def extract_story_elements():
min_head_vocab = 5
min_role_vocab = 4
min_tuples = 3
ATTRIBUTE = 0
AGENT_ROLE = 1
PATIENT_ROLE = 2
SURFACE_FORM = 3
parsed_dir = os.path.join(dirs.data_stanford_dir, 'parsed')
parsed_files = glob.glob(os.path.join(parsed_dir, '*.json'))
dependencies_file = os.path.join(dirs.data_stanford_dir, 'dependency_tuple_ids.json')
dependencies = fh.read_json(dependencies_file)
coref_file = os.path.join(dirs.data_stanford_dir, 'coref_heads.json')
coref_heads = fh.read_json(coref_file)
supersense_tags = fh.read_json(os.path.join(dirs.data_amalgram_dir, 'all_tags.json'))
heads = defaultdict(int)
tokens = defaultdict(int)
attributes = defaultdict(int)
agent_roles = defaultdict(int)
patient_roles = defaultdict(int)
story_elements = {}
print "Extracting story elements"
for f_i, f in enumerate(parsed_files):
sentences = fh.read_json(f)
basename = fh.get_basename_wo_ext(f)
element_list = extract_story_elements_from_article(sentences, dependencies[basename], coref_heads[basename], supersense_tags[basename], basename)
story_elements[basename] = element_list
for element in element_list:
for h in element.head_words:
heads[h] += 1
for t in element.attributes:
attributes[t] += 1
for t in element.agent_roles:
agent_roles[t] += 1
for t in element.patient_roles:
patient_roles[t] += 1
print "Finding most common tokens"
common_heads = [(v, k) for k, v in heads.items()]
common_heads.sort()
common_heads.reverse()
output_filename = os.path.join(dirs.lda_dir, 'common_heads.json')
fh.write_to_json(common_heads, output_filename, sort_keys=False)
"""
common_tokens = [(v, k) for k, v in tokens.items()]
common_tokens.sort()
common_tokens.reverse()
output_filename = os.path.join(dirs.lda_dir, 'common_tokens.json')
fh.write_to_json(common_tokens, output_filename, sort_keys=False)
"""
common_attributes = [(v, k) for k, v in attributes.items()]
common_attributes.sort()
common_attributes.reverse()
output_filename = os.path.join(dirs.lda_dir, 'common_attributes.json')
fh.write_to_json(common_attributes, output_filename, sort_keys=False)
common_agent_roles = [(v, k) for k, v in agent_roles.items()]
common_agent_roles.sort()
common_agent_roles.reverse()
output_filename = os.path.join(dirs.lda_dir, 'common_agent_roles.json')
fh.write_to_json(common_agent_roles, output_filename, sort_keys=False)
common_patient_roles = [(v, k) for k, v in patient_roles.items()]
common_patient_roles.sort()
common_patient_roles.reverse()
output_filename = os.path.join(dirs.lda_dir, 'common_patient_roles.json')
fh.write_to_json(common_patient_roles, output_filename, sort_keys=False)
print pronoun_list
#most_common_heads = {k: v for v, k in common_heads if v >= min_head_vocab and k not in pronoun_list}
most_common_attributes = {k: v for v, k in common_attributes if (v >= min_role_vocab and k not in pronoun_list)}
most_common_agent_roles = {k: v for v, k in common_agent_roles if (v >= min_role_vocab and k not in pronoun_list and k not in stopwords)}
most_common_patient_roles = {k: v for v, k in common_patient_roles if (v >= min_role_vocab and k not in pronoun_list and k not in stopwords)}
output_filename = os.path.join(dirs.lda_dir, 'most_common_attributes.json')
fh.write_to_json(most_common_attributes, output_filename, sort_keys=False)
output_filename = os.path.join(dirs.lda_dir, 'most_common_agent_roles.json')
fh.write_to_json(most_common_agent_roles, output_filename, sort_keys=False)
output_filename = os.path.join(dirs.lda_dir, 'most_common_patient_roles.json')
fh.write_to_json(most_common_patient_roles, output_filename, sort_keys=False)
