I'm using Lucene 7.x and ItalianStemmer. I have seen the code of ItalianStemmer class and it seems to take long to be understood. So, I'm looking for a quick (possibly standard) way to customize italian stemmer, without extending ItalianStemmer or SnowballProgram, because I have few days.
The point is that I don't understand why the name "saluto" (greeting) is stemmed to "sal". It should be stemmed to "salut", since the verb "salutare" (greet) is stemmed to "salut". Moreover, "sala" (room) and "sale" (rooms) are also stemmed to "sal", which is confusing, because they have a different meaning.
The standard way would be to copy the source, and create your own.
Stemming is a heuristic process, based on rules. It is designed to generate stems that, while imperfect, are usually good enough to facilitate search. It doesn't have a dictionary of conjugated words and their stems for you to modify. -uto is one of the verb suffixes removed from words by the Italian snowball stemmer, as described here. You could create your own version removing that suffix from the list, but you are probably going to create more problems than you solve, all told.
A tool that returns the correct root word would generally be called a lemmatizer, and I don't believe any come with Lucene, out of the box. The morphological analysis tends to be slower and more complex. If it's important to your use case, you might want to look up an Italian lemmatizer, and work it into a custom filter, or preprocess your text before passing it off the to the analyzer.
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I was looking for lightweight library that'd allow me to feed it a bunch of words, and then ask it whether a given word would have any close matches.z
I'm not particularly concerned with the underlying algorithm (I reckon a simple hamming distance algorithm would probably suffice, were I to undertake the task myself).
I'm just in the development of a small language and I found it nifty to make suggestions to the user when an "Undefined class" error is detected (lots of times it's just a misspelled word). I don't want to lose much time on the issue though.
Thanks
Levenshtein distance is a common way of handling it. Just add all the words to a list and then brute-force iterate over it and return the smallest distance. Here's one library with a Levenschtein function: http://commons.apache.org/lang/api-2.4/org/apache/commons/lang/StringUtils.html
If you have a large number of words and you want it to run fast, then you'd have to use ngrams. Spilt each word into bigrams and then add (bigram, word) to a map. Use the map to look up the bigrams in the target word, and then iterate through the candidates. That's probably more work than you want to do, though.
not necessarily a library but i think this article may be really helpful. it mostly describes the general workings of how a spelling corrector works in python, but also has a link for a java implementation which you may use if that is what you are looking for specifically (note that I haven't specifically used the java one before)
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'm implementing a new machine learning algorithm in Java that extracts a prototype datastructure from a set of structured datasets (tree-structure). As im developing a generic library for that purpose, i kept my design independent from concrete data-representations like XML.
My problem now is that I need a way to define a data model, which is basically a ruleset describing valid trees, against which a set of trees is being matched. I thought of using BNF or a similar dialect.
Basically I need a way to iterate through the space of all valid TreeNodes defined by the ModelTree (Like a search through the search space for algorithms like A*) so that i can compare my set of concrete trees with the model. I know that I'll have to deal with infinite spaces there but first things first.
I know, it's rather tricky (and my sentences are pretty bumpy) but I would appreciate any clues.
Thanks in advance,
Stefan
I believe that you are talking about a Regular Tree Grammar. This Wikipedia page is an entry point for the topic, and the book that it links to might be helpful.
I am looking for a Java library to do some initial spell checking / data normalization on user generated text content, imagine the interests entered in a Facebook profile.
This text will be tokenized at some point (before or after spell correction, whatever works better) and some of it used as keys to search for (exact match). It would be nice to cut down misspellings and the like to produce more matches. It would be even better if the correction would perform well on tokens longer than just one word, e.g. "trinking coffee" would become "drinking coffee" and not "thinking coffee".
I found the following Java libraries for doing spelling correction:
JAZZY does not seem to be under active development. Also, the dictionary-distance based approach seems inadequate because of the use of non-standard language in social network profiles and multi-word tokens.
APACHE LUCENE seems to have a statistical spell checker that should be much more suited. Question here would how to create a good dictionary? (We are not using Lucene otherwise, so there is no existing index.)
Any suggestions are welcome!
What you want to implement is not spelling corrector but a fuzzy search. Peter Norvig's essay is a good starting point to build a fuzzy search from candidates checked against a dictionary.
Alternatively have a look at BK-Trees.
An n-gram index (used by Lucene) produces better results for longer words. The approach to produce candidates up to a given edit distance will probably work good enough for words found in normal text but will not work good enough for names, addresses and scientific texts. It will increase you index size, though.
If you have the texts indexed you have your text corpus (your dictionary). Only what is in your data can be found anyway. You need not use an external dictionary.
A good resource is Introduction to Information Retrieval - Dictionaries and tolerant retrieval . There is a short description of context sensitive spelling correction.
With regards to populating a Lucene index as the basis of a spell checker, this is a good way to solve the problem. Lucene has an out the box SpellChecker you can use.
There are plenty of word dictionaries available on the net that you can download and use as the basis for your lucene index. I would suggest supplementing these with a number of domain specific texts as well e.g. if your users are medics then maybe supplement the dictionary with source texts from medical thesis and publications.
Try Peter Norvig's spell checker.
You can hit the Gutenberg project or the Internet Archive for lots and lots of corpus.
Also, I think that the Wiktionary could help you. You can even make a direct download.
http://code.google.com/p/google-api-spelling-java is a good Java spell checking library, but I agree with Thomas Jung, that may not be the answer to your problem.
I have a list of words and I want to filter it down so that I only have the nouns from that list of words (Using Java). To do this I am looking for an easy way to query a database of words for their type.
My question is does anybody know of a free, easy word lookup API that would enable me to find the class of a word, not necessarily its semantic definition.
Thanks!
Ben.
EDIT: By class of the word I meant 'part-of-speech' thanks for clearing this up
Word type? Such as verb, noun, adjective, etc? If so, you might run into the issue that some words can be used in more than one way. For example: "Can you trade me that card?", "That was a bad trade."
See this thread for some suggestions.
Have a look at this as well, seems like it might do exactly what you're looking for.
I think what you are looking for is the part-of-speech (POS) of a word. In general that will not be possible to determine except in the context of a sentence. There are many words that have can several different potential parts of speech (e.g. 'bank' can be used as a verb or noun).
You could use a POS tagger to get the information you want. However, the following part-of-speech taggers assume assume that you are tagging words within a well-structured English sentence...
The OpenNLP Java libraries are generally very good and released under the LGPL. There is a part-of-speech tagger for English and a few other languages included in the distribution. Just go to the project page to get the jar (and don't forget to download the models too).
There is also the Stanford part-of-speech tagger, written in Java under the GPL. I haven't had any direct experience with this library, but the Stanford NLP lab is generally pretty awesome.
Querying a database of words is going to lead to the problem that Ben S. mentions, e.g. is it lead (v. to show the way) or lead (n. Pb). If you want to spend some time on the problem, look at Part of Speech tagging. There's some good info in another SO thread.
For English, you could use WordNet with one of the available Java APIs to find the lexical category of a word (which in NLP is most commonly called the part of speech). Using a dedicated POS tagger would be another option.