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Previously, negative join filters were folded in with positive joins by
AND/ORing them together and negating the negative joins. Checking for
the existence of something that doesn't match a condition is different
from checking for the non-existence of something that does match a
condition, so now normalization considers positive and negative join
filters to be distinct classes of filters and does not fold them
together.
Also made some whitespace changes.
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Previously, the generator would recognize at most one form per
inflection per lemma; now, the generator adds all variants in AGID to
the database.
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Previously, we generated negative subqueries by integrating them into
the main statement normally, and then making the connecting join be a
LEFT JOIN instead of an INNER JOIN, and by adding a condition that the
join column be NULL. The problem with this is that if the top table of
the subquery joins against any other table (which join throughs always
do), then no rows will be returned. This was solved by putting the
subquery into a CTE and then LEFT JOINing as before with the CTE.
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The new object structure was designed to build on the existing WordNet
structure, while also adding in all of the data that we get from other sources.
More information about this can be found on the project wiki.
The generator has already been completely rewritten to generate a
datafile that uses the new structure. In addition, a number of indexes
are created, which does double the size of the datafile, but also allows
for much faster lookups. Finally, the new generator is written modularly
and is a lot more readable than the old one.
The verbly interface to the new object structure has mostly been
completed, but has not been tested fully. There is a completely new
search API which utilizes a lot of operator overloading; documentation
on how to use it should go up at some point.
Token processing and verb frames are currently unimplemented. Source for
these have been left in the repository for now.
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The submodule contained around 73MB of benchmarks and tests that are not necessary for inclusion in this project. Thus, the submodule has been removed, and the 2.0.7 release of nlohmann/json has been added to the repository.
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Also updated CMakeLists.txt such that including projects don't have to include sqlite3.
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The generator previously had a problem wherein it would ignore WordNet lemmas containing certain non-alpha characters (hyphens, slashes, numbers, apostrophes). In addition to these words not being included in the generated datafile, it had the side effect of causing relationships involving the ignored words (e.g. hypernymy, synonymy, etc) to instead be related to the word with id 0, which did not exist. This rarely caused a failure with direct queries; but it caused hierarchal queries (most notably full hyponymy, which is where the error was noticed) to potentially permit far more lemmas than they should have because a very large number of words could be transitively reached through the sentinel word id 0.
The generator has been fixed to not ignore the words containing special characters, which removed the word id 0 from most relationships and therefore fixed hierarchal queries. The only remaining word id 0s are as a synonym of "free-flying" (synset 301380571) and as an anti-mannernym of "aerially" (synset 400202718). This is because the WordNet data is malformed in the definitions of two words: "aerial" (synset 301380267) and "marine" (synset 301380721). The generator ignored those two lines, causing the described error, although the latter word being ignored did not cause any other errors.
The bug was discovered when the Twitter bot difference (https://github.com/hatkirby/difference) generated a tweet (https://twitter.com/differencebot/status/722084219925700613) as a result of returning the noun "tearaway" in a full hyponym query of "artifact".
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Rhyme detection now ensures that any rhymes it finds are perfect rhymes and not identical rhymes. Rhyme detection is also now a lot faster because additional information is stored in the datafile.
Also fixed a bug in the query interface (and the generator) that could cause incorrect queries to be executed.
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Datafile change: nouns now know how many images are associated with them on ImageNet, and also have their WordNet synset ID saved so that you can query for images of that noun via the ImageNet API. So far, verbly only exposes the ImageNet API URL, and doesn't actually interact with it itself. This may be changed in the future.
The query interface had a huge issue in which multiple instances of the same condition would overwrite each other. This has been fixed.
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adjectives, and adverbs
Word complexity refers to the number of words in a noun, adjective, or adverb.
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In addition:
- Added prepositions.
- Rewrote a lot of the query interface. It now, for a lot of relationships, supports nested AND, OR, and NOT logic.
- Rewrote the token class. It is now a union-like class instead of being polymorphic, which means smart pointers are no longer necessary.
- Querying with regards to word derivation has been temporarily removed.
- Sentinel values are now supported for all word types.
- The VerbNet data retrieved from http://verbs.colorado.edu/~mpalmer/projects/verbnet/downloads.html was found to not be perfectly satisfactory in some regards, especially regarding adjective phrases. A patch file is now included in the repository describing the changes made to the VerbNet v3.2 download for the canonical verbly datafile.
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verbly into its own directory
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Nouns, adjectives, and adverbs now have inflected forms. A large number of WordNet word relationships (all noun-noun relationships, plus synonymy and antonymy for all word types except verbs) have been added. Additionally, CMUDICT is now being used to store word pronunciations for rhyming purposes. Verbly is now also a compiled library rather than being header-only due to the complexity of the query interface.
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Currently, the generator:
- Uses AGID to create entries for verb words and their inflections
- Uses WordNet to create entries for adjective, adverb, and noun senses
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verbly is intended to be a general use natural language generation library. Here, I'm using it to simply generate random verbs or adjectives. A schema for the sqlite database is provided, and for testing I manually added data. A generator program is being written that will generate a database from WordNet, VerbNet, PropBank, and AGID data.
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