print len(most_common_attributes)
print len(most_common_agent_roles)
print len(most_common_patient_roles)
print "Filtering tuples"
valid_elements = defaultdict(list)
for basename, element_list in story_elements.items():
for se in element_list:
se.valid_heads = [h for h in se.head_words if h not in pronoun_list]
se.valid_phrases = [h for h in se.phrases if h not in pronoun_list]
if len(se.valid_heads) > 0:
se.valid_attributes = [t for t in se.attributes if t in most_common_attributes]
se.valid_agent_roles = [t for t in se.agent_roles if t in most_common_agent_roles]
se.valid_patient_roles = [t for t in se.patient_roles if t in most_common_patient_roles]
se.tuples = [(ATTRIBUTE, t) for t in se.valid_attributes] + \
[(AGENT_ROLE, t) for t in se.valid_agent_roles] + \
[(PATIENT_ROLE, t) for t in se.valid_patient_roles]
#[(SURFACE_FORM, t) for t in se.valid_heads]
if len(se.tuples) >= min_tuples:
valid_elements[basename].append(se)
print "Constructing vocabulary"
n_tuples = 0
vocab = VocabWithCounts('', add_oov=False)
n_entities = 0
for basename, element_list in valid_elements.items():
for se in element_list:
tokens = [token for role, token in se.tuples]
vocab.add_tokens(tokens)
n_tuples += len(tokens)
n_entities += 1
head_word_vocab = VocabWithCounts('', add_oov=False)
for basename, element_list in valid_elements.items():
for se in element_list:
tokens = [token for token in se.valid_heads]
head_word_vocab.add_tokens(tokens)
head_phrase_vocab = VocabWithCounts('', add_oov=False)
for basename, element_list in valid_elements.items():
for se in element_list:
tokens = [token for token in se.valid_phrases]
head_phrase_vocab.add_tokens(tokens)
print "Building indices"
tuple_vocab = np.zeros(n_tuples, dtype=int) # vocab index of the ith word
tuple_entity = np.zeros(n_tuples, dtype=int)
tuple_role = []
entity_doc = np.zeros(n_entities, dtype=int) # topic of the ith word
docs = valid_elements.keys()
docs.sort()
vocab_counts = np.zeros(len(vocab), dtype=int)
article_mapping = []
entity_index = 0
head_word_vocab_list = []
head_word_entity_list = []
head_phrase_vocab_list = []
head_phrase_entity_list = []
t_i = 0
for d_i, d in enumerate(docs):
element_list = valid_elements[d]
for se in element_list:
entity_doc[entity_index] = d_i
for role, token in se.tuples:
tuple_entity[t_i] = entity_index
tuple_role.append(role)
vocab_index = vocab.get_index(token)
tuple_vocab[t_i] = vocab_index
vocab_counts[vocab_index] += 1
t_i += 1
for token in se.valid_heads:
head_word_vocab_index = head_word_vocab.get_index(token)
head_word_vocab_list.append(head_word_vocab_index)
head_word_entity_list.append(entity_index)
for token in se.valid_phrases:
head_phrase_vocab_index = head_phrase_vocab.get_index(token)
head_phrase_vocab_list.append(head_phrase_vocab_index)
head_phrase_entity_list.append(entity_index)
article_mapping.append(str(entity_index) + ':' + d + ':' + ','.join(se.head_words) + ':' + ','.join(se.valid_attributes) + ':' + ','.join(se.valid_agent_roles) + ':' + ','.join(se.valid_patient_roles))
entity_index += 1
print len(docs), "valid documents"
print entity_index, "entities"
print t_i, "tuples"
print len(vocab), "word types"
print np.min(vocab_counts), np.max(vocab_counts), np.sum(vocab_counts)
output_filename = os.path.join(dirs.lda_dir, 'tuple_vocab.json')
fh.write_to_json(list(tuple_vocab), output_filename, sort_keys=False)
output_filename = os.path.join(dirs.lda_dir, 'tuple_role.json')
fh.write_to_json(list(tuple_role), output_filename, sort_keys=False)
