diff --git a/resources/arxmliv-embeddings-082019.md b/resources/arxmliv-embeddings-082019.md
index b14e2d6af97d804e4c33691c538ee95a12175aae..11a21b554832f9ec399e14b615b54d7d27757cc9 100644
--- a/resources/arxmliv-embeddings-082019.md
+++ b/resources/arxmliv-embeddings-082019.md
@@ -48,7 +48,7 @@ articles, the right of distribution was only given (or assumed) to arXiv itself.
| subset | tokens | unique words | unique words (freq 5+ ) |
| ---------- | -------------: | -----------: | ----------------------: |
-| complete | 15,214,964,673 | 2,868,070 | 1,013,106 |
+| complete | 15,192,564,807 | 2,782,667 | 989,136 |
### Citing this Resource
@@ -59,7 +59,7 @@ Please cite the main dataset when using the word embeddings, as they are generat
([SIGMathLing members](/member/) only)
### Generated via
- - [llamapun 0.3.3](https://github.com/KWARC/llamapun/releases/tag/0.3.3),
+ - [llamapun 0.3.4](https://github.com/KWARC/llamapun/releases/tag/0.3.4),
- [GloVe 1.2, 2019](https://github.com/stanfordnlp/GloVe/tree/07d59d5e6584e27ec758080bba8b51fce30f69d8)
### Generation Parameters
@@ -71,7 +71,7 @@ Please cite the main dataset when using the word embeddings, as they are generat
token_model_error.txt > token_model_complete.txt
```
- * [llamapun v0.3.3](https://github.com/KWARC/llamapun/releases/tag/0.3.3), `corpus_token_model` example used for token model extraction
+ * [llamapun v0.3.4](https://github.com/KWARC/llamapun/releases/tag/0.3.4), `corpus_token_model` example used for token model extraction
* processed logical paragraphs, abstracts, captions and keywords, ignore all other content (e.g. tables, bibliography, others)
* excluded paragraphs containing latexml errors (marked via a `ltx_ERROR` HTML class); also excluded when words over 25 characters were encountered.
* used llamapun math-aware word tokenization, with sub-formula math lexemes (improved robustness since 2018)
@@ -80,6 +80,7 @@ Please cite the main dataset when using the word embeddings, as they are generat
* internal references replaced with `ref` token (e.g. `Figure ref`)
* textual punctuation is included as-is, while mathematical punctuation is annotated via the latexml-generated lexemes.
* words are downcased, while math content is kept cased, to mitigate lexical ambiguity.
+ * an update was provided on Sep 29, 2019, improving tokenization of apostrophe-including constructs such as `'s`.
* [GloVe repository at sha 07d59d](https://github.com/stanfordnlp/GloVe/tree/07d59d5e6584e27ec758080bba8b51fce30f69d8)
* build/vocab_count -min-count 5
@@ -91,8 +92,8 @@ Please cite the main dataset when using the word embeddings, as they are generat
#### GloVe in-built evaluation (non-expert tasks e.g. language, relationships, geography)
1. NEW; 2019 model
- * Total accuracy: 38.30% (7017/18322)
- * Highest score: "gram3-comparative.txt", 78.60% (1047/1332)
+ * Total accuracy: 37.76% (7017/18322)
+ * Highest score: "gram3-comparative.txt", 77.33% (1047/1332)
2. 2018 [GloVe embeddings](/resources/arxmliv-embeddings-082018/)
@@ -110,23 +111,23 @@ python2 eval/python/word_analogy.py --vocab_file vocab.arxmliv.txt --vectors_fil
```
1. `abelian` is to `group` as `disjoint` is to `?`
- * Top hit: `union`, cosine distance `0.618853`
+ * Top hit: `union`, cosine distance `0.649029`
2. `convex` is to `concave` as `positive` is to `?`
- * Top hit: `negative`, cosine distance `0.806679`
+ * Top hit: `negative`, cosine distance `0.812031`
3. `finite` is to `infinte` as `abelian` is to `?`
- * Top hit: `nonabelian`, cosine distance `0.698089`
+ * Top hit: `nonabelian`, cosine distance `0.689419`
4. `quantum` is to `classical` as `bottom` is to `?`
- * Top hit: `middle`, cosine distance `0.769180`
- * Close second: `top`, cosine distance `0.765937`
+ * Top hit: `middle`, cosine distance `0.770132`
+ * Close second: `top`, cosine distance `0.758245`
5. `eq` is to `proves` as `figure` is to `?`
- * Top hit: `showing`, cosine distance `0.689938`
+ * Top hit: `shows`, cosine distance `0.675003`
6. `italic_x` is to `italic_y` as `italic_a` is to `?`
- * Top hit: `italic_b`, cosine distance `0.915467`
+ * Top hit: `italic_b`, cosine distance `0.912827`
#### Nearest word vectors
@@ -139,81 +140,92 @@ python2 eval/python/distance.py --vocab_file vocab.arxmliv.txt --vectors_file gl
```
Word: lattice Position in vocabulary: 515
- Word Cosine distance
+ Word Cosine distance
---------------------------------------------------------
- lattices 0.865888
+ lattices 0.860839
- honeycomb 0.677004
+ finite 0.662110
- finite 0.650216
+ honeycomb 0.657155
- triangular 0.632165
+ crystal 0.635061
- crystal 0.627800
+ triangular 0.632298
- sublattice 0.619792
+ spacing 0.619840
- cubic 0.609822
+ square 0.613936
+ sublattice 0.612161
+
+ hexagonal 0.606321
+
+ hypercubic 0.606101
+
+ latter 0.602747
+
+ symmetry 0.601192
+
+ cubic 0.601035
```
2. **entanglement**
```
- Word: entanglement Position in vocabulary: 1603
+ Word: entanglement Position in vocabulary: 1605
- