Python nltk.corpus.wordnet.synsets() Examples

def is_exact_match(token1, token2):
    token1 = token1.lower()
    token2 = token2.lower()

    if token1 == token2:
        return True

    token1_stem = get_stem(token1)

    for synsets in wn.synsets(token2):
        for lemma in synsets.lemma_names():
            if token1_stem == get_stem(lemma):
                return True

    if token1 == "n't" and token2 == "not":
        return True
    elif token1 == "not" and token2 == "n't":
        return True
    elif token1_stem == get_stem(token2):
        return True
    return False 

def tag_semantic_similarity(x, y, ic):
    mx = wn.morphy(x)
    my = wn.morphy(y)

    if mx is None or my is None:
        return 0

    synX = wn.synsets(mx, pos=wn.NOUN)
    synY = wn.synsets(my, pos=wn.NOUN)

    if len(synX) > 0 and len(synY) > 0:
        maxSim = synX[0].lin_similarity(synY[0], ic)
    else:
        maxSim = 0

    return maxSim 

def get_synonym(root):

	listofsyns = wordnet.synsets(root[0])
	synonym = listofsyns[3].name().split(".")[0]
	
	if root[1] =='VBD':
		synonym = verb.verb_past(synonym)
	elif root[1] =='VBG':
		synonym = verb.verb_present_participle(synonym)
	elif root[1] =='VBN':
		synonym = verb.verb_past_participle(synonym)
	elif root[1] =='VBP':
		synonym = verb.verb_present(synonym, person=3, negate=True)
	elif root[1] =='VBZ':
		synonym = verb.verb_present(synonym, person=3, negate=False)
		
	return synonym

#retrieve paraphrased sentence 

def has_relation(token1, token2, rel_name=""):
    token1 = token1.lower()
    token2 = token2.lower()
    t1 = stemmer.stem(token1)
    t2 = stemmer.stem(token2)
    _t1 = wn.synsets(t1)
    _t2 = wn.synsets(t2)

    if(len(_t1)==0 or len(_t2)==0):
        return [0,0]

    token_2_hyponyms = get_hyponyms(_t2[0])
    token_2_hypernyms = get_hypernyms(_t2[0])

    if(_t1[0] in token_2_hyponyms):
        # t1 is a hyponym of t2
        # print("Hyponym {} {}".format(t1, t2))
        return [1,0]
    elif(_t1[0] in token_2_hypernyms):
        # t1 is a hypernym of t2
        return [0,1]
    else:
        return [0,0] 

def is_antonyms(token1, token2):
    token1 = token1.lower()
    token2 = token2.lower()
    token1_stem = stemmer.stem(token1)
    antonym_lists_for_token2 = []
    for synsets in wn.synsets(token2):
        for l in synsets.lemmas():
            _ant = l.antonyms()
            if(len(_ant)>0):
                antonym_lists_for_token2.append(_ant[0].name())

        # for lemma_synsets in [wn.synsets(l) for l in synsets.lemma_names()]:
        #     for lemma_syn in lemma_synsets:
        #         for lemma in lemma_syn.lemmas():
        #             for antonym in lemma.antonyms():
                        # antonym_lists_for_token2.append(antonym.name())
    antonym_lists_for_token2 = list(set(antonym_lists_for_token2))
    for atnm in antonym_lists_for_token2:
        if token1_stem == stemmer.stem(atnm):
            return 1
    return 0 

def get_static_welcome_message():
    """
    Get the static welcome page.
    """
    return \
"""
<h3>Search Help</h3>
<ul><li>The display below the line is an example of the output the browser
shows you when you enter a search word. The search word was <b>green</b>.</li>
<li>The search result shows for different parts of speech the <b>synsets</b>
i.e. different meanings for the word.</li>
<li>All underlined texts are hypertext links. There are two types of links:
word links and others. Clicking a word link carries out a search for the word
in the Wordnet database.</li>
<li>Clicking a link of the other type opens a display section of data attached
to that link. Clicking that link a second time closes the section again.</li>
<li>Clicking <u>S:</u> opens a section showing the relations for that synset.</li>
<li>Clicking on a relation name opens a section that displays the associated
synsets.</li>
<li>Type a search word in the <b>Next Word</b> field and start the search by the
<b>Enter/Return</b> key or click the <b>Search</b> button.</li>
</ul>
""" 

