I know that in learning Japanese, at times, I've wanted to know which words I should learn that are semantically related to the word I just learned
and a couple of times I looked at the relevant ConceptNet pages
Can't I build this dependency with ConceptNet? The concept of "This" for example is the same for every language. So if I create a dependency tree of the first 2000 concepts. Then, with ConceptNet I can translate any concept into any languages. I'm probably missing something in my reasoning but I don't know what
and then for each concept outside the core, you have a list of which related concepts are closer to the core than it. If that does what you want it sounds pretty cool.
Well, hmm. Without some filtering of what kinds of links you want to follow, I suspect that almost all concepts would end up at distance 1 or 2. Many concepts have lots of edges coming out of them, so it's likely that at least one of those edges would point to a word in the core, and if not, then almost certainly at least one of them would point to another concept that's adjacent to the core that way.
The thing about what you described is, I think you're looking to not do this over all the edges in the graph, but over some subset of edges that indicate the essential knowledge you need to understand a word. And for a word to be at distance 1 from the core, you'd want not just one of its edges, but all of its essential edges, to point to the core.
I asked a while back how conceptnet does extract relation from wikipedia / wiktionary the answer was https://github.com/LuminosoInsight/wikiparsec
Would it be possible to parse the html rendering instead of the wikimarkup?
I mean did someone try to do that?
(by the way I am trying to load wikidata dump in my database it is MUCH MORE difficult than conceptnet, I mean it is all hardware work, but it takes a lot of time, the current estimation is at 2 months of import process remaining)
Hello,
I would like to train the numberbatch embeddings and have some questions.
How to use conceptnet5.vectors?
Is there a main function that starts the train function?
Do i have to build a database with CN Graph to be able to make retrofitting, or is it possible to work only with CSV files of assertions?
Thank you very much!
@amirouche Parsing the HTML instead of the wikimarkup is certainly a possibility -- it means you at least get to use something Official for turning the wikimarkup into HTML, either Parsoid or just scraping the page -- but it seems to me like it leaves you with as big of a parsing problem as you started with
and actually I suspect that some semantics are lost. Which templates are invoked tells you something about the intent of the markup. A word could be in italics for many different reasons, a link could mean many different things, etc.
@filievski I don't think the web page does anything that explicitly represents the link between
/c/en/bike and /c/en/bike/n -- correct me if I've overlooked something. It's just a syntactic operation on these URIs. The page for /c/en/bike/n knows to provide that link to /c/en/bike in the header, and the database is designed to include results for /c/en/bike/n when you look up /c/en/bike.
The data that the Web page is built from is API queries like this: http://api.conceptnet.io/c/en/bike?grouped=true
I have one more question for evaluation. The evaluation documentation says that the database is required for terms out of vocabulary to calculate the embedding from neighbouring terms in the graph. But I could not find a function that accesses the database for this case. I could only find the function that uses the prefix tree to derive similar words to calculate the embedding vector for an out of vocabulary term. In which case do I need the database? Which evaluation function needs access to the database?
There used to be an out-of-vocabulary step that would try looking up adjacent terms in the graph, but now we have the "propagate" step that creates vectors for these terms and makes them part of the vocabulary
which makes the out-of-vocab step a lot simpler
@Akashtyagi08 that's odd. I don't know what gensim is doing differently
we show "nondog" as only 0.7 related to "dog"
Hello,
I tried to reproduce the results of the numberbatch paper and used the evaluation function.
I got the results of word similarity task on the MEN data set 0.878 compared to the paper 0.866.
If the results are better with the new ConceptNet version is there a paper with actual evaluation results?
And should I use ConceptNet 5.5 to be able to reproduce the 0.866 result?
What makes ConceptNet well suited for Embeddings compared to other knowledge graphs?
Unfortunately I could not find in the paper what makes it so good for this task.
I tried to reproduce the results of the numberbatch paper and used the evaluation function.
I got the results of word similarity task on the MEN data set 0.878 compared to the paper 0.866.
If the results are better with the new ConceptNet version is there a paper with actual evaluation results?
And should I use ConceptNet 5.5 to be able to reproduce the 0.866 result?
What makes ConceptNet well suited for Embeddings compared to other knowledge graphs?
Unfortunately I could not find in the paper what makes it so good for this task.
Hello,
I have a question about the retrofitting parameters. Does the orig_vec_weight variable define the alpha parameter of the retrofitting? How did you estimate the value of alpha? Was it a random choice or did you make a parameter search? Currently orig_vec_weight is set to 0.15.
I have a question about the retrofitting parameters. Does the orig_vec_weight variable define the alpha parameter of the retrofitting? How did you estimate the value of alpha? Was it a random choice or did you make a parameter search? Currently orig_vec_weight is set to 0.15.
@JohannaOm I'm looking at the code for that, and I honestly don't know. That parameter was added by someone who once worked for me, Robert Beaudoin, and the commit comment for it was "Shard-oblivious retrofitting."
I remember the issue being that we'd had to break up the matrix into shards by column to keep the retrofitting process in memory, and then he noticed that this was causing distortions when we normalized these smaller rows as if they were the full rows, so this new retrofitting code was his fix for that. But I don't know where the parameter came from.
@Totole75 We tried Neo4J around 2012, so perhaps the situation has changed, but it seemed to me that Neo4J may be a good way to access data that's already in Neo4J, or gradually added to Neo4J, but its ability to import data was lacking and required extending the database itself with custom code.
Hello,
in the paper conceptnet 5.5 you write:
"To avoid manually overfitting by designing our semantic
space around a particular evaluation, we experimented using
smaller development sets, holding out some test data until it
was time to include results in this paper..."
Why did you split the datasets into test and development subsets?
What did you manipulate?
Which parameter did you learn with the development subset.
in the paper conceptnet 5.5 you write:
"To avoid manually overfitting by designing our semantic
space around a particular evaluation, we experimented using
smaller development sets, holding out some test data until it
was time to include results in this paper..."
Why did you split the datasets into test and development subsets?
What did you manipulate?
Which parameter did you learn with the development subset.
It wasn't an automatic parameter search or anything, we were trying different details of how to implement it and seeing if the validation results improved.
Hello! I just recently started looking into and using ConceptNet, and wanted to ask if everything is strictly factual. For example in the research I'm apart of I'd like to have "cat" be associated with "having 9 lives." I realize this is a myth and I don't see that ConceptNet can make this association, but wanted to check. Also does anyone know of some similar database that could make this association?