Anton, sequential and random parses are in D56 and D57. Or do you want
specifically the ones for GS and SS? If so, please tell me where you
want them to avoid messing with your file structure, please.
Yes, the mix of distance and MI is what we have been doing when we use
the distance weighting in MST parsing. But as I noticed before, we
should find a good tuning for each case, because the MI's vary about two
orders of magnitude.
a.
On 07/05/19 15:58, Anton Kolonin @ Gmail wrote:
Andres, can you upload the sequential parses that you have evaluated
and provide them in the comments to the cells?
Ben, I think the 0.67-0.72 corresponds to naive impression that
2/3-3/4 of word-to-word connections in English is "sequential" and the
rest is not. For Russian and Portuguese, it would be somewhat less, I
guess.
What you suggest here ("used *both* the sequential parse *and* some
fancier hierarchical parse as inputs to clustering and grammar
learning? I.e. don't throw out the information of simple
before-and-after co-occurrence, but augment it with information from
the statistically inferred dependency parse tree") can be simply (I
guess) implemented in existing MST-Parser given the changes that
Andres and Claudia have done year ago.
That could be tried with "distance_vs_MI" blending parameter in the
MST-Parser code which accounts for word-to-word distance. So that if
the distance_vs_MI=1.0 we would get "sequential parses",
distance_vs_MI=0.0 would produce "Pure MST-Parses", distance_vs_MI=0.7
would provide "English parses", distance_vs_MI=0.5 would provide
"Russian parses", does it make sense, Andres?
Ben, do you want let Andres to try this - get parses with different
distance_vs_MI in range 0.0-1.0 an see what happens?
This could be tried both ways using traditional MI or DNN-MI, BTW.
Cheers,
-Anton
06.05.2019 12:30, Ben Goertzel :
On Sun, May 5, 2019 at 10:15 PM Anton Kolonin @ Gmail
<akolo...@gmail.com <mailto:akolo...@gmail.com>> wrote:
Hi Linas, I am re-reading your emails and updating our TODO
issues from some of them.
Not sure about this one:
>Did Deniz Yuret falsify his thesis data? He got better than 80%
accuracy; we should too.
I don't recall Deniz Yuret comparing MST-parses to
LG-English-grammar-parses.
Linas: Where does the > 80% figure come from?
This paper of Yuret's
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.129.5016&rep=rep1&type=pdf
cites 53% accuracy compared against "dependency parses derived from
dependency-grammar-izing Penn Treebank parses on WSJ text" .... It
was written after his PhD thesis. Is there more recent work by Yuret
that gives massively better results? If so I haven't seen it.
Spitkovsky's more recent work on unsupervised grammar induction seems
to have gotten better statistics than this, but it used radically
different methods.
a) Seemingly "worse than LG-English" "sequential parses" provide
seemingly better "LG grammar" - that may be some mistake, so we
will have to double-check this.
Anton -- Have you looked at the inferred grammar for this case, to
see how much sense it makes conceptually?
Using sequential parses is basically just using co-occurrence rather
than syntactic information
I wonder what would happen if you used *both* the sequential parse
*and* some fancier hierarchical parse as inputs to clustering and
grammar learning? I.e. don't throw out the information of simple
before-and-after co-occurrence, but augment it with information from
the statistically inferred dependency parse tree...
-- Ben
--
-Anton Kolonin
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