def get_report(self, doc):
nativespeaker_report = Report(
"\nTerms biased towards native speakers:")
words_with_indices = doc.words_with_indices()
#print(words_with_indices)
found = False
for word, start, stop in words_with_indices:
word = word.lower()
for nativeword in NATIVE_WORDS.Used:
x = re.search(nativeword, word)
if (x):
#print(x.span(), x.string, x.group())
found = True
if NATIVE_WORDS['Recommend2'].loc[
NATIVE_WORDS['Used'] ==
x.group()].item() == 'none':
recommend2 = ""
else:
recommend2 = " or '" + NATIVE_WORDS['Recommend2'].loc[
NATIVE_WORDS['Used'] == x.group()].item() + "'"
print(
"Consider replacing '", x.group(), "' with '",
NATIVE_WORDS['Recommend1'].loc[NATIVE_WORDS['Used'] ==
x.group()].item(), "'",
recommend2)
nativespeaker_report.add_flag(
Flag(start, stop, Issue(word)))
if found:
nativespeaker_report.set_summary(
"To encourage non-native speakers, use short words and simple sentences"
)
return nativespeaker_report
def get_report(self, doc):
"""
Report the usage of unnecessarily gendered words.
Arguments:
doc (Document): The document to check
Returns:
Report
"""
report = Report("\nUnnecessary use of gender terms")
token_indices = doc.words_with_indices()
found = False
for word, start, stop in token_indices:
# NELSON - changed loop so word can be found in GENDERED_WORDS
if word.lower() in GENDERED_WORDS:
found = True
report.add_flag(
Flag(start, stop, Issue("{word}".format(word=word))))
if found:
report.set_summary(
"Replace gender terms with 'person' or 'individual', or a position-specific term, such as 'doctor' or 'author'"
)
return report
def get_report(self, doc):
"""
Generate a report on the text based upon mentions of
personal-life-related words.
Arguments:
doc (Document): The document to check
Returns:
Report
"""
report = Report("\nTerms about personal life")
token_indices = doc.words_with_indices()
found = False
for word, start, stop in token_indices:
# NELSON - changed loop to look for word in lis of PERSONAL_LIFE_TERMS
if word.lower() in PERSONAL_LIFE_TERMS:
found = True
report.add_flag(
Flag(start, stop, Issue("{word}".format(word=word))))
if found:
report.set_summary('Found words relating to personal life')
return report
def get_report(self, doc):
"""
Generates a report on the text based upon effort vs accomplishment.
Also adds a summary if there are NO words about accomplishment, or if
the ratio of effort to accomplishment words is particularly low.
Arguments:
doc (Document): The document to check
Returns:
Report
"""
report = Report("Effort vs Accomplishment")
effort_flags = []
accomplishment_flags = []
token_indices = doc.words_with_indices()
for word, start, stop in token_indices:
if word.lower() in EFFORT_WORDS:
effort_flags.append(
Flag(
start, stop,
Issue(
"Effort vs Accomplishment",
"The word '{word}' tends to speak about effort more than accomplishment."
.format(word=word),
"Try replacing with phrasing that emphasizes accomplishment.",
bias=Issue.negative_result)))
if word.lower() in ACCOMPLISHMENT_WORDS:
accomplishment_flags.append(
Flag(
start, stop,
Issue(
"Effort vs Accomplishment",
"The word '{word}' tends to speak about accomplishment more than effort."
.format(word=word),
bias=Issue.positive_result)))
for flag in effort_flags:
report.add_flag(flag)
if (len(accomplishment_flags) is 0
or len(effort_flags) / len(accomplishment_flags) >
1.2 # TODO: Arbitrary!
):
# Avoid divide-by-zero errors
if len(accomplishment_flags) == 0:
report.set_summary(
"This document has too few words about concrete accomplishment."
)
else:
report.set_summary(
"This document has a high ratio ({}:{}) of words suggesting effort to words suggesting concrete accomplishment."
.format(len(effort_flags), len(accomplishment_flags)))
return report
def get_report(self, doc):
"""
Generates a report on the text based upon effort vs accomplishment.
Also adds a summary if there are NO words about accomplishment, or if
the ratio of effort to accomplishment words is particularly low.
Arguments:
doc (Document): The document to check
Returns:
Report
"""
report = Report("\nTerms focusing on effort vs accomplishment")
effort_flags = []
accomplishment_flags = []
token_indices = doc.words_with_indices()
for word, start, stop in token_indices:
# NELSON - changed loop so word can be found in EFFORT or ACCOMPLISHMENT words
if word.lower() in EFFORT_WORDS:
effort_flags.append(
Flag(
start, stop,
Issue("{word}".format(word=word),
bias=Issue.negative_result)))
if word.lower() in ACCOMPLISHMENT_WORDS:
accomplishment_flags.append(
Flag(
start, stop,
Issue(
"'{word}' describes accomplishment more than effort"
.format(word=word),
bias=Issue.positive_result)))
for flag in effort_flags:
report.add_flag(flag)
if (len(accomplishment_flags) is 0
or len(effort_flags) / len(accomplishment_flags) >
1.2 # TODO: Arbitrary!
