Requires Python 3.4, NLTK 3.0 and plWordNet 2.1.

To install NLTK (assuming Python 3.4 is installed):

sudo apt-get install python3-pip
sudo easy_install3 pip
sudo pip3.4 install nltk

Polish Wordnet 2.1. is available here. After downloading and unzipping, copy the files from *.pwn_format folder into /usr/share/nltk_data/corpora/wordnet.

• To check whether a word exists in the Wordnet, use is_known(word):

if is_known("spam"):
  print("Spam is a word!")

Note: this function actually returns a list of synsets for a given lemma. In Python, an empty list evaluates to False.

• To list all synonyms (in form of lemmas) for a certain word, use get_synonyms(word):

if is_known("spam"):
  print(get_synonyms("spam"))

• To check whether two words appear together in any synset, use are_synonyms(word, otherWord):

are_synonyms("spam", "eggs")

• To list hyponyms, hypernyms etc. of a word, use get_closure(word, relation, level):

level = 3
rel = lambda s:s.hyponyms()
get_closure("spam", rel, level)

Note: the function uses a breadth-first approach, maximum depth is set with the third argument. For a list of Synset methods, see NLTK source and documentation.

There is also a separate function for hyponyms, namely get_hyponyms(word, level):

level = 3
get_hyponyms("spam", level)

Requires Python 3.4 (developer package), NLTK 3.0 and plWordNet 2.1.

To install Python developer package:

sudo apt-get install python3-dev

Installing NLTK and Polish Wordnet was described in the previous section. To compile and run the program (for Python 3.4):

g++ -o main main.cpp -I /usr/include/python3.4 -l python3.4m
./main

Note: the Python file (wdnet.py) must be in the same directory.

• To check whether a word exists in the Wordnet, use bool Wordnet::Reader::isKnown(std::string word)
• To get all synonyms (in form of lemmas) for a certain word, use std::vector<std::string> Wordnet::Reader::getSynonymsOf(std::string word)
• To check whether two words appear together in any synset, use bool Wordnet::Reader::areSynonyms(std::string word, std::string otherWord)
• To get hyponyms of a word up to a given level, use std::vector<std::string> Wordnet::Reader::getHyponymsOf(std::string word, int level)

Example:

#include <Python.h>
#include <iostream>
#include <string>

#include "wordnet_reader.h"
#include "wordnet_reader.cpp"

int main() {
  Py_Initialize();
  //append current directory to path
  PyObject* sysPath = PySys_GetObject((char*)"path");
  PyObject* curDir = PyUnicode_FromString(".");
  PyList_Append(sysPath, curDir);
  Py_DECREF(curDir);

  int level = 2;
  std::string word = "lekarz";
  std::string otherWord = "chirurg"
  std::vector<std::string> synonyms;
  std::vector<std::string> hyponyms;
  Wordnet::Reader wordnet = Wordnet::Reader();

  if (isKnown(word) && isKnown(otherWord)) {
    synonyms = wordnet.getSynonymsOf(word);
    hyponyms = wordnet.getHyponymsOf(word, level);
    if (wordnet.areSynonyms(word, otherWord))
      std::cout<<"Yes, "<<word<<" and "<<otherWord<<" are synonyms."
  }
  Py_Finalize()
  return 0;
}

Concraft-pl is a morphosyntactic tagger for Polish based on constrained conditional random fields. It combines the following components into a pipeline:

• Maca, a morphosyntactic segmentation and analysis tool
• Concraft, a morphosyntactic disambiguation library

For more information, see the project website.

First, install necessary utils and libraries:

sudo apt-get install build-essential cmake bison flex python-dev swig git subversion
sudo apt-get install libicu-dev libboost-dev libloki-dev libxml++-dev libedit-dev libreadline-dev

Next, install Morfeusz SGJP. The package is available here. Alternatively run:

sudo add-apt-repository ppa:bartosz-zaborowski/nlp
sudo apt-get update
sudo apt-get install morfeusz-sgjp

Install Stuttgart Finite State Tools:

sudo apt-get install sfst

Next, use git to clone Corpus2, Toki, and Maca repositories (this make take a few tries):

cd ~
git clone http://nlp.pwr.wroc.pl/corpus2.git
git clone http://nlp.pwr.wroc.pl/toki.git
git clone http://nlp.pwr.wroc.pl/maca.git

Install Corpus2:

mkdir corpus2/bin
cd corpus2/bin
cmake ..
make
sudo make install

Use the above procedure to install Maca and Toki. Finally run:

sudo ldconfig

First, obtain Haskell Platform:

sudo apt-get install haskell-platform

Next, use Cabal to install Concraft-pl:

cabal update 
cabal install concraft-pl

Finally, download a pre-trained model, rename it to model.gz (do not unzip it) and put into Concraft directory (default is ~/.cabal/bin). You can now test the installation by running:

concraft-pl tag model.gz < input.txt > output.plain

For more information, see the Github repo.