lemmatization

Do anyone know how to resolve this file reading error in TreeTagger that is a common Natural Language Processing tool used to POS tag, lemmatize and chunk sentences? alvas@ikoma:~/treetagger$ echo 'Hello world!' | cmd/tree-tagger-english reading parameters ... ERROR: Can't open for reading: /home/alvas/treetagger/lib/english.par aborted. I didn't encounter any possible installation problems as hinted on http://www.ims.uni-stuttgart.de/projekte/corplex/TreeTagger/installation-hints.txt . I've followed the instructions on the webpage and it's installed properly ( http://www.ims.uni-stuttgart.de

I've tried PorterStemmer and Snowball but both don't work on all words, missing some very common ones. My test words are: " cats running ran cactus cactuses cacti community communities ", and both get less than half right. See also: Stemming algorithm that produces real words Stemming - code examples or open source projects? theycallmemorty If you know Python, The Natural Language Toolkit (NLTK) has a very powerful lemmatizer that makes use of WordNet . Note that if you are using this lemmatizer for the first time, you must download the corpus prior to using it. This can be done by: >>> import

Natural Language Processing (NLP), especially for English, has evolved into the stage where stemming would become an archaic technology if "perfect" lemmatizers exist. It's because stemmers change the surface form of a word/token into some meaningless stems. Then again the definition of the "perfect" lemmatizer is questionable because different NLP task would have required different level of lemmatization. E.g. Convert words between verb/noun/adjective forms . Stemmers [in]: having [out]: hav Lemmatizers [in]: having [out]: have So the question is, are English stemmers any useful at all today?