):
# Avoid divide-by-zero errors
if len(accomplishment_flags) == 0:
report.set_summary(
"Too few words about concrete accomplishments")
else:
report.set_summary(
"High ratio ({}:{}) of words suggesting effort ratherthan accomplishment."
.format(len(effort_flags), len(accomplishment_flags)))
return report
def get_report(self, doc):
"""
Generate a report on the text based upon mentions of publications.
Arguments:
doc (Document): The document to check
Returns:
Report
"""
report = Report("Publications")
report.set_summary(self.get_summary(doc))
return report
def get_report(self, doc):
male_report = Report("\nTerms biased towards men:")
words_with_indices = doc.words_with_indices()
#print(words_with_indices)
found = False
for word, start, stop in words_with_indices:
word = word.lower()
for maleword in MALE_WORDS:
searchTerm = "^" + maleword + ".."
x = re.search(searchTerm, word)
if (x):
#print(x.span(), x.string, x.group())
found = True
male_report.add_flag(
Flag(start, stop, Issue("{word}".format(word=word))))
if found:
male_report.set_summary(
"Depending on context, these words may be biased towards recruiting men"
)
return male_report
def get_report(self, doc: Document):
"""
Generates a report on the text based upon effort vs accomplishment.
Also adds a summary if there are NO words about accomplishment, or if
the ratio of effort to accomplishment words is particularly low.
Arguments:
doc (Document): The document to check
Returns:
Report
"""
report = Report("Effort vs Accomplishment")
# Keep track of accomplishment- or effort-specific words:
accomplishment_words = 0
effort_words = 0
# Keep track of flags (we'll deduplicate them before reporting)
flags = set()
for word, start, stop in doc.words_with_indices():
if word.lower() in EFFORT_WORDS:
report.add_flag(
Flag(
start,
stop,
Issue(
"Effort vs Accomplishment",
f"The word '{word}' tends to speak more about " +
"effort than concrete accomplishment.",
# lower negative bias because this may be spurious.
# Specifically, the presence of these words doesn't
# mean that it's being attributed to the subject of
# the letter.
bias=Issue.negative_result * 0.5,
fix="Speak about concrete achievement rather " +
"than abstract effort.",
),
))
if word.lower() in ACCOMPLISHMENT_WORDS:
report.add_flag(
Flag(
start,
stop,
Issue(
"Effort vs Accomplishment",
f"The word '{word}' illustrates concrete accomplishment.",
# lower positive valence because this may be spurious.
# Specifically, the presence of these words doesn't
# mean that it's being attributed to the subject of
# the letter.
bias=Issue.positive_result * 0.5,
),
))
doc = nlp(doc.text())
# Loop over tokens to find adjectives to flag:
for token in doc:
# Find all tokens whose dependency tag is adjectival complement:
if token.dep_ == "acomp":
# Get all dependencies of the head/root of the tagged sentence
# and look for nouns (which are likely to be the referenced
# subject of this adjectival complement):
for reference_token in token.head.children:
# If this token IS a noun but it's an ignored pronoun, move on
if (reference_token.pos_ in ["PRON", "PROPN"] and
reference_token.text not in _PRONOUNS_TO_IGNORE):
# If accomplishment-flavored, add positive flag.
if token.text in ACCOMPLISHMENT_WORDS:
accomplishment_words += 1
warning = (
f"The word '{token.text}' refers to " +
"explicit accomplishment rather than effort.")
suggestion = ""
bias = Issue.positive_result
# If effort-flavored, add negative flag.
elif token.text in EFFORT_WORDS:
effort_words += 1
warning = (
f"The word '{token.text}' tends to speak " +
"about effort more than accomplishment.")
suggestion = ("Try replacing with phrasing that " +
"emphasizes accomplishment.")
bias = Issue.negative_result
else:
continue
flags.add((
token.sent.start_char,
token.sent.end_char,
warning,
suggestion,
bias,
))
for (start, stop, warning, suggestion, bias) in flags:
# Add a flag to the report:
report.add_flag(
Flag(
start,
stop,
Issue("Effort vs Accomplishment",
warning,
suggestion,
bias=bias),
))
if 0 < effort_words <= accomplishment_words:
report.set_summary(
"This document has a high ratio of words suggesting " +
f"effort ({effort_words}) to words suggesting " +
f"concrete accomplishment ({effort_words}).", )
return report