corpus-preprocess 0.0.7

Utility functions to preprocess Phil. legalese in weasel-based flows.

corpus-preprocess

Utility functions to preprocess Phil. legalese in weasel-based flows:

1. lexcat-proj; and
2. lexcat-multi

[!IMPORTANT] Requires private corpus-assets folder and sqlite3 db in citelaws-data to be cloned locally.

- corpus-assets: # folder structure
  - concept: # must be two-level nested patterns.json + q.txt
  - artifact: # single folder patterns.json + q.txt
  - text: # each file is a .txt

Language customization

Assuming familiarity with spacy:

nlp.tokenizer = customize_tokenizer(nlp, special_token_rules) # custom tokenizer
ruler = nlp.add_pipe(
    "span_ruler",
    config={
        "spans_key": "ruler",
        "phrase_matcher_attr": "LOWER",
        "spans_filter": {"@misc": "spacy.first_longest_spans_filter.v1"}, # longest spans only
    },
)
ruler.add_patterns(patterns)  # created patterns from this library and corpus-assets

[!NOTE] Loading model with 130k pattern lines takes ~2 min.

Training data

Concept spans

for folder in get_concepts(asset_dir.joinpath("concept")):
    bn = DocBin()
    # use q.txt as queries to the db
    # number of segments per q.txt to fetch
    docs = apply_concept_q_filter(nlp, db_file, filter_path=folder, max_segments=500)
    for doc in docs:
        bn.add(doc)
    bn.to_disk(asset_dir.joinpath(f"train/{folder.stem}.spacy"))

Each concept_dir contains subtopics:

- corpus-assets: # folder structure
  - concept: # must be two-level nested
    - political: # main subject category
        - bill_of_rights: # sub-topic
            - patterns.json # contains matcher files
            - q.txt # contains lines which can be used to query the database

Because of this structure, it's possible to train a textcat_multilabel component:

textcat_options = [concept["id"].split("/")[0] for concept in concept_patterns]

@Language.factory(name="add_cats_from_spans")
class AddTextCatComponent:
    def __init__(self, nlp: Language, name: str, options: list[str]):
        self.nlp = nlp
        self.options = options

    def __call__(self, doc) -> Doc:
        doc.cats = {op: 0.0 for op in self.options}
        for span in doc.spans["sc"]:
            if span.id:  # some spans won't have an id
                value = self.nlp.vocab.strings[span.id]
                if "/" in value:  # e.g. political/bill_of_rights
                    main_topic = value.split("/")[0]  # just political
                    if main_topic in self.options:
                        if doc.cats[main_topic] == 0.0:
                            doc.cats[main_topic] = 1.0
        return doc

Non-concept spans

Although patterns from set_patterns() are included in the constructed nlp object, can ensure that a certain of rows (filter_count) are fetched from the database that have spans which are labeled title and/or serial, etc.

for label in {"unit", "ref", "serial", "title", "axiom", "date", "juridical"}:
    bn = DocBin()
    docs = apply_label_filter(nlp, db_file, filter_labels={label}, filter_count=1500)
    for doc in docs:
        bn.add(doc)
    bn.to_disk(asset_dir.joinpath(f"train/{label}.spacy"))