def is_atomic_mwe(mwe, verb_lemma, complement_lemma, synsets):
    mwe_count = 0
    for synset in synsets:
        gloss_lemmas = set([WordNetLemmatizer().lemmatize(word) for word in synset.definition.split()])
        if verb_lemma in gloss_lemmas or complement_lemma in gloss_lemmas:
            return False
        for syn_lemma in synset.lemmas:
            if syn_lemma.name != mwe: 
                tokens = syn_lemma.name.split('_')
                for token in tokens:
                    if token == verb_lemma:
                        return False
                if len(tokens) == 2 and tokens[1] == complement_lemma:
                    return False
        else:
            mwe_count += syn_lemma.count()
    return True 

def transform_one(self, obs, target, id):
        obs_tokens = nlp_utils._tokenize(obs, token_pattern)
        target_tokens = nlp_utils._tokenize(target, token_pattern)
        obs_synset_list = [wn.synsets(obs_token) for obs_token in obs_tokens]
        target_synset_list = [wn.synsets(target_token) for target_token in target_tokens]
        val_list = []
        for obs_synset in obs_synset_list:
            _val_list = []
            for target_synset in target_synset_list:
                _s = self._maximum_similarity_for_two_synset_list(obs_synset, target_synset)
                _val_list.append(_s)
            if len(_val_list) == 0:
                _val_list = [config.MISSING_VALUE_NUMERIC]
            val_list.append( _val_list )
        if len(val_list) == 0:
            val_list = [[config.MISSING_VALUE_NUMERIC]]
        return val_list 

def length_dist(synset_1, synset_2):
    """
    Return a measure of the length of the shortest path in the semantic
    ontology (Wordnet in our case as well as the paper's) between two
    synsets.
    """
    l_dist = six.MAXSIZE
    if synset_1 is None or synset_2 is None:
        return 0.0
    if synset_1 == synset_2:
        # if synset_1 and synset_2 are the same synset return 0
        l_dist = 0.0
    else:
        wset_1 = set([str(x.name()) for x in synset_1.lemmas()])
        wset_2 = set([str(x.name()) for x in synset_2.lemmas()])
        if len(wset_1.intersection(wset_2)) > 0:
            # if synset_1 != synset_2 but there is word overlap, return 1.0
            l_dist = 1.0
        else:
            # just compute the shortest path between the two
            l_dist = synset_1.shortest_path_distance(synset_2)
            if l_dist is None:
                l_dist = 0.0
    # normalize path length to the range [0,1]
    return math.exp(-ALPHA * l_dist) 

def get_best_synset_pair(word_1, word_2):
    """
    Choose the pair with highest path similarity among all pairs.
    Mimics pattern-seeking behavior of humans.
    """
    max_sim = -1.0
    synsets_1 = wn.synsets(word_1)
    synsets_2 = wn.synsets(word_2)
    if len(synsets_1) == 0 or len(synsets_2) == 0:
        return None, None
    else:
        max_sim = -1.0
        best_pair = None, None
        for synset_1 in synsets_1:
            for synset_2 in synsets_2:
               sim = wn.path_similarity(synset_1, synset_2)
               if sim is not None and sim > max_sim:
                   max_sim = sim
                   best_pair = synset_1, synset_2
        return best_pair 

def synset(data):
    result = {}
    syns = wordnet.synsets(data)
    list = []
    for s in syns:
        r = {}
        r["name"] = s.name()
        r["lemma"] = s.lemmas()[0].name()
        r["definition"] = s.definition()
        r["examples"] = s.examples()
        list.append(r)

