Python wordnet.synsets方法代碼示例

# 需要導入模塊: from nltk.corpus import wordnet [as 別名]
# 或者: from nltk.corpus.wordnet import synsets [as 別名]
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 

# 需要導入模塊: from nltk.corpus import wordnet [as 別名]
# 或者: from nltk.corpus.wordnet import synsets [as 別名]
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 

# 需要導入模塊: from nltk.corpus import wordnet [as 別名]
# 或者: from nltk.corpus.wordnet import synsets [as 別名]
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 

# 需要導入模塊: from nltk.corpus import wordnet [as 別名]
# 或者: from nltk.corpus.wordnet import synsets [as 別名]
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)) 

# 需要導入模塊: from nltk.corpus import wordnet [as 別名]
# 或者: from nltk.corpus.wordnet import synsets [as 別名]
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)) 

# 需要導入模塊: from nltk.corpus import wordnet [as 別名]
# 或者: from nltk.corpus.wordnet import synsets [as 別名]
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 

# 需要導入模塊: from nltk.corpus import wordnet [as 別名]
# 或者: from nltk.corpus.wordnet import synsets [as 別名]
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 

# 需要導入模塊: from nltk.corpus import wordnet [as 別名]
# 或者: from nltk.corpus.wordnet import synsets [as 別名]
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)) 

# 需要導入模塊: from nltk.corpus import wordnet [as 別名]
# 或者: from nltk.corpus.wordnet import synsets [as 別名]
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 

# 需要導入模塊: from nltk.corpus import wordnet [as 別名]
# 或者: from nltk.corpus.wordnet import synsets [as 別名]
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 

# 需要導入模塊: from nltk.corpus import wordnet [as 別名]
# 或者: from nltk.corpus.wordnet import synsets [as 別名]
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 

# 需要導入模塊: from nltk.corpus import wordnet [as 別名]
# 或者: from nltk.corpus.wordnet import synsets [as 別名]
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 

# 需要導入模塊: from nltk.corpus import wordnet [as 別名]
# 或者: from nltk.corpus.wordnet import synsets [as 別名]
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 

# 需要導入模塊: from nltk.corpus import wordnet [as 別名]
# 或者: from nltk.corpus.wordnet import synsets [as 別名]
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) 

# 需要導入模塊: from nltk.corpus import wordnet [as 別名]
# 或者: from nltk.corpus.wordnet import synsets [as 別名]
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