def test_tokens_between_spans(self): span1 = dd.Span(0, 2) span2 = dd.Span(3, 5) words_between = dd.tokens_between_spans(self.words, span1, span2) self.assertEqual(words_between[:], (False, ["Jake"])) words_between = dd.tokens_between_spans(self.words, span2, span1) self.assertEqual(words_between[:], (True, ["Jake"])) words_between = dd.tokens_between_spans(self.words, span1, span1) self.assertEqual(words_between[:], (False, []))
def test_tokens_between_spans(self):
span1 = dd.Span(0, 2)
span2 = dd.Span(3, 5)
words_between = dd.tokens_between_spans(self.words, span1, span2)
self.assertEqual(words_between[:], (False, ["Jake"]))
words_between = dd.tokens_between_spans(self.words, span2, span1)
self.assertEqual(words_between[:], (True, ["Jake"]))
words_between = dd.tokens_between_spans(self.words, span1, span1)
self.assertEqual(words_between[:], (False, []))
def test_tokens_between_spans(self): span1 = dd.Span(0, 2) span2 = dd.Span(3, 5) words_between = dd.tokens_between_spans(self.words, span1, span2) self.assertEqual([words_between[0], list(words_between[1])], [False, ["Jake"]]) words_between = dd.tokens_between_spans(self.words, span2, span1) self.assertEqual([words_between[0], list(words_between[1])], [True, ["Jake"]]) words_between = dd.tokens_between_spans(self.words, span1, span1) self.assertEqual([words_between[0], list(words_between[1])], [False, []])
def test_tokens_between_spans(self):
span1 = dd.Span(0, 2)
span2 = dd.Span(3, 5)
words_between = dd.tokens_between_spans(self.words, span1, span2)
self.assertEqual(
[words_between[0], list(words_between[1])], [False, ["Jake"]])
words_between = dd.tokens_between_spans(self.words, span2, span1)
self.assertEqual(
[words_between[0], list(words_between[1])], [True, ["Jake"]])
words_between = dd.tokens_between_spans(self.words, span1, span1)
self.assertEqual(
[words_between[0], list(words_between[1])], [False, []])
# Get all fields from a row
words = parts[0].split(ARR_DELIM)
relation_id = parts[1]
p2_text = parts[2]
p1_start, p1_length, p2_start, p2_length = [int(x) for x in parts[3:]]
# Unpack input into tuples.
span1 = ddlib.Span(begin_word_id=p1_start, length=p1_length)
span2 = ddlib.Span(begin_word_id=p2_start, length=p2_length)
# Features for this pair come in here
features = set()
# Feature 1: Bag of words between the two phrases
words_between = ddlib.tokens_between_spans(words, span1, span2)
count = 1
for word in words_between.elements:
if count < nbWordsBetweenPeopleCompanyConsidered:
features.add("word_between=" + word)
count +=1
# Feature 2: Number of words between the two phrases
features.add("num_words_between=%s" % len(words_between.elements))
# Feature 3: Is the last name of the founder included in the name of the company?
last_word_left = ddlib.materialize_span(words, span1)[-1]
if (last_word_left in p2_text):
features.add("potential_last_name_match")
import sys, json
import ddlib # DeepDive python utility
# For each input tuple
for row in sys.stdin:
obj = json.loads(row)
words = obj["words"]
# Unpack input into tuples.
span1 = ddlib.Span(begin_word_id=obj['p1_start'], length=obj['p1_length'])
span2 = ddlib.Span(begin_word_id=obj['p2_start'], length=obj['p2_length'])
# Features for this pair come in here
features = set()
# Feature 1: Bag of words between the two phrases
words_between = ddlib.tokens_between_spans(words, span1, span2)
for word in words_between.elements:
features.add("word_between=" + word)
# Feature 2: Number of words between the two phrases
features.add("num_words_between=%s" % len(words_between.elements))
# Feature 3: Does the last word (last name) match?
last_word_left = ddlib.materialize_span(words, span1)[-1]
last_word_right = ddlib.materialize_span(words, span2)[-1]
if (last_word_left == last_word_right):
features.add("potential_last_name_match")
########################
# Improved Feature Set #
########################
# sample json
for row in sys.stdin:
# Unpack input into tuples.
#
obj = json.loads(row)
words, lemmas = obj["words"], obj["lemma"]
span1 = ddlib.Span(begin_word_id=obj['p1.start_position'], length=obj['p1.length'])
span2 = ddlib.Span(begin_word_id=obj['p2.start_position'], length=obj['p2.length'])
features = set()
# Feature 1: Find out if a lemma of marry occurs.
# A better feature would ensure this is on the dependency path between the two.
#
lemma_between = ddlib.tokens_between_spans(lemmas, span1, span2)
married_words = ('marry', 'widow')
for lemma in lemma_between.elements:
if lemma in married_words:
features.add("important_word=%s" % lemma)
# Feature 2: The number of words between the two phrases.
# Intuition: if they are close by, the link may be stronger.
#
words_between = ddlib.tokens_between_spans(words, span1, span2)
l = len(words_between.elements)
features.add("num_words_between=%s" % l if l<5 else "many_words_between")
# Feature 3: Check if the last name matches heuristically.
#
last_word_left = ddlib.materialize_span(words, span1)[-1]
# sample json
for row in sys.stdin:
# Unpack input into tuples.
#
obj = json.loads(row)
words, lemmas = obj["words"], obj["lemma"]
span1 = ddlib.Span(begin_word_id=obj['p1.start_position'], length=obj['p1.length'])
span2 = ddlib.Span(begin_word_id=obj['p2.start_position'], length=obj['p2.length'])
features = set()
# Feature 1: Find out if a lemma of marry occurs.
# A better feature would ensure this is on the dependency path between the two.
#
lemma_between = ddlib.tokens_between_spans(lemmas, span1, span2)
married_words = ('marry', 'widow')
for lemma in lemma_between.elements:
if lemma in married_words:
features.add("important_word=%s" % lemma)
# Feature 2: The number of words between the two phrases.
# Intuition: if they are close by, the link may be stronger.
#
words_between = ddlib.tokens_between_spans(words, span1, span2)
l = len(words_between.elements)
features.add("num_words_between=%s" % l if l<5 else "many_words_between")
# Feature 3: Check if the last name matches heuristically.
#
last_word_left = ddlib.materialize_span(words, span1)[-1]