Python Model.update Examples

Example #1

File: show_top_verbs.py Project: lukuang/keyword_extraction

def get_verbs(tuple_result):
    verbs = {}
    for identifier in tuple_result:
        verb_model = Model(True)
        for single_tuple in tuple_result[identifier]:
            verb = single_tuple['verb']
            if verb not in NO_NEED:
                verb_model.update(text_list=[verb])
        verb_model.normalize()
        for verb in verb_model.model:
            if verb not in verbs:
                verbs[verb] = 0
            verbs[verb] += verb_model.model[verb]
    return verbs

Example #2

File: get_result_lm.py Project: lukuang/2016-rts

def get_models(results,index_dir):
    models = {}
    for qid in results:
        if qid not in models:
            models[qid] = {}
        for day in results[qid]:
            single_model = Model(True,need_stem=True)
            for tid in results[qid][day]:
                text = get_text(index_dir,tid)
                if text:
                    single_model.update(text_string=text)
            single_model.normalize()     
            models[qid][day] = single_model.model
    
    return models

Example #3

def get_all_verbs(example_result_tuples):
    verb_model = Model(True, need_stem=True)

    for single_tuple in example_result_tuples:
        word = single_tuple['verb']
        if single_tuple['verb_label'] != 'VB':
            word = WordNetLemmatizer().lemmatize(word, 'v')
        try:
            verb_model.update(text_list=[str(word)])
        except TypeError:
            print "Wrong Word!"
            print word
            print type(word)
            print single_tuple
            sys.exit(0)
    verb_model.normalize()

    return verb_model

Example #4

File: get_feature_for_verb_and_prep.py Project: lukuang/keyword_extraction

def get_all_verbs(result_tuples):
    verb_model = Model(False,need_stem=False)

    for single_tuple in result_tuples:
        word = single_tuple['verb']
        # if single_tuple['verb_label'] != 'VB':
        #     word = WordNetLemmatizer().lemmatize(word,'v')
        try:
            verb_model.update(text_list=[str(word)])
        except TypeError:
            print "Wrong Word!"
            print word
            print type(word)
            print single_tuple
            sys.exit(0)
    verb_model.to_dirichlet()

    return verb_model

Example #5

File: gene_features_for_verbs.py Project: lukuang/keyword_extraction

def get_all_verbs(example_result_tuples):
    verb_model = Model(True,need_stem=True)

    for single_tuple in example_result_tuples:
        word = single_tuple['verb']
        if single_tuple['verb_label'] != 'VB':
            word = WordNetLemmatizer().lemmatize(word,'v')
        try:
            verb_model.update(text_list=[str(word)])
        except TypeError:
            print "Wrong Word!"
            print word
            print type(word)
            print single_tuple
            sys.exit(0)
    verb_model.normalize()

    return verb_model

Example #6

def get_all_verbs(result_tuples):
    verb_model = Model(False, need_stem=False)

    for single_tuple in result_tuples:
        word = single_tuple['verb']
        # if single_tuple['verb_label'] != 'VB':
        #     word = WordNetLemmatizer().lemmatize(word,'v')
        try:
            verb_model.update(text_list=[str(word)])
        except TypeError:
            print "Wrong Word!"
            print word
            print type(word)
            print single_tuple
            sys.exit(0)
    verb_model.to_dirichlet()

    return verb_model