book-pipeline held its own copy of the scheme operations bibclean already
had. The scheme itself — which headings a collection uses, their aliases,
their parents — stays with the collection, because that is the most
collection-specific thing there is. What a scheme *means* is the same
question everywhere, and this answers it: `canonical_subject`,
`compose_subjects`, `needs_subject`.
Checked against the Python over the library's 228 distinct tags and all
5,385 books: identical on every one.
book-pipeline had its own copy of bibclean's author recovery. Bound on all
four surfaces so it can stop.
Checked against that Python over all 5,385 books in the library: 5,375
identical. On the other ten the Python had taken a translator, an illustrator
or the publisher off a copyright line — Sally-Ann Spencer for *The Dwarves*,
Thomas Nelson for a table of contents, C. S. Lewis for a book about Tolkien —
and bibclean either finds the real author in the Library of Congress block or
says nothing. Every one of the ten was read by hand.
`looks_like_footnote_citations` is the function chunkmd's `looks_like_citations`
was ported from, and book-pipeline was still running the Python original at
four call sites. Bound on all four surfaces so it can stop.
Checked against the Python over 34,458 chunks of the live corpus: no
disagreement.
Cuts a reference list into entries — at the note numbers where there are any,
at the layout where there are not — and reads each as far as its punctuation
allows. Fields that would not parse are named in `unsettled` rather than
guessed at, and `--prompt` asks a model about exactly those and no others.
Over 202 real reference sections from the corpus: 8,566 references, 95% with
a title, 82% with a year, 46% settling every field with no model involved.
A title is refused where the entry was never divided, so no record reports
its own text back as the work it cites.
`ibid.` is flagged and left alone — it points at a work, and the entry does
not say which. A rule of dashes is filled in, because the style defines it as
the author above.
chunkmd gains `note_starts` and `cites_something`: recognising a page of
notes and splitting one are the same knowledge, and two copies of that
pattern would drift.
The hashed bag of words asks only whether two stretches repeat the same
words, and cannot tell that ships and vessels are one subject. An embedding
can. segment_with takes one vector per window and falls back to the words
when it is given none, or the wrong number of them — a caller that embedded
some other recording's windows is asking about that recording.
windows_of gives the text to embed, in the order the vectors come back in,
so nobody has to reimplement the windowing pass to use this.
Bound in all three languages, and on the command line as --windows and
--embeddings, which pipe through emb. The C ABI version goes to 2:
catena_segment_cues gained an argument.
funkpower