StanfordNLP lemmatization cannot handle -ing words
java,nlp,stanford-nlp,stemming,lemmatization
Lemmatization crucially depends on the part of speech of the token. Only tokens with the same part of speech are mapped to the same lemma. In the sentence "This is confusing", confusing is analyzed as an adjective, and therefore it is lemmatized to confusing. In the sentence "I was confusing...
StanfordCoreNLP does not work in my way
java,nlp,stanford-nlp,stemming,lemmatization
Lemmatization should ideally return a canonical form (known as 'lemma' or 'headword') of a group of words. This canonical form, however, is not always what we intuitively expect. For example, you expect "learning" to be yield the lemma "learn". But the noun "learning" has the lemma "learning", while only the...
Difference in handling possessive (apostrophes) with english stemmer between 1.2 and 1.4
elasticsearch,lucene,stemming,porter-stemmer
I think this is the change, introduced in 1.3.0: The StemmerTokenFilter had a number of issues: english returned the slow snowball English stemmer porter2 returned the snowball Porter stemmer (v1) Changes: english now returns the fast PorterStemmer (for indices created from v1.3.0 onwards) porter2 now returns the snowball English stemmer...
Porter Stemming of fried
nlp,nltk,stemming,porter-stemmer
A stem as returned by Porter Stemmer is not necessarily the base form of a verb, or a valid word at all. If you're looking for that, you need to look for a lemmatizer instead.
Are Snowball & SnowballC packages different in R?
You have an older version of pkg:tm. The current version of tm has a DESCRIPTION file that lists SnowballC as a "Suggests". Older versions suggested Snowball. Package: tm Title: Text Mining Package Version: 0.5-10 Date: 2014-01-07 [email protected]: c(person("Ingo", "Feinerer", role = c("aut", "cre"), email = "[email protected]"), person("Kurt", "Hornik", role =...
Lucene - default search lemmatization/stemming
java,lucene,search-engine,stemming,lemmatization
The sample referred in your post uses Lucene StandardAnalyzer which does not do stemming. If you want to use stemming, you need to use an other Analyzer implementation e.g.: SnowballAnalyzer ...
How to split a text into two meaningful words in R
r,string-split,stemming,text-analysis
Given a list of English words you can do this pretty simply by looking up every possible split of the word in the list. I'll use the first Google hit I found for my word list, which contains about 70k lower-case words: wl <- read.table("http://www-personal.umich.edu/~jlawler/wordlist")$V1 check.word <- function(x, wl) {...
Confused about priority between stemmer and pos tagger
python,nltk,stemming,part-of-speech
Why don't you try it out? Here's an example: >>> from nltk.stem import PorterStemmer >>> from nltk import word_tokenize, pos_tag >>> sent = "This is a messed up sentence from the president's Orama and it's going to be sooo good, you're gonna laugh." This is the outcome of tokenizing. >>>...
Are there any Lucene stemmers that handle Shakespearean English?
It looks like the rule changes are minimal for that. So, it might be possible to copy/modify the PorterStemmer class and related Factories/Filters. Or it might be possible to add those specific rules as Regular expression filter before Porter....
How to use/call stemmer (croatian stemmer) [closed]
The answer is written in your file : python Croatian_stemmer.py input_file output_file ...
Mongodb stemming text
Snowball is a commonly used open source stemming approach, with implementations/ports for many (if not most) programming languages. If you only want stemming for your application, you should use the Snowball library directly. MongoDB 2.4+ uses Snowball internally for text stemming & indexing, but does not provide a separate API...
Why cowardly becomes cowardli after stemming?
First let's look at the different stemmers/lemmatizer that NLTK has: >>> from nltk import stem >>> lancaster = stem.lancaster.LancasterStemmer() >>> porter = stem.porter.PorterStemmer() >>> snowball = stem.snowball.EnglishStemmer() >>> wnl = stem.wordnet.WordNetLemmatizer() >>> word = "cowardly" >>> lancaster.stem(word) 'coward' >>> porter.stem(word) u'cowardli' >>> snowball.stem(word) u'coward' >>> wnl.stem(word) Traceback (most recent call...
FastVectorHighlighter phrase highlighting not working with stemming
java,solr,lucene,stemming,fast-vector-highlighter
Turns out hl.fragSize wasn't set to a large enough value to include the entire highlighted sequence. The silly problems are often the worst.
Default english stemming in SOLR
Your query doesn't look right, try to query it like this: http://localhost:8983/solr/collection1/select?q=name:walking&wt=json&indent=true ...
Logical flaw: if List is null return input else print function output
java,null,wordnet,stemming,data-processing
That won't work because the List is not null, the List is empty. You have to do the check like this if (stemmed_words.size() > 0)
Is it possible to get a natural word after it has been stemmed?
Stemmer is able to process artificial non-existing words. Would you like them to be returned as elements of a set of all possible words? How do you know that the word doesn't exist and shouldn't be returned? As an option: find a dictionary of all words and their forms. Find...
UnicodeDecodeError unexpected end of data while stemming over dataset
python,unicode,pandas,nltk,stemming
Read up on unicode string processing in python. There is the type str but there is also a type unicode. I suggest to: decode each line immediately after reading, to narrow down incorrect characters in your input data (real data contains errors) work with unicode and u" " strings everywhere....
Why did PortStemmer in NLTK converts my “string” into u“string”
As @kindall noted, it's becaus eof the unicode string. But more specifically, it's because NLTK uses from __future__ import unicode_literals which converts ALL strings to unicode by default, see https://github.com/nltk/nltk/blob/develop/nltk/stem/porter.py#L87 So let's try an experiment in python 2.x : $ python >>> from nltk.stem import PorterStemmer >>> porter = PorterStemmer()...
How to correctly configure solr stemming
I would also suggest to try a different Stemmer. There are 4 included in Solr solr.PorterStemFilterFactory solr.SnowballPorterFilterFactory solr.KStemFilterFactory solr.HunspellStemFilterFactory (you will need a dictionary for this one from an external source, like open office) Each of those produces different results for your problem, see below. Given the results and that...
Greek words stemming Lucene
You don't really need to do anything with GreekStemFilter directly. Just use GreekAnalyzer. It includes the stemmer.
Stemming text in java [duplicate]
Make this: public static String stem(String string) throws IOException { TokenStream tokenizer = new StandardTokenizer(Version.LUCENE_47, new StringReader(string)); tokenizer = new StandardFilter(Version.LUCENE_47, tokenizer); tokenizer = new LowerCaseFilter(Version.LUCENE_47, tokenizer); tokenizer = new PorterStemFilter(tokenizer); CharTermAttribute token = tokenizer.getAttribute(CharTermAttribute.class); tokenizer.reset(); StringBuilder stringBuilder = new StringBuilder(); while(tokenizer.incrementToken()) { if(stringBuilder.length()...
Terms get truncated after indexing document (Elasticsearch)
This is the effect of the stemmer. By default the snowball stemmer is also used as analyzer. The expected behaviour of stemmer is to convert words to its base form , like below - Jumping => jump Running = > run And so on. snowball stemmer works on an algorithm...
stemming words in python
For each suffix in the given list you can check if the given word ends with any of the given suffixes, if yes the remove the suffix, else return the word. suffixes = ['ing'] def stem(word): for suff in suffixes: if word.endswith(suff): return word[:-len(suff)] return word print(stem ('having')) >>> hav...