output_filename = os.path.join(dirs.lda_dir, 'tuple_entity.json')
fh.write_to_json(list(tuple_entity), output_filename, sort_keys=False)
output_filename = os.path.join(dirs.lda_dir, 'entity_doc.json')
fh.write_to_json(list(entity_doc), output_filename, sort_keys=False)
output_filename = os.path.join(dirs.lda_dir, 'vocab.json')
fh.write_to_json(vocab.index2token, output_filename, sort_keys=False)
output_filename = os.path.join(dirs.lda_dir, 'docs.json')
fh.write_to_json(list(docs), output_filename, sort_keys=False)
output_filename = os.path.join(dirs.lda_dir, 'article_map.json')
fh.write_to_json(list(article_mapping), output_filename, sort_keys=False)
output_filename = os.path.join(dirs.lda_dir, 'head_word_vocab.json')
fh.write_to_json(head_word_vocab.index2token, output_filename, sort_keys=False)
output_filename = os.path.join(dirs.lda_dir, 'head_phrase_vocab.json')
fh.write_to_json(head_phrase_vocab.index2token, output_filename, sort_keys=False)
output_filename = os.path.join(dirs.lda_dir, 'head_word_vocab_list.json')
fh.write_to_json(head_word_vocab_list, output_filename, sort_keys=False)
output_filename = os.path.join(dirs.lda_dir, 'head_word_entity_list.json')
fh.write_to_json(head_word_entity_list, output_filename, sort_keys=False)
output_filename = os.path.join(dirs.lda_dir, 'head_phrase_vocab_list.json')
fh.write_to_json(head_phrase_vocab_list, output_filename, sort_keys=False)
output_filename = os.path.join(dirs.lda_dir, 'head_phrase_entity_list.json')
fh.write_to_json(head_phrase_entity_list, output_filename, sort_keys=False)
def get_bamman_entities(all_trees, clustered_entity_indices, word2vec_file=None, min_role_vocab=4, min_tuples=3):
ATTRIBUTE = 0
AGENT_ROLE = 1
PATIENT_ROLE = 2
SURFACE_FORM = 3
tokens = defaultdict(int)
heads = defaultdict(int)
attributes = defaultdict(int)
agent_roles = defaultdict(int)
patient_roles = defaultdict(int)
story_elements = {}
for basename, trees in all_trees.items():
story_elements[basename] = []
article_clusters = clustered_entity_indices[basename]
# go through each entity, represented by a list of tree/node locations
for c_i, cluster_indices in enumerate(article_clusters):
# create an entity for each cluster in this document
entity = BammanEntity(basename)
# for each appearance, create an appearance object for this entity
for t_i, n_i in cluster_indices:
word = trees[t_i].node_dict[n_i].word
compound_word = get_compound_noun(trees[t_i], n_i)
mention_attributes = get_attributes(trees[t_i], n_i)
mention_agent_roles = get_agent_roles(trees[t_i], n_i)
mention_patient_roles = get_patient_roles(trees[t_i], n_i)
appearance = BammanEntityAppearance(t_i, n_i, word, mention_attributes, mention_agent_roles, mention_patient_roles, compound_word)
entity.add_appearance(appearance)
# count the total mentions of these words to build vocabularies
heads[word] += 1
for t in mention_attributes:
attributes[t[0]] += 1
for t in mention_agent_roles:
agent_roles[t[0]] += 1
for t in mention_patient_roles:
patient_roles[t[0]] += 1
# add the newly created entity to a dict
story_elements[basename].append(entity)
print "Finding most common tokens"
common_heads = [(v, k) for k, v in heads.items()]
common_heads.sort()
common_heads.reverse()
output_filename = os.path.join(dirs.persona_dir, 'common_heads.json')
fh.write_to_json(common_heads, output_filename, sort_keys=False)
common_attributes = [(v, k) for k, v in attributes.items()]
common_attributes.sort()
common_attributes.reverse()
output_filename = os.path.join(dirs.persona_dir, 'common_attributes.json')
