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synset.py

# Natural Language Toolkit: Wordnet Interface: Wordnet Module
#
# Copyright (C) 2001-2009 NLTK Project
# Author: Oliver Steele <steele@osteele.com>
#         David Ormiston Smith <daosmith@csse.unimelb.edu.au>>
#         Steven Bird <sb@csse.unimelb.edu.au>
# URL: <http://www.nltk.org/>
# For license information, see LICENSE.TXT

import math
import pickle
import string
import re

from nltk import defaultdict
from nltk.util import binary_search_file
from nltk.internals import deprecated

from util import *
import dictionary
import similarity
from frequency import *
from lexname import Lexname

class Word(object):
    
    @deprecated("Use nltk.corpus.wordnet.Lemma() instead.")
    def __init__(self, line):
        """
        Extract a word from a line of a WordNet POS file.
        @type  line: C{string}
        @param line: The appropriate line taken from the Wordnet data files.
        """

        tokens = line.split()
        ints = map(int, tokens[int(tokens[3]) + 4:])

        self.form = tokens[0].replace('_', ' ')   # orthography
        self.pos = normalizePOS(tokens[1])        # NOUN, VERB, ADJECTIVE, ADVERB
        self.taggedSenseCount = ints[1]           # Number of senses tagged
        self._synsetOffsets = ints[2:ints[0]+2]   # Offsets of this word's synsets

    def synsets(self):
        """
        Get a sequence of the L{synsets}s of this word.

        >>> from nltk.wordnet import *
        >>> N['dog'].synsets()
        [{noun: dog, domestic dog, Canis familiaris}, {noun: frump, dog}, {noun: dog}, {noun: cad, bounder, blackguard, dog, hound, heel}, {noun: frank, frankfurter, hotdog, hot dog, dog, wiener, wienerwurst, weenie}, {noun: pawl, detent, click, dog}, {noun: andiron, firedog, dog, dog-iron}]

        @return: A list of this L{Word}'s L{Synset}s
        """

        try:
            return self._synsets
        except AttributeError:
            self._synsets = [dictionary.synset(self.pos, offset)
                             for offset in self._synsetOffsets]
            del self._synsetOffsets
            return self._synsets

    def senses(self):
        """
        Return a list of WordSense objects corresponding to this word's L{synset}s.
        """
        return [s.wordSense(self.form) for s in self.synsets()]

    def senseCounts(self):
        """
        Return the frequencies of each sense of this word in a tagged concordance.
        """
        return [s.count() for s in self.senses()]

    def isTagged(self):
        """
        >>> from nltk.wordnet import *
        >>> N['dog'].isTagged()
        True

        @return: True/false (1/0) if one of this L{Word}'s senses is tagged.
        """
        return self.taggedSenseCount > 0
    
# Broken
#    def getAdjectivePositions(self):
#        """
#        >>> from nltk.wordnet import *
#        >>> ADJ['clear'].getAdjectivePositions()
#        [None, 'predicative']
#
#        @return: Return a list of adjective positions that this word can
#        appear in. These are elements of ADJECTIVE_POSITIONS.
#        """
#
#        return list(set(synset.position for synset in self))

    def __getitem__(self, idx):
        return self.synsets()[idx]
    
    def __iter__(self):
        return iter(self.synsets())

    def __contains__(self, item):
        return item in self.synsets()
    
    def __getslice__(self, i, j):
        return self.synsets()[i:j]
    
    def __len__(self):
        return len(self.synsets())

    def __repr__(self):
#        return "<Word:" + self.form + '/' + self.pos + ">"
        return self.__str__()

    def __str__(self):
        return self.form + ' (' + self.pos + ")"

    def __cmp__(self, other):
        return _compareInstances(self, other, ('form', 'pos'))

    def __hash__(self):
        return hash((self.form, self.pos))


00126 class WordSense(object):
    """
    A single word-sense pairing, indicated by in WordNet by a sense key of
    the form::
       lemma%ss_type:lex_filenum:lex_id:head_word:head_id
    """

    _ssTypeMap = {'n': 1, 'v': 2, 'a': 3, 'r': 4, 's':5}
    _ssTypeRevMap = dict((v,k) for k,v in _ssTypeMap.iteritems())

    @deprecated("Use nltk.corpus.wordnet.Lemma() instead.")
    def __init__(self, senseKey):
        self.senseKey = senseKey
        self.lemma, remainder = senseKey.split('%', 1)
        (ssType, lexFilenum, lexId,
                self.headWord, headId) = remainder.split(':')

        self.ssType = self._ssTypeRevMap[int(ssType)]
        self.lexFilenum = int(lexFilenum)
        self.lexId = int(lexId)
        try:
            self.headId = int(headId)
        except ValueError:
            self.headId = None

    def count(self):
        return senseCount(self.senseKey)
    
    def _senseIndexLine(self):
        try:
            WordSense._index
        except AttributeError:
            path = nltk.data.find('corpora/wordnet/index.sense')
            WordSense._index = open(path, FILE_OPEN_MODE)
        
        res = binary_search_file(WordSense._index, self.senseKey)
        if res:
            return res
        raise ValueError("Count not find data for sense '%s'. "
            "Is the key wrong?" % self.senseKey)

    def synset(self):
        line = self._senseIndexLine()
        return dictionary.synset(self.ssType, int(line.split()[1]))

    def word(self):
        return dictionary.word(self.lemma, self.ssType)

    def senseNo(self):
        line = self._senseIndexLine()
        return int(line.split()[2])

    def lexname(self):
        return Lexname.lexnames[self.lexFilenum]

    def __str__(self):
        return ('%s (%s) %d'
                % (self.lemma, normalizePOS(self.ssType), self.senseNo()))
            
    def __cmp__(self, other):
        return _compareInstances(self, other, ('senseKey',))
        
    def __hash__(self):
        return hash(self.senseKey)
        

    __repr__ = __str__

    @staticmethod
    def fromSynset(synset, lemma, lex_id):
        ss_type = WordSense._ssTypeMap[synset.ssType]
        lex_filenum = synset.lexname.id
        head_word = ''
        head_id = ''
        if synset.ssType == 's':
            # Satellite adjectives are treated specially
            head_word = synset.headSynset.words[0]
            head_id = synset.headSynset.wordSenses[0].lexId

        return WordSense.fromKeyParams(
                lemma.lower(), ss_type, lex_filenum, lex_id, head_word, head_id)

    @staticmethod
    def fromKeyParams(lemma, ss_type, lex_filenum, lex_id,
            head_word='', head_id=''):

        if head_word:
            head_id = '%02d' % head_id

        return WordSense('%s%%%d:%02d:%02d:%s:%s'
                % (lemma, ss_type, lex_filenum, lex_id, head_word, head_id))


00219 class Synset(object):
    """
    A set of synonyms.
    
    Each synset contains one or more Senses, which represent a
    specific sense of a specific word.  Senses can be retrieved via
    synset.senses() or through the index notations synset[0],
    synset[string], or synset[word]. Synsets participate in
    lexical relations, which can be accessed via synset.relations().

    >>> from nltk.wordnet import *
    >>> N['dog'][0]
    {noun: dog, domestic_dog, Canis_familiaris}
    >>> N['dog'][0][HYPERNYM]
    [{noun: canine, canid}, {noun: domestic_animal, domesticated_animal}]
    >>> V['think'][0].verbFrameStrings
    ['Something think something Adjective/Noun', 'Somebody think somebody']

    @type pos: C{string}
    @ivar pos: The part of speech -- one of NOUN, VERB, ADJECTIVE, ADVERB.

    @type offset: C{int}
    @ivar offset: An integer offset into the part-of-speech file. Together
        with pos, this can be used as a unique id.

    @type gloss: C{string}
    @ivar gloss: A gloss (dictionary definition) for the sense.

    @type verbFrames: C{list} of C{integer}
    @ivar verbFrames: A sequence of integers that index into
        VERB_FRAME_STRINGS. These list the verb frames that any
        Sense in this synset participates in. (See also
        Sense.verbFrames.) Defined only for verbs.
    """
    
    @deprecated("Use nltk.corpus.wordnet.Synset() instead.")
00255     def __init__(self, pos, offset, line):
        """Initialize the synset from a line in a WordNet lexicographer file."""

        # Part of speech -- one of NOUN, VERB, ADJECTIVE, ADVERB.
        self.pos = pos

        # Integer offset into the part-of-speech file. Together with pos,
        # this can be used as a unique id.
        self.offset = offset
        
        # cache min and max depth
        self._min_depth = self._max_depth = None

        # The synset entry can be broadly divided into two parts: the
        # synset and relational data, and its human readable description, or
        # gloss. The '|' character separates these.

        dividerIndex = line.index('|')
        tokens = line[:dividerIndex].split()
        self.ssType = tokens[2]
        self.gloss = line[dividerIndex + 1:].strip()
        self.lexname = Lexname.lexnames[int(tokens[1])]

        # TODO: This next code is dense and confusing. Clean up at some point.
        # line is of the form: 
        # synset_offset lex_filenum ss_type w_cnt word lex_id [word lex_id...] p_cnt [ptr...] [frames...] | gloss 
        
        synset_cnt = int(tokens[3], 16) # hex integer representing number of items in the synset; same as w_cnt above

        #extract all pairs of the form (sense, lex_id), plus a remainder
        (senseTuples, remainder1) = _partition(tokens[4:], 2, synset_cnt)
        self.words = [form for form, lex_id in senseTuples]

        #extract all pointer quadruples, plus a remainder
        (self._pointerTuples, remainder2) = _partition(remainder1[1:], 4, int(remainder1[0]))

        # Find word senses (via sense keys) from lemma and lex_id
        if self.ssType == 's':
            # need head synset available for finding sense_keys
            self.headSynset = self.relation('similar')[0]
        self.wordSenses = [WordSense.fromSynset(self, form, int(lex_id, 16))
                           for form, lex_id in senseTuples]

        #frames: In data.verb only, a list of numbers corresponding to the
        #generic verb sentence frames for word s in the synset. frames is of
        #the form:
        #f_cnt   +   f_num  w_num  [ +   f_num  w_num...]
        #where f_cnt is a two digit decimal integer indicating the number of
        #generic frames listed, f_num is a two digit decimal integer frame
        #number, and w_num is a two digit hexadecimal integer indicating the
        #word in the synset that the frame applies to. As with pointers, if
        #this number is 00 , f_num applies to all word s in the synset. If
        #non-zero, it is applicable only to the word indicated. Word numbers
        #are assigned as described for pointers.
        
        if pos == VERB:
            (vfTuples, remainder3) = _partition(remainder2[1:], 3, int(remainder2[0]))
            
            #now only used for senseVerbFrames
            def extractVerbFrames(index, vfTuples):
                return tuple(map(lambda t:int(t[1]), filter(lambda t,i=index:int(t[2],16) in (0, i), vfTuples)))
                
            senseVerbFrames = []
            for index in range(1, len(self.words) + 1):
                senseVerbFrames.append(extractVerbFrames(index, vfTuples))
            self._senseVerbFrames = senseVerbFrames

            # A sequence of integers that index into VERB_FRAME_STRINGS. These
            # list the verb frames that any Sense in this synset participates
            # in (see also Sense.verbFrames). Defined only for verbs.
            
            self.verbFrames = tuple(extractVerbFrames(None, vfTuples))
            
            #A list of verb frame strings for this synset
            self.verbFrameStrings = self.extractVerbFrameStrings(vfTuples)

00331     def wordSense(self, word):
        """
        Return the WordSense object for the given word in this synset.
        """
        word = word.replace(' ', '_')
        try:
            index = self.words.index(word)
        except ValueError:
            try:
                # Try for proper noun
                index = self.words.index(word.title())
            except ValueError:
                raise ValueError(
                        "Could not find word '%s' for this synset." % word)

        return self.wordSenses[index]

00348     def extractVerbFrameStrings(self, vfTuples):
        """
        Return a list of verb frame strings for this synset.
        """
        # extract a frame index if 3rd item is 00
        frame_indices = [int(t[1]) for t in vfTuples if int(t[2], 16) == 0]
        try:
            verbFrames = [VERB_FRAME_STRINGS[i] for i in frame_indices]
        except IndexError:
            return []
        #ideally we should build 3rd person morphology for this form
        form = self[0]
        verbFrameStrings = [vf % form for vf in verbFrames]
        return verbFrameStrings
            
00363     def relations(self):
        """
        Return a dictionary of synsets, one per lexical relation

        @return: relations defined on this L{Synset}.
        """

        # Load the pointers from the Wordnet files if necessary.
        if not hasattr(self, '_relations'):
            relations = defaultdict(list)

            for (type, offset, pos, indices) in self._pointerTuples:
                rel = _RELATION_TABLE[type]
                idx = int(indices, 16) & 255
                pos = normalizePOS(pos)
                offset = int(offset)

                synset = dictionary.synset(pos, offset)
                if idx:
                    relations[rel].append(synset[idx-1])
                else:
                    relations[rel].append(synset)
            del self._pointerTuples
            self._relations = dict(relations)
            
        return self._relations

    def relation(self, rel):
        return self.relations().get(rel, [])

    ### BROKEN:
00394     def isTagged(self):
        """
        >>> from nltk.wordnet import *
        >>> N['dog'][0].isTagged()
        True

        >>> N['dog'][1].isTagged()
        False

        @return: True/false (1/0) if one of this L{Word}'s senses is tagged.
        """
        return len(filter(Word.isTagged, self.words)) > 0
  
00407     def __str__(self):
        """
        Return a human-readable representation.

        >>> from nltk.wordnet import *
        >>> str(N['dog'][0].synset)
        '{noun: dog, domestic dog, Canis familiaris}'
        """
        return "{" + self.pos + ": " + string.join(self.words, ", ") + "}"
    
    def __repr__(self):
        return "{" + self.pos + ": " + string.join(self.words, ", ") + "}"
    
    def __cmp__(self, other):
        return _compareInstances(self, other, ('pos', 'offset'))
    
    def __hash__(self):
        return hash((self.pos, self.offset))

    def __ne__(self, other):
        return not (self==other)

    def __getitem__(self, idx):
        try:
            return self.words[idx]       # integer key
        except TypeError:
            return self.relation(idx)    # string key
    
    def __iter__(self):
        return iter(self.words)

    def __contains__(self, item):
        return item in self.words
    
    def __getslice__(self, i, j):
        return self.words[i:j]

    def __nonzero__(self):
        return 1
    
00447     def __len__(self):
        """
        >>> from nltk.wordnet import *
        >>> len(N['dog'][0].synset)
        3
        """
        return len(self.words)
    
00455     def max_depth(self):
        """
        @return: The length of the longest hypernym path from this synset to the root.
        """

        if not self._max_depth:
            if self[HYPERNYM] == []:
                self._max_depth = 0
            else:
                self._max_depth = 1 + max(h.max_depth() for h in self[HYPERNYM])
        return self._max_depth

00467     def min_depth(self):
        """
        @return: The length of the shortest hypernym path from this synset to the root.
        """

        if not self._min_depth:
            if self[HYPERNYM] == []:
                self._min_depth = 0
            else:
                self._min_depth = 1 + min(h.min_depth() for h in self[HYPERNYM])
        return self._min_depth

00479     def closure(self, rel, depth=-1):
        """Return the transitive closure of source under the rel relationship, breadth-first
    
        >>> dog = N['dog'][0]
        >>> dog.closure(HYPERNYM)
        [{noun: dog, domestic dog, Canis familiaris}, {noun: canine, canid}, {noun: carnivore}, {noun: placental, placental mammal, eutherian, eutherian mammal}, {noun: mammal, mammalian}, {noun: vertebrate, craniate}, {noun: chordate}, {noun: animal, animate being, beast, brute, creature, fauna}, {noun: organism, being}, {noun: living thing, animate thing}, {noun: object, physical object}, {noun: physical entity}, {noun: entity}]
        """
        from nltk.util import breadth_first
        synset_offsets = []
        for synset in breadth_first(self, lambda s:s[rel], depth):
            if synset.offset != self.offset and synset.offset not in synset_offsets:
                synset_offsets.append(synset.offset)
                yield synset
#        return synsets

00494     def hypernym_paths(self):
        """
        Get the path(s) from this synset to the root, where each path is a
        list of the synset nodes traversed on the way to the root.

        @return: A list of lists, where each list gives the node sequence
           connecting the initial L{Synset} node and a root node.
        """
        paths = []

        hypernyms = self[HYPERNYM]
        if len(hypernyms) == 0:
            paths = [[self]]

        for hypernym in hypernyms:
            for ancestor_list in hypernym.hypernym_paths():
                ancestor_list.append(self)
                paths.append(ancestor_list)
        return paths

00514     def hypernym_distances(self, distance, verbose=False):
        """
        Get the path(s) from this synset to the root, counting the distance
        of each node from the initial node on the way. A list of
        (synset, distance) tuples is returned.

        @type  distance: C{int}
        @param distance: the distance (number of edges) from this hypernym to
            the original hypernym L{Synset} on which this method was called.
        @return: A list of (L{Synset}, int) tuples where each L{Synset} is
           a hypernym of the first L{Synset}.
        """
        distances = set([(self, distance)])

        for hypernym in self[HYPERNYM]:
            distances |= hypernym.hypernym_distances(distance+1, verbose=False)
        if verbose:
            print "> Hypernym Distances:", self, string.join(synset.__str__() + ":" + `dist` for synset, dist in distances)
        return distances

00534     def shortest_path_distance(self, other):
        """
        Returns the distance of the shortest path linking the two synsets (if
        one exists). For each synset, all the ancestor nodes and their distances
        are recorded and compared. The ancestor node common to both synsets that
        can be reached with the minimum number of traversals is used. If no
        ancestor nodes are common, -1 is returned. If a node is compared with
        itself 0 is returned.

        @type  other: L{Synset}
        @param other: The Synset to which the shortest path will be found.
        @return: The number of edges in the shortest path connecting the two
            nodes, or -1 if no path exists.
        """

        if self == other: return 0

        path_distance = -1

        dist_list1 = self.hypernym_distances(0)
        dist_dict1 = {}

        dist_list2 = other.hypernym_distances(0)
        dist_dict2 = {}

        # Transform each distance list into a dictionary. In cases where
        # there are duplicate nodes in the list (due to there being multiple
        # paths to the root) the duplicate with the shortest distance from
        # the original node is entered.

        for (l, d) in [(dist_list1, dist_dict1), (dist_list2, dist_dict2)]:
            for (key, value) in l:
                if key in d:
                    if value < d[key]:
                        d[key] = value
                else:
                    d[key] = value
        
        # For each ancestor synset common to both subject synsets, find the
        # connecting path length. Return the shortest of these.

        for synset1 in dist_dict1.keys():
            for synset2 in dist_dict2.keys():
                if synset1 == synset2:
                    new_distance = dist_dict1[synset1] + dist_dict2[synset2]
                    if path_distance < 0 or new_distance < path_distance:
                        path_distance = new_distance

        return path_distance

00584     def tree(self, rel, depth=-1, cut_mark=None):
        """
        >>> dog = N['dog'][0]
        >>> from pprint import pprint
        >>> pprint(dog.tree(HYPERNYM))
        ['dog' in {noun: dog, domestic dog, Canis familiaris},
         [{noun: canine, canid},
          [{noun: carnivore},
           [{noun: placental, placental mammal, eutherian, eutherian mammal},
            [{noun: mammal, mammalian},
             [{noun: vertebrate, craniate},
              [{noun: chordate},
               [{noun: animal, animate being, beast, brute, creature, fauna},
                [{noun: organism, being},
                 [{noun: living thing, animate thing},
                  [{noun: object, physical object},
                   [{noun: physical entity}, [{noun: entity}]]]]]]]]]]]]]
        """

        tree = [self]        
        if depth != 0:
            tree += [x.tree(rel, depth-1, cut_mark) for x in self[rel]]
        elif cut_mark:
            tree += [cut_mark]
        return tree

    # interface to similarity methods
     
    def path_similarity(self, other, verbose=False):
        return similarity.path_similarity(self, other, verbose)

    def lch_similarity(self, other, verbose=False):
        return similarity.lch_similarity(self, other, verbose)
        
    def wup_similarity(self, other, verbose=False):
        return similarity.wup_similarity(self, other, verbose)

    def res_similarity(self, other, ic, verbose=False):
        return similarity.res_similarity(self, other, ic, verbose)

    def jcn_similarity(self, other, ic, verbose=False):
        return similarity.jcn_similarity(self, other, ic, verbose)
    
    def lin_similarity(self, other, ic, verbose=False):
        return similarity.lin_similarity(self, other, ic, verbose)


# Lexical Relations

_RELATION_TABLE = {
    '!': ANTONYM,           '@': HYPERNYM,           '~': HYPONYM,        '=': ATTRIBUTE,
    '^': ALSO_SEE,          '*': ENTAILMENT,         '>': CAUSE,          '$': VERB_GROUP,
    '#m': MEMBER_MERONYM,   '#s': SUBSTANCE_MERONYM, '#p': PART_MERONYM, 
    '%m': MEMBER_HOLONYM,   '%s': SUBSTANCE_HOLONYM, '%p': PART_HOLONYM,
    '&': SIMILAR,           '<': PARTICIPLE_OF,      '\\': PERTAINYM,     '+': FRAMES,
    ';c': CLASSIF_CATEGORY, ';u': CLASSIF_USAGE,     ';r': CLASSIF_REGIONAL,
    '-c': CLASS_CATEGORY,   '-u': CLASS_USAGE,       '-r': CLASS_REGIONAL,
    '@i': INSTANCE_HYPERNYM,'~i': INSTANCE_HYPONYM,
    }
    
# Private Utility Functions

def _index(key, sequence, testfn=None, keyfn=None):
    """
    Return the index of key within sequence, using testfn for
    comparison and transforming items of sequence by keyfn first.
    
    >>> _index('e', 'hello')
    1
    >>> _index('E', 'hello', testfn=_equalsIgnoreCase)
    1
    >>> _index('x', 'hello')
    """
    index = 0
    for element in sequence:
        value = element
        if keyfn:
            value = keyfn(value)
        if (not testfn and value == key) or (testfn and testfn(value, key)):
            return index
        index = index + 1
    return None

def _partition(sequence, size, count):
    """
    Partition sequence into C{count} subsequences of
    length C{size}, and a remainder.
    
    Return C{(partitions, remainder)}, where C{partitions} is a sequence of
    C{count} subsequences of cardinality C{size}, and
    C{apply(append, partitions) + remainder == sequence}.
    """

    partitions = []
    for index in range(0, size * count, size):
        partitions.append(sequence[index:index + size])
    return (partitions, sequence[size * count:])

def _compareInstances(a, b, fields):
    """
    Return -1, 0, or 1 according to a comparison first by type,
    then by class, and finally by each of fields. Used when comparing two
    Wordnet objects (Synsets, Words, or Senses) to each other.
    """
    if not hasattr(b, '__class__'):
        return cmp(type(a), type(b))
    elif a.__class__ != b.__class__:
        return cmp(a.__class__, b.__class__)

    for field in fields:
        diff = cmp(getattr(a, field), getattr(b, field))
        if diff: return diff

    return 0

def _equalsIgnoreCase(a, b):
    """
    Return true iff a and b have the same lowercase representation.
    
    >>> _equalsIgnoreCase('dog', 'Dog')
    True
    >>> _equalsIgnoreCase('dOg', 'DOG')
    True
    """
    return a == b or a.lower() == b.lower()



def demo():
    from nltk import wordnet
    from pprint import pprint
    
    dog = wordnet.N['dog']
    cat = wordnet.N['cat']

    print "wordnet.N['dog']"
    print 'dog' in wordnet.N
    print dog
    print dog.pos, dog.form
    print dog.taggedSenseCount
    print dog.synsets()
    print dog.isTagged()
    # ADJ['clear'].getAdjectivePositions()
    # N['cat'] < N['dog']
    # N['dog'] < V['dog']

    print "Verb Frames:",
    print wordnet.V['think'][0].verbFrameStrings

    print "Relations:"
    print dog[0].relations()
    print dog[0][wordnet.HYPERNYM]

    print "Glosses:"
    print dog[0].gloss
    print dog[0].relation(wordnet.HYPERNYM)[0].gloss

    print
    print "Paths and Distances:"
    print

    print dog[0].hypernym_paths()
    print dog[0].hypernym_distances(0)
    print dog[0].shortest_path_distance(cat[0])
    
    print
    print "Closures and Trees:"
    print

    pprint(wordnet.ADJ['red'][0].closure(wordnet.SIMILAR, depth=1))
    pprint(wordnet.ADJ['red'][0].closure(wordnet.SIMILAR, depth=2))
    pprint(dog[0].tree(wordnet.HYPERNYM))
    pprint(dog[0].tree(wordnet.HYPERNYM, depth=2, cut_mark = '...'))
    
    entity = wordnet.N["entity"]
    print entity, entity[0]
    print entity[0][wordnet.HYPONYM]
    pprint(entity[0].tree(wordnet.HYPONYM, depth=1), indent=4)
    abstract_entity = wordnet.N["abstract entity"]
    print abstract_entity, abstract_entity[0]
    print abstract_entity[0][wordnet.HYPONYM]
    pprint(abstract_entity[0].tree(wordnet.HYPONYM, depth=1), indent=4)
        
    # Adjectives that are transitively SIMILAR to any of the senses of 'red'
    #flatten1(map(lambda sense:closure(sense, SIMILAR), ADJ['red']))    # too verbose

    print "All the words in the hyponym synsets of dog[0]"
    print [word for synset in dog[0][wordnet.HYPONYM] for word in synset]

    print "Hyponyms of the first (and only) sense of 'animal' that are homophonous with verbs:"
    print [word for synset in wordnet.N['animal'][0].closure(wordnet.HYPONYM) for word in synset if word in wordnet.V]

    # BROKEN
    print "Senses of 'raise'(v.) and 'lower'(v.) that are antonyms:"
    print filter(lambda p:p[0] in p[1][wordnet.ANTONYM], [(r,l) for r in wordnet.V['raise'] for l in wordnet.V['lower']])

    print
    print "Similarity: dog~cat"
    print
    
    print "Path Distance Similarity:",
    print dog[0].path_similarity(cat[0])
    print "Leacock Chodorow Similarity:",
    print dog[0].lch_similarity(cat[0])
    print "Wu Palmer Similarity:",
    print dog[0].wup_similarity(cat[0])

#    set up the data file
#    print "Resnik Similarity:",
#    print dog[0].resnik_similarity(cat[0], datafile)
#    print "Jiang-Conrath Similarity:",
#    print dog[0].jiang_conrath_similarity(cat[0], datafile)
#    print "Lin Similarity:",
#    print dog[0].lin_similarity(cat[0], datafile)

if __name__ == '__main__':
    demo()
    

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