def extract(sentence):
mentions = []
# Skip the sentence if there are no English words in the sentence
no_english_words = True
for word in sentence.words:
if len(word.word) > 2 and \
(word.word in english_dict or
word.word.casefold() in english_dict):
no_english_words = False
break
if no_english_words:
return [] # Stop iteration
sentence_is_upper = False
if " ".join([x.word for x in sentence.words]).isupper():
sentence_is_upper = True
# The following set keeps a list of indexes we already looked at and which
# contained a mention
history = set()
words = sentence.words
# Scan all subsequences of the sentence of length up to max_mention_length
for start, end in get_all_phrases_in_sentence(sentence,
max_mention_length):
if start in history or end in history:
continue
phrase = " ".join([word.word for word in words[start:end]])
if sentence_is_upper: # This may not be a great idea...
phrase = phrase.casefold()
mention = None
# If the phrase is a hpoterm name containing a gene, then it is a
# mention candidate to supervise as negative
if phrase in hpoterms_with_gene:
mention = Mention("GENE_SUP_HPO", phrase, words[start:end])
add_features(mention, sentence)
mention.is_correct = False
mentions.append(mention)
for i in range(start, end):
history.add(i)
# If the phrase is in the gene dictionary, then is a mention candidate
if len(phrase) > 1 and phrase in merged_genes_dict:
# The entity is a list of all the main symbols that could have the
# phrase as symbol. They're separated by "|".
mention = Mention("GENE",
"|".join(merged_genes_dict[phrase]),
words[start:end])
# Add features to the candidate
add_features(mention, sentence)
# Add mention to the list
mentions.append(mention)
# Add indexes to history so that they are not used for another
# mention
for i in range(start, end):
history.add(i)
return mentions
def extract(sentence):
mentions = []
# Skip the sentence if there are no English words in the sentence
no_english_words = True
for word in sentence.words:
if len(word.word) > 2 and \
(word.word in english_dict or
word.word.casefold() in english_dict):
no_english_words = False
break
if no_english_words:
return [] # Stop iteration
sentence_is_upper = False
if " ".join([x.word for x in sentence.words]).isupper():
sentence_is_upper = True
# The following set keeps a list of indexes we already looked at and which
# contained a mention
history = set()
words = sentence.words
# Scan all subsequences of the sentence of length up to max_mention_length
for start, end in get_all_phrases_in_sentence(sentence,
max_mention_length):
if start in history or end in history:
continue
phrase = " ".join([word.word for word in words[start:end]])
if sentence_is_upper: # This may not be a great idea...
phrase = phrase.casefold()
mention = None
# If the phrase is a hpoterm name containing a gene, then it is a
# mention candidate to supervise as negative
if phrase in hpoterms_with_gene:
mention = Mention("GENE_SUP_HPO", phrase, words[start:end])
add_features(mention, sentence)
mention.is_correct = False
mentions.append(mention)
for i in range(start, end):
history.add(i)
# If the phrase is in the gene dictionary, then is a mention candidate
if len(phrase) > 1 and phrase in merged_genes_dict:
# The entity is a list of all the main symbols that could have the
# phrase as symbol. They're separated by "|".
mention = Mention("GENE", "|".join(merged_genes_dict[phrase]),
words[start:end])
# Add features to the candidate
add_features(mention, sentence)
# Add mention to the list
mentions.append(mention)
# Add indexes to history so that they are not used for another
# mention
for i in range(start, end):
history.add(i)
return mentions
def extract(sentence):
mentions = []
mention_ids = set()
# If there are no English words in the sentence, we skip it.
no_english_words = True
for word in sentence.words:
word.stem = stemmer.stem(word.word) # Here so all words have stem
if len(word.word) > 2 and \
(word.word in english_dict or
word.word.casefold() in english_dict):
no_english_words = False
if no_english_words:
return mentions
history = set()
# Iterate over each phrase of length at most max_mention_length
for start, end in get_all_phrases_in_sentence(sentence,
max_mention_length):
if start in history or end - 1 in history:
continue
phrase = " ".join([word.word for word in sentence.words[start:end]])
# If the phrase is a gene long name containing a phenotype name, create
# a candidate that we supervise as negative
if len(phrase) > 1 and phrase in genes_with_hpoterm:
mention = Mention("HPOTERM_SUP_GENEL", phrase,
sentence.words[start:end])
mention.is_correct = False
add_features(mention, sentence)
mentions.append(mention)
for word in sentence.words[start:end]:
history.add(word.in_sent_idx)
continue
# Iterate over each phrase of length at most max_mention_length
for start, end in get_all_phrases_in_sentence(sentence,
max_mention_length):
should_continue = False
for i in range(start, end):
if i in history:
should_continue = True
break
if should_continue:
continue
phrase = " ".join([word.word for word in sentence.words[start:end]])
# The list of stems in the phrase (not from stopwords or symbols, and
# not already used for a mention)
phrase_stems = []
for word in sentence.words[start:end]:
if not re.match("^(_|\W)+$", word.word) and \
(len(word.word) == 1 or
word.lemma.casefold() not in stopwords_dict):
phrase_stems.append(word.stem)
phrase_stems_set = frozenset(phrase_stems)
if phrase_stems_set in hpoterms_dict:
# Find the word objects of that match
mention_words = []
mention_lemmas = []
mention_stems = []
for word in sentence.words[start:end]:
if word.stem in phrase_stems_set and \
word.lemma.casefold() not in mention_lemmas and \
word.stem not in mention_stems:
mention_lemmas.append(word.lemma.casefold())
mention_words.append(word)
mention_stems.append(word.stem)
if len(mention_words) == len(phrase_stems_set):
break
entity = list(hpoterms_dict[phrase_stems_set])[0]
mention = Mention("HPOTERM",
hponames_to_ids[entity] + "|" + entity,
mention_words)