fh.write_to_json(common_attributes, output_filename, sort_keys=False)
common_agent_roles = [(v, k) for k, v in agent_roles.items()]
common_agent_roles.sort()
common_agent_roles.reverse()
output_filename = os.path.join(dirs.persona_dir, 'common_agent_roles.json')
fh.write_to_json(common_agent_roles, output_filename, sort_keys=False)
common_patient_roles = [(v, k) for k, v in patient_roles.items()]
common_patient_roles.sort()
common_patient_roles.reverse()
output_filename = os.path.join(dirs.persona_dir, 'common_patient_roles.json')
fh.write_to_json(common_patient_roles, output_filename, sort_keys=False)
# filter vocabularies based on frequency and stopwords
most_common_attributes = {k: v for v, k in common_attributes if (v >= min_role_vocab and k not in pronoun_list and k not in stopwords)}
most_common_agent_roles = {k: v for v, k in common_agent_roles if (v >= min_role_vocab and k not in pronoun_list and k not in stopwords)}
most_common_patient_roles = {k: v for v, k in common_patient_roles if (v >= min_role_vocab and k not in pronoun_list and k not in stopwords)}
# save these vocabularies
output_filename = os.path.join(dirs.persona_dir, 'most_common_attributes.json')
fh.write_to_json(most_common_attributes, output_filename, sort_keys=False)
output_filename = os.path.join(dirs.persona_dir, 'most_common_agent_roles.json')
fh.write_to_json(most_common_agent_roles, output_filename, sort_keys=False)
output_filename = os.path.join(dirs.persona_dir, 'most_common_patient_roles.json')
fh.write_to_json(most_common_patient_roles, output_filename, sort_keys=False)
print len(most_common_attributes)
print len(most_common_agent_roles)
print len(most_common_patient_roles)
print "Filtering tuples"
filtered_indices = {}
valid_elements = defaultdict(list)
for basename, entity_list in story_elements.items():
#filtered_indices[basename] = []
for e_index, entity in enumerate(entity_list):
appearances = entity.get_appearances()
valid_heads = []
for ap in appearances:
if ap.head_word not in pronoun_list:
valid_heads.append(ap.head_word)
ap.valid_heads = [ap.head_word]
ap.valid_compound_heads = [ap.compound_word]
else:
ap.valid_heads = []
ap.valid_compound_heads = []
if len(valid_heads) > 0:
for ap in appearances:
ap.valid_attributes = [t for t in ap.attributes if t[0] in most_common_attributes]
ap.valid_agent_roles = [t for t in ap.agent_roles if t[0] in most_common_agent_roles]
ap.valid_patient_roles = [t for t in ap.patient_roles if t[0] in most_common_patient_roles]
ap.tuples = [(ATTRIBUTE, t[0], t[1], t[2], t[3]) for t in ap.valid_attributes] + \
[(AGENT_ROLE, t[0], t[1], t[2], t[3]) for t in ap.valid_agent_roles] + \
[(PATIENT_ROLE, t[0], t[1], t[2], t[3]) for t in ap.valid_patient_roles]
if entity.get_n_tuples() >= min_tuples:
valid_elements[basename].append(entity)
#filtered_indices[basename].append(clustered_entity_indices[basename][se_index])
print "Constructing vocabulary"
n_tuples = 0
vocab = VocabWithCounts('', add_oov=False)
n_entities = 0
n_mentions = 0
for basename, element_list in valid_elements.items():
for se in element_list:
for appearance in se.appearances:
tokens = [token for role, token, relation, pos, tuple_token_index in appearance.tuples]
vocab.add_tokens(tokens)
n_tuples += len(tokens)
if len(appearance.tuples) > 0:
n_mentions += 1
n_entities += 1
head_word_vocab = VocabWithCounts('', add_oov=False)
for basename, element_list in valid_elements.items():
for se in element_list:
for appearance in se.appearances:
tokens = [token for token in appearance.valid_heads]
head_word_vocab.add_tokens(tokens)
