Yes, thank you for the UNL background, that is extremely helpful. I've been
reading some of the articles Louis provided as references, and it seems to
me from just this perhaps naive point of view, that a lot of the complexity
is associated with disambiguation of meaning - for nouns I think Wikidata
items (and their relations to lexeme senses) solve that problem, but we are
still missing I think a lot of the detail needed to do the same with
adjectives and verbs (at least). So there is definitely some room for
finding better ways to model - but maybe Wikidata could be expanded to
handle the adjective/verb cases too. In general the concept of a single
meaning associated with a Wikidata item as its identifier and a collection
of attributes and relationships attached to that item is a powerful one
that could resolve many such issues.
On Sun, Jul 5, 2020 at 6:55 PM Adam Sobieski <adamsobieski(a)hotmail.com>
Thank you for the information about the Universal Networking Language 
and the World Atlas of Language Structures .
Do you opine that adding attributes to objects, relations and expressions
enhances expressiveness for various features of natural language?
I wonder whether there exist mappings or workarounds with which to obtain
such expressiveness for models such as Wikidata’s.
Scripting Environments for Natural Language Generation
observing that Lua / WebAssembly solutions exist, we can note that
scripting engines such as V8 are easy to use and to add global objects and
API to. Resembling how Web browsers provide scripting environments and API
for functions, we can envision providing scripting environments and API for
natural language generation functions.
I wonder what you might think about scripting environments and API for
natural language generation scenarios?
*From: *Louis Lecailliez <louis.lecailliez(a)outlook.fr>
*Sent: *Saturday, July 4, 2020 2:10 PM
*Subject: *Re: [Abstract-wikipedia] NLP issues severely overlooked (Amir
I understand the process is different that usual research. In fact I've
seen Wikipedia grown from an unknown website to the biggest encyclopedia it
is now. I use it daily in multiple languages and love it. I know what crowd
sourcing could achieve.
It's also possible that the mere *finding* of
these stumbling blocks by
such a big, diverse, open, and active community, will
itself be a
contribution to the scientific knowledge around this subject.
I disagree here. It would be contribution to scientic knowledge if and
only if it wasn't discovered before. My email was precisely about that:
capitalizing on the knowledge that has already been discovered, to avoid
making the same mistake them again. It would not matter for a small
project, but this one is really ambitious. We are speaking of 40 years of
work by a horde of talented and very knowledgeable people, so this isn't to
be dismissed easily.
This thing is, my previous email was a bit abstract, because it were a
review of the paper, not of the project itself. I should have made more
examples to illustrate where the problem lies.
Let's start with a simple example, in English, with corresponding Wikidata
entities in-between parenthesis. I'm also using pseudo-turtle notation with
made up relationships.
France (Q142) is a country (Q6256).
<Q142> <rel_is> <Q6256> .
Creating the English sentence is straightforward with the naive approach
presented in the paper.
What is the French equivalent?
La France est un pays.
More information is required in the abstract representation: the text
generator needs to know about the gender of both nouns (France and pays).
So we need to extend the model as such:
<Q142> <rel_gender> <Q1775415> .
<Q6256> <rel_gender> <Q499327> .
Fine! Now what about Chinese?
What we have in the middle of the sentence is a classifier (個). The model
needs the following update:
<Q499327> <rel_use_classifier> <Q63153> .
To handle these 3 languages, the model has already 3 additional triples
just for accounting for linguistic facts occuring in these languages.
Wikipedia exists in more than 300 languages, and the world has about 6000
of them, each of them having particularities that must be taken into
account. Fortunately they recoup themselves in-between languages.
Nonetheless the World Atlas Language Structures (
) count 144 distinct language features. Some
are related to speech, but this means there is probably something like a
hundred features that must be taken into account in the data model to
produce valid natural language sentence.
Note that in the Chinese example, there is also a number (一, one) showing
up. This is a phenomenon that must be taken into account; and it's not
always appearing when using 是 (to be). How complex the "lambda" system
will be just to deal with this issue? Hint: very much. It also needs to be
compatible with dozen of other phenomena.
