I'm asking because I thought there are no pre-trained models for the lemmatizer. How are you using it exactly? There's also an option to use a dictionary, e.g. https://stackoverflow.com/questions/38982423/opennlp-lemmatization-example
AFAIK the models in 1.8.1 are the same as 1.5.3 jds On Thu, Jul 6, 2017 at 6:26 PM, Ling <[email protected]> wrote: > The openNLP1.5.3. I will update to 1.8.1 version after this week, if it's > an issue due to old models. > > Thanks. > > On Thu, Jul 6, 2017 at 3:19 PM, John Stewart <[email protected]> wrote: > > > What model or dictionary are you using with the lemmatizer? > > > > jds > > > > On Thu, Jul 6, 2017 at 6:05 PM, Ling <[email protected]> wrote: > > > > > Hi, the problem with lemma is that, for "tmoble", the lemma returned by > > > openNLP is "null", not "tmoble". > > > > > > Why is it? > > > > > > On Mon, Jul 3, 2017 at 6:54 PM, Rakesh P <[email protected]> > wrote: > > > > > > > Hi, > > > > Stemmer works based on some predefined rules. Examples for rules are > > > "word > > > > that ends with 'e'". So, if you want to get a meaning word after > > > > preprocessing, then better use lemmatization. > > > > > > > > Regards, > > > > Rakesh P > > > > > > > > > On 03-Jul-2017, at 10:24 PM, Ling <[email protected]> wrote: > > > > > > > > > > Hi, I noticed that some words are stemmed like the following: > > > > > > > > > > iphone -> iphon > > > > > tmobile -> T-mobil > > > > > > > > > > Is there some parameter to control this behavior? In such cases, > > those > > > > > stems are actually harmful, making them become unknown words in > text. > > > > Since > > > > > these are quite common, I am just curious whether there is a way to > > > > change > > > > > the default behavior. > > > > > > > > > > Thanks. > > > > > Ling > > > > > > > > > >