head_phrase_vocab = VocabWithCounts('', add_oov=False)
for basename, element_list in valid_elements.items():
for se in element_list:
for appearance in se.appearances:
tokens = [token for token in appearance.valid_compound_heads]
head_phrase_vocab.add_tokens(tokens)
print "Building indices"
tuple_vocab = np.zeros(n_tuples, dtype=int) # vocab index of the ith word
tuple_entity = np.zeros(n_tuples, dtype=int)
tuple_role = []
mention_entity = np.zeros(n_mentions, dtype=int)
tuple_mention = np.zeros(n_tuples, dtype=int)
entity_doc = np.zeros(n_entities, dtype=int) # topic of the ith word
docs = valid_elements.keys()
docs.sort()
"""
vocab_vectors = None
if word2vec_file is not None:
import gensim
dx = 300
vocab_vectors = np.zeros((len(vocab), dx))
# load pre-trained word vectors
print "Loading pre-trained word vectors"
all_vectors = gensim.models.Word2Vec.load_word2vec_format(word2vec_file, binary=True)
word2vec_vocab = set()
for v in vocab.get_all_tokens():
v_i = vocab.get_index(v)
if v in all_vectors:
vocab_vectors[v_i, :] = all_vectors[v]
word2vec_vocab.add(v)
else:
vocab_vectors[v_i, :] = 0.05 * np.random.uniform(-1.0, 1.0, (1, dx))
print len(list(set(vocab.get_all_tokens()) - word2vec_vocab)), "words in training vocabulary with no word2vec vector"
"""
vocab_counts = np.zeros(len(vocab), dtype=int)
entity_appearances = {}
entity_index = 0
mention_index = 0
head_word_vocab_list = []
head_word_entity_list = []
head_phrase_vocab_list = []
head_phrase_entity_list = []
entity_text_mentions = {}
t_i = 0
for d_i, d in enumerate(docs):
print d
basename = os.path.basename(d)
entity_appearances[basename] = {}
element_list = valid_elements[d]
entity_text_mentions[d] = {}
for se in element_list:
entity_text_mentions[d][entity_index] = {'sent_indices': [], 'token_indices': [], 'roles': []}
entity_doc[entity_index] = d_i
for appearance in se.appearances:
entity_text_mentions[d][entity_index]['sent_indices'].append(appearance.tree_index)
entity_text_mentions[d][entity_index]['token_indices'].append(appearance.token_index)
for role, token, relation, pos, tuple_token_index in appearance.tuples:
tuple_entity[t_i] = entity_index
tuple_mention[t_i] = mention_index
tuple_role.append(role)
vocab_index = vocab.get_index(token)
tuple_vocab[t_i] = vocab_index
vocab_counts[vocab_index] += 1
t_i += 1
entity_text_mentions[d][entity_index]['roles'].append((role, token, appearance.tree_index, tuple_token_index))
for token in appearance.valid_heads:
head_word_vocab_index = head_word_vocab.get_index(token)
head_word_vocab_list.append(head_word_vocab_index)
head_word_entity_list.append(entity_index)
for token in appearance.valid_compound_heads:
head_phrase_vocab_index = head_phrase_vocab.get_index(token)
head_phrase_vocab_list.append(head_phrase_vocab_index)
head_phrase_entity_list.append(entity_index)
# keep track of which document / sentences this entity appears in
s_i = appearance.tree_index
if s_i in entity_appearances[basename]:
entity_appearances[basename][s_i].append(entity_index)
else:
entity_appearances[basename][s_i] = [entity_index]
if len(appearance.tuples):
mention_entity[mention_index] = entity_index
mention_index += 1
entity_index += 1
# as initial testing for Gaussian LDA, export a small vector for each tuple
"""
tuple_vectors = None
if word2vec_file is not None:
vec_size = 10
tuple_vectors = np.zeros([n_tuples, vec_size])
for v_i, v in enumerate(tuple_vocab):
tuple_vectors[v_i, :] = vocab_vectors[v, :vec_size]
"""
# export network data
rnn_data = []
t_i = 0
entity_index = 0
mention_index = 0
