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113 lines
4.6 KiB
Python
113 lines
4.6 KiB
Python
import random
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import itertools
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def make_orth_variants_example(nlp, example, orth_variant_level=0.0): # TODO: naming
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raw_text = example.text
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orig_dict = example.to_dict()
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variant_text, variant_token_annot = make_orth_variants(
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nlp, raw_text, orig_dict["token_annotation"], orth_variant_level
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)
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doc = nlp.make_doc(variant_text)
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orig_dict["token_annotation"] = variant_token_annot
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return example.from_dict(doc, orig_dict)
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def make_orth_variants(nlp, raw_text, orig_token_dict, orth_variant_level=0.0):
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if random.random() >= orth_variant_level:
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return raw_text, orig_token_dict
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if not orig_token_dict:
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return raw_text, orig_token_dict
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raw = raw_text
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token_dict = orig_token_dict
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lower = False
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if random.random() >= 0.5:
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lower = True
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if raw is not None:
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raw = raw.lower()
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orth_variants = nlp.vocab.lookups.get_table("orth_variants", {})
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ndsv = orth_variants.get("single", [])
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ndpv = orth_variants.get("paired", [])
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words = token_dict.get("words", [])
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tags = token_dict.get("tags", [])
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# keep unmodified if words or tags are not defined
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if words and tags:
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if lower:
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words = [w.lower() for w in words]
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# single variants
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punct_choices = [random.choice(x["variants"]) for x in ndsv]
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for word_idx in range(len(words)):
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for punct_idx in range(len(ndsv)):
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if (
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tags[word_idx] in ndsv[punct_idx]["tags"]
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and words[word_idx] in ndsv[punct_idx]["variants"]
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):
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words[word_idx] = punct_choices[punct_idx]
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# paired variants
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punct_choices = [random.choice(x["variants"]) for x in ndpv]
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for word_idx in range(len(words)):
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for punct_idx in range(len(ndpv)):
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if tags[word_idx] in ndpv[punct_idx]["tags"] and words[
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word_idx
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] in itertools.chain.from_iterable(ndpv[punct_idx]["variants"]):
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# backup option: random left vs. right from pair
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pair_idx = random.choice([0, 1])
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# best option: rely on paired POS tags like `` / ''
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if len(ndpv[punct_idx]["tags"]) == 2:
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pair_idx = ndpv[punct_idx]["tags"].index(tags[word_idx])
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# next best option: rely on position in variants
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# (may not be unambiguous, so order of variants matters)
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else:
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for pair in ndpv[punct_idx]["variants"]:
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if words[word_idx] in pair:
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pair_idx = pair.index(words[word_idx])
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words[word_idx] = punct_choices[punct_idx][pair_idx]
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token_dict["words"] = words
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token_dict["tags"] = tags
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# modify raw
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if raw is not None:
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variants = []
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for single_variants in ndsv:
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variants.extend(single_variants["variants"])
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for paired_variants in ndpv:
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variants.extend(
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list(itertools.chain.from_iterable(paired_variants["variants"]))
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)
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# store variants in reverse length order to be able to prioritize
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# longer matches (e.g., "---" before "--")
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variants = sorted(variants, key=lambda x: len(x))
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variants.reverse()
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variant_raw = ""
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raw_idx = 0
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# add initial whitespace
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while raw_idx < len(raw) and raw[raw_idx].isspace():
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variant_raw += raw[raw_idx]
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raw_idx += 1
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for word in words:
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match_found = False
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# skip whitespace words
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if word.isspace():
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match_found = True
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# add identical word
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elif word not in variants and raw[raw_idx:].startswith(word):
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variant_raw += word
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raw_idx += len(word)
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match_found = True
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# add variant word
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else:
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for variant in variants:
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if not match_found and raw[raw_idx:].startswith(variant):
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raw_idx += len(variant)
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variant_raw += word
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match_found = True
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# something went wrong, abort
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# (add a warning message?)
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if not match_found:
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return raw_text, orig_token_dict
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# add following whitespace
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while raw_idx < len(raw) and raw[raw_idx].isspace():
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variant_raw += raw[raw_idx]
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raw_idx += 1
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raw = variant_raw
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return raw, token_dict
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