String(){"string1", "string2", "string3", "string4"});
List<String> matches = allTerms.Find(Match);
}
Or this uses the lambda syntax to implement the classic predicate, which again should be faster than the LINQ, but is more readable than the previous syntax:
List<String> allTerms = new List<String>(new String(){"string1", "string2", "string3", "string4"});
List<String> matches = allTerms.Find(checkItem => Regex.IsMatch(bigTextToSearchForTerms, checkItem, RegexOptions.IgnoreCase));
I haven't tested any of them for performance, but they all implement your idea of iteration through the search list using the regex. It's just different methods of implementing it.