# The following is a way to avoid duplicates.
# It's ugly and not perfect
if mention.id() in mention_ids:
continue
mention_ids.add(mention.id())
# Features
add_features(mention, sentence)
mentions.append(mention)
for word in mention_words:
history.add(word.in_sent_idx)
# Generate some negative candidates at random, if this sentences didn't
# contain any other candidate. We want the candidates to be nouns.
if len(mentions) == 0 and random.random() <= NEG_PROB:
index = random.randint(0, len(sentence.words) - 1)
# We may not get a noun at random, so we try again if we don't.
tries = 10
while not sentence.words[index].pos.startswith("NN") and tries > 0:
index = random.randint(0, len(sentence.words) - 1)
tries -= 1
if sentence.words[index].pos.startswith("NN"):
mention = Mention("HPOTERM_SUP_rand",
sentence.words[index].lemma.casefold(),
sentence.words[index:index + 1])
mention.is_correct = False
add_features(mention, sentence)
mentions.append(mention)
return mentions
def extract(sentence):
mentions = []
mention_ids = set()
# If there are no English words in the sentence, we skip it.
no_english_words = True
for word in sentence.words:
word.stem = stemmer.stem(word.word) # Here so all words have stem
if len(word.word) > 2 and \
(word.word in english_dict or
word.word.casefold() in english_dict):
no_english_words = False
if no_english_words:
return mentions
history = set()
# Iterate over each phrase of length at most max_mention_length
for start, end in get_all_phrases_in_sentence(sentence,
max_mention_length):
if start in history or end - 1 in history:
continue
phrase = " ".join([word.word for word in sentence.words[start:end]])
# If the phrase is a gene long name containing a phenotype name, create
# a candidate that we supervise as negative
if len(phrase) > 1 and phrase in genes_with_hpoterm:
mention = Mention("HPOTERM_SUP_GENEL",
phrase,
sentence.words[start:end])
mention.is_correct = False
add_features(mention, sentence)
mentions.append(mention)
for word in sentence.words[start:end]:
history.add(word.in_sent_idx)
continue
# Iterate over each phrase of length at most max_mention_length
for start, end in get_all_phrases_in_sentence(sentence,
max_mention_length):
should_continue = False
for i in range(start, end):
if i in history:
should_continue = True
break
if should_continue:
continue
phrase = " ".join([word.word for word in sentence.words[start:end]])
# The list of stems in the phrase (not from stopwords or symbols, and
# not already used for a mention)
phrase_stems = []
for word in sentence.words[start:end]:
if not re.match("^(_|\W)+$", word.word) and \
(len(word.word) == 1 or
word.lemma.casefold() not in stopwords_dict):
phrase_stems.append(word.stem)
phrase_stems_set = frozenset(phrase_stems)
if phrase_stems_set in hpoterms_dict:
# Find the word objects of that match
mention_words = []
mention_lemmas = []
mention_stems = []
for word in sentence.words[start:end]:
if word.stem in phrase_stems_set and \
word.lemma.casefold() not in mention_lemmas and \
word.stem not in mention_stems:
mention_lemmas.append(word.lemma.casefold())
mention_words.append(word)
mention_stems.append(word.stem)
if len(mention_words) == len(phrase_stems_set):
break
entity = list(hpoterms_dict[phrase_stems_set])[0]
mention = Mention(
"HPOTERM", hponames_to_ids[entity] + "|" + entity,
mention_words)
# The following is a way to avoid duplicates.
# It's ugly and not perfect
if mention.id() in mention_ids:
continue
mention_ids.add(mention.id())
# Features
add_features(mention, sentence)
mentions.append(mention)
for word in mention_words:
history.add(word.in_sent_idx)
# Generate some negative candidates at random, if this sentences didn't
# contain any other candidate. We want the candidates to be nouns.
if len(mentions) == 0 and random.random() <= NEG_PROB:
index = random.randint(0, len(sentence.words) - 1)
# We may not get a noun at random, so we try again if we don't.
tries = 10
while not sentence.words[index].pos.startswith("NN") and tries > 0:
index = random.randint(0, len(sentence.words) - 1)
tries -= 1
if sentence.words[index].pos.startswith("NN"):
mention = Mention(
"HPOTERM_SUP_rand", sentence.words[index].lemma.casefold(),
sentence.words[index:index+1])
mention.is_correct = False
add_features(mention, sentence)
mentions.append(mention)
return mentions