for d_i, d in enumerate(docs):
element_list = valid_elements[d]
for entity in element_list:
appearance_list = []
for appearance in entity.appearances:
tuple_list = []
head_word = appearance.head_word
head_phrase = appearance.compound_word
for role, token, relation, pos, tuple_token_index in appearance.tuples:
tuple_list.append((t_i, role, token, relation, head_word, pos, head_phrase))
t_i += 1
if len(tuple_list) > 0:
appearance_list.append(tuple_list)
rnn_data.append([d_i, entity_index, appearance_list])
entity_index += 1
output_filename = os.path.join(dirs.persona_dir, 'rnn_data.json')
fh.write_to_json(rnn_data, output_filename, sort_keys=False)
print len(docs), "valid documents"
print entity_index, "entities"
print t_i, "tuples"
print len(vocab), "word types"
print np.min(vocab_counts), np.max(vocab_counts), np.sum(vocab_counts)
output_filename = os.path.join(dirs.persona_dir, 'tuple_vocab.json')
fh.write_to_json(list(tuple_vocab), output_filename, sort_keys=False)
output_filename = os.path.join(dirs.persona_dir, 'tuple_role.json')
fh.write_to_json(list(tuple_role), output_filename, sort_keys=False)
output_filename = os.path.join(dirs.persona_dir, 'tuple_entity.json')
fh.write_to_json(list(tuple_entity), output_filename, sort_keys=False)
output_filename = os.path.join(dirs.persona_dir, 'tuple_mention.json')
fh.write_to_json(list(tuple_mention), output_filename, sort_keys=False)
output_filename = os.path.join(dirs.persona_dir, 'mention_entity.json')
fh.write_to_json(list(mention_entity), output_filename, sort_keys=False)
output_filename = os.path.join(dirs.persona_dir, 'entity_doc.json')
fh.write_to_json(list(entity_doc), output_filename, sort_keys=False)
output_filename = os.path.join(dirs.persona_dir, 'vocab.json')
fh.write_to_json(vocab.index2token, output_filename, sort_keys=False)
output_filename = os.path.join(dirs.persona_dir, 'docs.json')
fh.write_to_json(list(docs), output_filename, sort_keys=False)
#output_filename = os.path.join(dirs.persona_dir, 'article_map.json')
#fh.write_to_json(list(article_mapping), output_filename, sort_keys=False)
output_filename = os.path.join(dirs.persona_dir, 'head_word_vocab.json')
fh.write_to_json(head_word_vocab.index2token, output_filename, sort_keys=False)
output_filename = os.path.join(dirs.persona_dir, 'head_word_vocab_list.json')
fh.write_to_json(head_word_vocab_list, output_filename, sort_keys=False)
output_filename = os.path.join(dirs.persona_dir, 'head_word_entity_list.json')
fh.write_to_json(head_word_entity_list, output_filename, sort_keys=False)
output_filename = os.path.join(dirs.persona_dir, 'entity_appearances.json')
fh.write_to_json(entity_appearances, output_filename, sort_keys=False)
#if tuple_vectors is not None:
# output_filename = os.path.join(dirs.persona_dir, 'tuple_vectors.json')
# fh.write_to_json(tuple_vectors.tolist(), output_filename, sort_keys=False)
output_filename = os.path.join(dirs.persona_dir, 'head_phrase_vocab.json')
fh.write_to_json(head_phrase_vocab.index2token, output_filename, sort_keys=False)
output_filename = os.path.join(dirs.persona_dir, 'head_phrase_vocab_list.json')
fh.write_to_json(head_phrase_vocab_list, output_filename, sort_keys=False)
output_filename = os.path.join(dirs.persona_dir, 'head_phrase_entity_list.json')
fh.write_to_json(head_phrase_entity_list, output_filename, sort_keys=False)
output_filename = os.path.join(dirs.persona_dir, 'entity_text_mentions.json')
fh.write_to_json(entity_text_mentions, output_filename, sort_keys=False)
return filtered_indices, valid_elements
