def _compute_score(self, raw_tokens, ans_eng):
term_count = 0
related = []
causal_match = False
position = []
for term, synsets in ans_eng.ir_query_tagged:
match = False
term_related = []
for i, page_term in enumerate(indexer.regularize(raw_tokens)):
page_term_related = ans_eng.related_values(synsets, page_term)
if page_term_related:
term_related.append((max(page_term_related), i))
if term == page_term or max(
page_term_related) >= ans_eng.lch:
match = True
if match: # above LCH value
term_count += 1
if term == 'cause':
causal_match = True
if term_related:
term_related.sort(key=lambda tup: tup[0])
term_related, i = term_related[-1] # maximum value
related.append(term_related)
position.append(i)
return term_count, related, causal_match, position
def regularize_text(self):
if not self.paragraphs:
self.tokenize_sentences()
for i, para in enumerate(self.paragraphs):
for j, sent in enumerate(para.sentence_tokens):
self.paragraphs[i].sentence_tokens[j] = regularize(sent)
# Remove empty sentences
self.paragraphs[i].sentence_tokens = [
x for x in self.paragraphs[i].sentence_tokens if x
]
def regularize_text(self):
"""Regularizes all tokens for each sentence in each paragraph."""
if not self.paragraphs:
self.tokenize_sentences()
for i, para in enumerate(self.paragraphs):
for j, sent in enumerate(para.sentence_tokens):
self.paragraphs[i].sentence_tokens[j] = regularize(sent)
# Remove empty sentences
self.paragraphs[i].sentence_tokens = [x for x in self.
paragraphs[i].sentence_tokens
if x]
def _compute_score(self, raw_tokens, ans_eng):
"""Compute various score components from the answer text.
Args:
raw_tokens: List of token strings, such as from a sentence.
ans_eng: The AnswerEngine object that was used to generate
this Answer object.
Returns:
term_count: Integer number of how many query-terms had a
matching answer-term with a semantic relatedness (LCH)
value above the threshold specified in the ans_eng.
related: List of LCH similarity values (semantic relatedness),
representing the maximum LCH value for each query-term
when evaluated with each answer-term.
causal_match: Boolean value representing whether or not the
(usually hidden) causal term had a match (LCH greater
than the threshold in ans_eng).
position: List of token indexes for each query term,
representing the location in the raw_tokens that had the
maximal semantic relatedness (LCH) for each query term.
"""
term_count = 0
related = []
causal_match = False
position = []
for term, synsets in ans_eng.ir_query_tagged:
match = False
term_related = []
for i, page_term in enumerate(indexer.regularize(raw_tokens)):
page_term_related = ans_eng.related_values(synsets, page_term)
if page_term_related:
term_related.append((max(page_term_related), i))
if term == page_term or max(
page_term_related) >= ans_eng.lch:
match = True
if match: # above LCH value
term_count += 1
if term == 'cause':
causal_match = True
if term_related:
term_related.sort(key=lambda tup: tup[0])
term_related, i = term_related[-1] # maximum value
related.append(term_related)
position.append(i)
return term_count, related, causal_match, position
def _compute_score(self, raw_tokens, ans_eng):
"""Compute various score components from the answer text.
Args:
raw_tokens: List of token strings, such as from a sentence.
ans_eng: The AnswerEngine object that was used to generate
this Answer object.
Returns:
term_count: Integer number of how many query-terms had a
matching answer-term with a semantic relatedness (LCH)
value above the threshold specified in the ans_eng.
related: List of LCH similarity values (semantic relatedness),
representing the maximum LCH value for each query-term
when evaluated with each answer-term.
causal_match: Boolean value representing whether or not the
(usually hidden) causal term had a match (LCH greater
than the threshold in ans_eng).
position: List of token indexes for each query term,
representing the location in the raw_tokens that had the
maximal semantic relatedness (LCH) for each query term.
"""
term_count = 0
related = []
causal_match = False
position = []
for term, synsets in ans_eng.ir_query_tagged:
match = False
term_related = []
for i, page_term in enumerate(indexer.regularize(raw_tokens)):
page_term_related = ans_eng.related_values(synsets, page_term)
if page_term_related:
term_related.append((max(page_term_related), i))
if term == page_term or max(page_term_related) >= ans_eng.lch:
match = True
if match: # above LCH value
term_count += 1
if term == 'cause':
causal_match = True
if term_related:
term_related.sort(key=lambda tup: tup[0])
term_related, i = term_related[-1] # maximum value
related.append(term_related)
position.append(i)
return term_count, related, causal_match, position
def __init__(self, index, query, start=0, num_top=10, lch=2.16):
"""Inits AnswerEngine by querying the IR module to get Page objects.
Args:
index: An indexer.Index object, which represent the IR system.
query: The direct query string from the user.
start: The number of pages to offset from the beginning of the
page list returned by the index.
num_top: The number of pages (from the top of the ranked list
of pages (sorted by similarity) returned by the index) to
extract answers from.
Combined withe the start-argument, this allows for paging
through the results by only looking at a certain number of
pages at a time.
lch: The Leacock-Chodorow Similarity measurement. Used to
determine if two WordNet senses (synsets) are related.
The default value has been empirically determined to
provide good results, though it may be fine-tuned. This
argument should be a float.
"""
self.query = query
self.start = start
self.num_top = num_top
self.lch = lch
self.answers = None
# Candidate Document Selection
self.ir_query = indexer.regularize(indexer.tokenizer.tokenize(query))
self.ir_query_tagged = None
page_sim = index.ranked(self.ir_query)
self.num_pages = len(page_sim)
# Reduce number of pages we need to get from disk
page_sim = page_sim[start:num_top]
page_ids, similarity = zip(*page_sim)
# Retrieve the Page objects from the list of Page.IDs
self.pages = index.get_page(page_ids)
# Tell each page the value of its similarity score
for page, sim in zip(self.pages, similarity):
page.cosine_sim = sim
def __init__(self, index, query, start=0, num_top=10, lch=2.16):
"""Inits AnswerEngine by querying the IR module to get Page objects.
Args:
index: An indexer.Index object, which represent the IR system.
query: The direct query string from the user.
start: The number of pages to offset from the beginning of the
page list returned by the index.
num_top: The number of pages (from the top of the ranked list
of pages (sorted by similarity) returned by the index) to
extract answers from.
Combined withe the start-argument, this allows for paging
through the results by only looking at a certain number of
pages at a time.
lch: The Leacock-Chodorow Similarity measurement. Used to
determine if two WordNet senses (synsets) are related.
The default value has been empirically determined to
provide good results, though it may be fine-tuned. This
argument should be a float.
"""
self.query = query
self.start = start
self.num_top = num_top
self.lch = lch
self.answers = None
# Candidate Document Selection
self.ir_query = indexer.regularize(indexer.tokenizer.tokenize(query))
self.ir_query_tagged = None
page_sim = index.ranked(self.ir_query)
self.num_pages = len(page_sim)
# Reduce number of pages we need to get from disk
page_sim = page_sim[start:num_top]
page_ids, similarity = zip(*page_sim)
# Retrieve the Page objects from the list of Page.IDs
self.pages = index.get_page(page_ids)
# Tell each page the value of its similarity score
for page, sim in zip(self.pages, similarity):
page.cosine_sim = sim
def __init__(self, index, query, start=0, num_top=10, lch=2.16):
self.query = query
self.start = start
self.num_top = num_top
self.lch = lch
self.answers = None
# Candidate Document Selection
self.ir_query = indexer.regularize(indexer.tokenizer.tokenize(query))
self.ir_query_tagged = None
page_sim = index.ranked(self.ir_query)
self.num_pages = len(page_sim)
# Reduce number of pages we need to get from disk
page_sim = page_sim[start:num_top]
page_ids, similarity = zip(*page_sim)
# Retrieve the Page objects from the list of Page.IDs
self.pages = index.get_page(page_ids)
# Tell each page the value of its similarity score
for page, sim in zip(self.pages, similarity):
page.cosine_sim = sim