    result["list"] = list
    synonyms = []
    antonyms = []
    for syn in syns:
        for l in syn.lemmas():
            synonyms.append(l.name())
            if l.antonyms():
                antonyms.append(l.antonyms()[0].name())

    result["synonyms"] = synonyms
    result["antonyms"] = antonyms
    return json.loads(jsonpickle.encode(result, unpicklable=False)) 

def get_hyponym_stems_p(item, hyponym_stems_dict):
    word, pos = item
    item_key = word + '<(-.-)>' + pos

    if item_key in hyponym_stems_dict:
        return hyponym_stems_dict[item_key]

    stems = set([])
    for synset in wn.synsets(word, pos=pos):
        for lemma in synset.lemma_names():
            for syn_lemma in wn.synsets(lemma, pos=pos):
                try:
                    syn_lemma_hypos = get_hyponyms(syn_lemma)
                except RecursionError:
                    # print(syn_lemma)
                    continue

                for nym in syn_lemma_hypos:
                    stems |= set(nym.lemma_names())
                    stems |= set([get_stem(ln) for ln in nym.lemma_names()])

    result_set = (stems - get_stem_set(word))
    hyponym_stems_dict[item_key] = result_set
    return result_set 

def __init__(self, save_dir, config_path='./miner.yaml'):
        super(ImageNetMiner, self).__init__(save_dir, config_path)
        self.__engine__ = 'imagenet'
        self.format_url = 'http://www.image-net.org/api/text/imagenet.synset.geturls?wnid={}'

        # maximum number of synsets to retrieve - we don't need all images necessarily, other-
        # wise we get enormous amounts of synsets for words like 'entity' or 'animal'
        self.max_synsets = 10000

        self.wnl = WordNetLemmatizer()

        # url cache
        self.imgnet_url_cache = {}

        # whether we "level up" in hierarchy if no images found
        self.level_up_if_no_images = True 

def valid(word, replacement):
    if replacement.lower() != replacement:
        return False
    synset_word = wn.synsets(word)
    synset_replacement = wn.synsets(replacement)
    for item_1 in synset_word:
        for item_2 in synset_replacement:
            if item_1 == item_2:
                return False
    # one-step hypernymy/hyponymy check
    for item_1 in synset_word:
        for subitem in item_1.hypernyms():
            for item_2 in synset_replacement:
                if item_2 == subitem:
                    return False
        for subitem in item_1.hyponyms():
            for item_2 in synset_replacement:
                if item_2 == subitem:
                    return False
    return True 

def get_hypernym_stems(item):
    word, pos = item
    stems = set([])

    for synset in wn.synsets(word, pos=pos):
        for lemma in synset.lemma_names():
            for syn_lemma in wn.synsets(lemma, pos=pos):
                try:
                    syn_lemma_hypers = get_hypernyms(syn_lemma)
                except RecursionError:
                    # print(syn_lemma)
                    continue

                for nym in syn_lemma_hypers:
                    stems |= set(nym.lemma_names())
                    stems |= set([get_stem(ln) for ln in nym.lemma_names()])

    return (stems - get_stem_set(word)) 

def get_hyponym_stems(item):
    word, pos = item
    stems = set([])

    for synset in wn.synsets(word, pos=pos):
        for lemma in synset.lemma_names():
            for syn_lemma in wn.synsets(lemma, pos=pos):
                try:
                    syn_lemma_hypos = get_hyponyms(syn_lemma)
                except RecursionError:
                    # print(syn_lemma)
                    continue

                for nym in syn_lemma_hypos:
                    stems |= set(nym.lemma_names())
                    stems |= set([get_stem(ln) for ln in nym.lemma_names()])

    return (stems - get_stem_set(word)) 

def get_antonym_stems_p(item, ant_dict):
    word, pos = item
    stems = set([])
    item_key = word + '<(-.-)>' + pos
    if item_key in ant_dict:
        return ant_dict[item_key]

    for synset in wn.synsets(word, pos=pos):
        for lemma in synset.lemma_names():
            for syn_lemma in wn.synsets(lemma, pos=pos):
                for l in syn_lemma.lemmas():
                    for antonym in l.antonyms():
                        stems.add(antonym.name())
                        stems.add(get_stem(antonym.name()))

    ant_dict[item_key] = stems

    return stems 

def graph_synsets(terms, pos=wn.NOUN, depth=2):
    """
    Create a networkx graph of the given terms to the given depth.
    """

    G = nx.Graph(
        name="WordNet Synsets Graph for {}".format(", ".join(terms)), depth=depth,
    )

    def add_term_links(G, term, current_depth):
        for syn in wn.synsets(term):
            for name in syn.lemma_names():
                G.add_edge(term, name)
                if current_depth < depth:
                    add_term_links(G, name, current_depth+1)

    for term in terms:
        add_term_links(G, term, 0)

    return G 

def get_hypo_up_to_lv_p(item, level, hypo_lvl_dict):
    word, pos = item
    stems = set([])

    item_key = word + '<(-.-)>' + pos + '#' + str(level)
    if item_key in hypo_lvl_dict:
        return hypo_lvl_dict[item_key]

    for synset in wn.synsets(word, pos=pos):
        for lemma in synset.lemma_names():
            for syn_lemma in wn.synsets(lemma, pos=pos):
                for h in syn_lemma.closure(hypo, level):
                    for h_lemma in h.lemma_names():
                        stems.add(h_lemma)
                        stems.add(get_stem(h_lemma))

    hypo_lvl_dict[item_key] = stems

    return stems 

def get_hypernym_stems(item):
    word, pos = item
    stems = set([])

    for synset in wn.synsets(word, pos=pos):
        for lemma in synset.lemma_names():
            for syn_lemma in wn.synsets(lemma, pos=pos):
                try:
                    syn_lemma_hypers = get_hypernyms(syn_lemma)
                except RecursionError:
                    # print(syn_lemma)
                    continue

                for nym in syn_lemma_hypers:
                    stems |= set(nym.lemma_names())
                    stems |= set([get_stem(ln) for ln in nym.lemma_names()])

    return (stems - get_stem_set(word)) 

def get_hypernym_stems_p(item, hypernym_stems_dict):
    word, pos = item
    item_key = word + '<(-.-)>' + pos
    if item_key in hypernym_stems_dict:
        return hypernym_stems_dict[item_key]

    stems = set([])

    for synset in wn.synsets(word, pos=pos):
        for lemma in synset.lemma_names():
            for syn_lemma in wn.synsets(lemma, pos=pos):
                try:
                    syn_lemma_hypers = get_hypernyms(syn_lemma)
                except RecursionError:
                    # print(syn_lemma)
                    continue

                for nym in syn_lemma_hypers:
                    stems |= set(nym.lemma_names())
                    stems |= set([get_stem(ln) for ln in nym.lemma_names()])

    result_set = (stems - get_stem_set(word))
    hypernym_stems_dict[item_key] = result_set
    return result_set 

def get_hyponym_stems(item):
    word, pos = item
    stems = set([])

    for synset in wn.synsets(word, pos=pos):
        for lemma in synset.lemma_names():
            for syn_lemma in wn.synsets(lemma, pos=pos):
                try:
                    syn_lemma_hypos = get_hyponyms(syn_lemma)
                except RecursionError:
                    # print(syn_lemma)
                    continue

                for nym in syn_lemma_hypos:
                    stems |= set(nym.lemma_names())
                    stems |= set([get_stem(ln) for ln in nym.lemma_names()])

    return (stems - get_stem_set(word)) 

def get_related_lemmas(word):
    """
    args
        - word : a word e.g. "lovely"

    returns a list of related lemmas e.g [Lemma('cover_girl.n.01.lovely'),
        Lemma('lovely.s.01.lovely'), Lemma('adorable.s.01.lovely'),
        Lemma('comeliness.n.01.loveliness')]
    returns [] if Wordnet doesn't recognize the word
    """

    all_lemmas_for_this_word = [lemma for ss in wn.synsets(word)
                                for lemma in ss.lemmas()
                                if lemma.name() == word]
    all_related_lemmas = [lemma for lemma in all_lemmas_for_this_word]
    new_lemmas = []
    for lemma in all_lemmas_for_this_word:
        for new_lemma in (lemma.derivationally_related_forms() +
                          lemma.pertainyms()):
            if (not belongs(new_lemma, all_related_lemmas) and
            not belongs(new_lemma, new_lemmas)):
                new_lemmas.append(new_lemma)
    while len(new_lemmas) > 0:
        all_lemmas_for_new_words = []
        for new_lemma in new_lemmas:
            word = new_lemma.name()
            all_lemmas_for_this_word = [lemma for ss in wn.synsets(word)
                                        for lemma in ss.lemmas()
                                        if lemma.name() == word]
            for lemma in all_lemmas_for_this_word:
                if not belongs(lemma, all_lemmas_for_new_words):
                    all_lemmas_for_new_words.append(lemma)
        all_related_lemmas += all_lemmas_for_new_words
        new_lemmas = []
        for lemma in all_lemmas_for_new_words:
            for new_lemma in (lemma.derivationally_related_forms() +
                              lemma.pertainyms()):
                if (not belongs(new_lemma, all_related_lemmas) and
                not belongs(new_lemma, new_lemmas)):
                    new_lemmas.append(new_lemma)
    return all_related_lemmas 

def get_hyper_up_to_lv(item, level):
    word, pos = item
    stems = set([])

    for synset in wn.synsets(word, pos=pos):
        for lemma in synset.lemma_names():
            for syn_lemma in wn.synsets(lemma, pos=pos):
                for h in syn_lemma.closure(hyper, level):
                    for h_lemma in h.lemma_names():
                        stems.add(h_lemma)
                        stems.add(get_stem(h_lemma))

    return stems 

def verbs2basicform(words):
    ret = []
    for w in words:
        analysis = wn.synsets(w)
        if any([a.pos() == 'v' for a in analysis]):
            w = WordNetLemmatizer().lemmatize(w, 'v')
        ret.append(w)
    return ret 

def generate_senses(review):

    review.senses = set()
    for noun in review.nouns:
        review.senses |= set(wordnet.synsets(noun, pos='n')) 

def SemEval2015Task13():
  with open(FILEDIR+'data-SemEval2015_Task13/test/keys/gold_keys/EN/semeval-2015-task-13-en.key', 'r') as f:
    gold = [(split[1], set(key.split('%')[1] for key in split[2:] if key[:3] == 'wn:')) for split in (line.split() for line in f if '\twn:' in line)]
  ids = {entry[0] for entry in gold}
  with open(FILEDIR+'data-SemEval2015_Task13/test/data/semeval-2015-task-13-en.xml','r') as f:
    soup = BS(f, 'lxml')
  data = [(wf['id'], wf['lemma'], wf['pos'], list(split_on_punctuation(' '.join(child.text for child in sent.children if not child == wf and not child == '\n')))) for text in soup('text') for sent in text('sentence') for wf in sent('wf') if wf['id'] in ids and wf['pos'][0].lower() in POSMAP and wn.synsets(wf['lemma'], POSMAP[wf['pos'][0].lower()])]
  id2keys = defaultdict(lambda: set())
  for entry, keys in gold:
    id2keys[entry] = id2keys[entry].union(keys)
  gold = [(entry, id2keys[entry]) for entry, _, _, _ in data]
  return data, gold 

def _get_lemmas(synsets: List[Any]) -> List[str]:
    """
    Return all the lemma names associated with a list of synsets.
    """
    return [lemma_name for synset in synsets for lemma_name in synset.lemma_names()] 

def wordnet_synsets_for_domain(self, domains: List[str]):
        return [synset for synset in self.synsets() if self.__has_domains(synset, domains)] 

def is_noun_in_group(noun, group):
    senses = wordnet.synsets(noun, pos='n')
    return any(i in senses for i in group)