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Improved quote detection and attribution #382
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hey @afriedman412 , thanks for your patience on this. i've left comments requesting a handful of minor changes. there's also a consistent formatting issue that makes diffing a bit hard; could you run black
over the changed modules, so that the code formatting is standard / consistent with the rest of textacy?
src/textacy/extract/triples.py
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from spacy.tokens import Doc, Span, Token | ||
import regex as re |
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textacy
doesn't currently have regex
as a dependency. is it possible to use the stdlib re
module here instead?
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probably? I've had some issues using re
when working with finicky regular expressions, so I kind of use it by default now but I can test it.
@@ -9,12 +9,12 @@ | |||
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import collections | |||
from operator import attrgetter | |||
from typing import Iterable, Mapping, Optional, Pattern | |||
from typing import Iterable, Mapping, Optional, Pattern, Literal |
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from typing import Iterable, Mapping, Optional, Pattern, Literal | |
from typing import Iterable, Literal, Mapping, Optional, Pattern |
content = doc[qtok_start_idx : qtok_end_idx + 1] | ||
# pairs up quotation-like characters based on acceptable start/end combos | ||
# see constants for more info | ||
qtoks = [tok for tok in doc if tok.is_quote or (re.match(r"\n", tok.text))] |
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why do we consider tokens with "\n"
in them to be quotation-like?
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some formatting dictates that if you start a new paragraph while quoting someone, you start the next paragraph with a quotation mark even though the original quotation mark is never closed. in that case the linebreak functions as a closing quotation mark.
i actually added a test for it but it's actually not a great example -- I'll find a better one.
src/textacy/extract/triples.py
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@@ -27,9 +27,10 @@ | |||
nsubjpass, | |||
obj, | |||
pobj, | |||
xcomp, | |||
xcomp |
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this comma was here for a reason -- black
put it there automatically :)
xcomp | |
xcomp, |
src/textacy/constants.py
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@@ -21,6 +21,51 @@ | |||
OBJ_DEPS: set[str] = {"attr", "dobj", "dative", "oprd"} | |||
AUX_DEPS: set[str] = {"aux", "auxpass", "neg"} | |||
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MIN_QUOTE_LENGTH: int=4 |
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i'm not sure this is a "constant" value, seems more like a parameter with a default value that should go in the direct_quotations
extraction function. what do you think?
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yeah that makes sense
and q.i > qtok_idx_pairs[-1][1] | ||
): | ||
for q_ in qtoks[n+1:]: | ||
if (ord(q.text), ord(q_.text)) in constants.QUOTATION_MARK_PAIRS: |
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why do we store -- and compare against -- the ord
values instead of just the "raw" text quotation marks?
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less room for error
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could you elaborate on that?
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there are lots of ways something that is supposed to be a raw text quotation mark gets tokenized incorrectly, when you start dealing with encoding, decoding, pulling text from html, escape character issues, the whitespace_
issue above, etc etc etc. as the edge cases piled up, I realized the ord
value was consistent no matter what. so I decided to use that instead.
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got it. i think at a later point i may revisit some of this logic, under the assumption that the user has dealt with bad text encodings, etc. before attempting quote detection. but it's probably fine for now :)
@@ -305,15 +304,105 @@ def expand_noun(tok: Token) -> list[Token]: | |||
child | |||
for tc in tok_and_conjuncts | |||
for child in tc.children | |||
# TODO: why doesn't compound import from spacy.symbols? |
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just wondering why this line was deleted? it's a comment, for me! :)
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i thought it was my comment lol
tests/extract/test_triples.py
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], | ||
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looks like your code editor is making some spurious changes (here and elsewhere) and that aren't PEP-compliant / black-enforced. we'll want to fix these before any merge.
tests/extract/test_triples.py
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] | ||
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…x` package, min_quote_length is now a `direct_quotations` parameter (not a constant), added a better example for testing linebreaks that function as closing quotes
Description
Motivation and Context
This is part of a larger project to create a package to combine quote detection and attribution with coreference resolution, which will be used for the analysis of several thousand newspaper articles.
How Has This Been Tested?
A/B testing with random samples of said articles, test creation after major changes.
(New tests added as well.)