Then each of those features require extensive and complete data. For
French, the gender of every noun entity *must* be present, otherwise there
is half a chance of producing a wrong sentence each time a noun entity is
encountered. For Chinese and Japanese, classifier information must be
present for all noun, in case one must be enumerated. Where does the
project will get the data from? (we are speaking of millions of item, most
not referenced in existing dictionaries) How will this be encoded? Those
are real questions that must be answered.
Suppose now we have done the work for "renderers" in these three
languages. They both use the more or less similar A X B structure where X
is a verb meaning "to be".
What would be the Japanese equivalent?
The more natural structure would be like:
What is a play here is a topicalization (Q63105) of France, followed by a
predicate (it's a country). This is very different from the previous
structure, which, not surprisingly enough, needs it's own representation.
To make situation more difficult, the previous (A be B) structure can also
exists in Japanese, but would lead to a totally different sentence if used.
The paper states that Figure 1 and 2 are examples that will be more
complex in real life. Yet, the use of any existing formalism is dismissed,
which mean all the situations I illustrated in this email will need to be
dealt with in an ad hoc fashion. Moreover, changing formalism (be it ad hoc
or not) will require to change every piece of code/data using it. This will
happen everytime a language with unsupported feature(s) is added to the
project. It's not hard to see how this will waste a huge amount of time and
goodwill from involved people. The very code focussed tone of the paper,
the english-centric approach used in the examples and the lack of
references shows that the complexity of the task on the NLP front is not
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1. Re: NLP issues severely overlooked (Charles Matthews)
2. Use case: generation of short description (Jakob Voß)
3. Re: NLP issues severely overlooked (Amir E. Aharoni)
Date: Sat, 4 Jul 2020 14:05:09 +0100 (BST)
From: Charles Matthews <charles.r.matthews(a)ntlworld.com>
To: "General public mailing list for the discussion of Abstract
Wikipedia (aka Wikilambda)" <
Subject: Re: [Abstract-wikipedia] NLP issues severely overlooked
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It is interesting to be on a list where one can hear about software
issues, and then computational linguistic problems. I'm not an expert in
I do have 17 years of varied Wikimedia experience (and I use my real name
On 04 July 2020 at 12:25 Louis Lecailliez
Nothing precise is said about linguistic
resources in the AW paper
except for "These function finally can call the
stored in Wikidata.", which is not very convincing: first because
Wiktionary projects themselves severely lacks content and structure for
those who has some content at all, secondly since specialized NLP
ressources are missing there too (note: I'm not familiar with Wikidata so I
could be wrong, however I never saw it cited for the kind of NLP resources
I'm talking about).
I can comment about this. Besides Wiktionary, there is the "lexeme"
namespace of Wikidata. It is a relatively new part of Wikidata, dealing
with verbal forms.
To finish on a positive note, I would like to
highlight the points I
really like in the paper. First, its collaborative and open
all Wikimedia projects, gives him a much higher chance of success than its
It is worth saying, for context, that there is a certain style or
philosophy coming from the wiki side: more precisely, from the wikis before
Wikipedia. There is the slogan "what is the simplest thing that would
actually work?" You might argue, plausibly, that Wikipedia at nearly 20
years old, shows that there is a bit more to engineering than that.
On the other hand, looking at Wikidata at seven years old, there is some
point to the comment. It has a rather simple approach to linked structured
data, compared to the Semantic Web environment. (Really, just write a very
large piece of JSON and try to cope with it!) But the number of binary
relations used (8K, if you count the "external links" handling) is now
quite large, and has grown organically. The data modelling is in a sense
primitive, sometimes non-existent. But the range of content handled really
is encyclopedic. And in an area like scientific bibliography, at a scale of
tens of millions of entities, the advantages of not much ontological
fussiness begin to be seen.
Wikidata started as an index of all Wikipedia articles, and is now five
times the size needed for that: a very enriched "index".
I suppose the NLP required to code up, for example, 50K chemistry articles
about molecules, might be a problem that could be solved, leaving aside the
general problems for the moment.
In any case, there is a certain approach, neither academic nor commercial,
that comes with Wikimedia and its communities, and it will be interesting
to see how the issues are addressed.
Charles Matthews (in Cambridge UK)