Still thinking about interlinear glossing for my language learning project. The leizig.js library is great but my use case isn’t really what the author had in mind. I really just need to display a unit consisting of the word as it appears in the text, the lemma for that word form, and (possibly) the part of speech. For academic linguistics purposes, what I have in mind is completely non-standard.
The other issue with leizig.
leipzig.js is a library for applying interlinear gloss
to texts for linguistic analysis. In this post, I experiment a little with this libary to evaluate whether it would work for a little project of mine.
Starting a new devlog about Hedghog, a new language learning app and some thoughts about the interlinear display of lemmas.
Splitting text into sentences is one of those tasks that looks simple but on closer inspection is more difficult than you think. A common approach is to use regular expressions to divide up the text on punction marks. But without adding layers of complexity, that method fails on some sentences. This is a method using spaCy
I alluded to this nuance involving variable scope in my post on automating pdf processing, but I wanted to expand on it a bit.
Consider this little snippet:
i=0 printf "foo:bar:baz:quux" | grep -o '[^:]+' | while read -r line ; do printf "Inner scope: %d - %s\n" $i $line ((i++)) [ $i -eq 3 ] && break; done printf "====\nOuter scope\ni = %d\n" $i; If you run this script - not in interactive mode in the shell - but as a script, what will i be in the outer scope?
In my perpetual effort to get out of work, I’ve developed a suite of automation tools to help file statements that I download from banks, credit cards and others. While my setup described here is tuned to my specific needs, any of the ideas should be adaptable for your particular circumstances. For the purposes of this post, I’m going to assume you already have Hazel. None of what follows will be of much use to you without it.
In DEVONthink, I tag a lot. It’s an integral part of my strategy for finding things in my paperless environment. As I wrote about previously hierarchical tags are a big part of my organizational system in DEVONthink. For many years, I tagged subject matter with tags that emmanate from a single tag named topic_, but it was really an unnecessary top-level complication. So, the first item on my to-do list was to get rid of the all tags with a topic_ first level.
I have written previously about stripping syllabic stress marks from Russian text using a Perl-based regex tool. But I needed a means of doing in solely in Python, so this just extends that idea.
#!/usr/bin/env python3 def strip_stress_marks(text: str) -> str: b = text.encode('utf-8') # correct error where latin accented ó is used b = b.replace(b'\xc3\xb3', b'\xd0\xbe') # correct error where latin accented á is used b = b.replace(b'\xc3\xa1', b'\xd0\xb0') # correct error where latin accented é is used b = b.
For one-off projects that target Anki collections, I often use Python in a standalone application rather than an Anki add-on. Since I’m not going to distribute these little creations that are specific to my own needs, there’s no reason to create an add-on. These are just a few notes - nothing comprehensive - on the process.
One thing to be aware of is that there must be a perfect match between the Anki major and minor version numbers for the Python anki module to work.
This may be obvious to some, but visually-recognizing character encoding at a glance is not always obvious.
For example, pronunciation files downloaded form Forvo have the following appearance:
How can we extact the actual word from this gibberish? Optimally, the filename should reflect that actual word uttered in the pronunciation file, after all.
Step 1 - Extracting the interesting bits The gibberish begins after the pronunciation_ru_ and ends before the file extension.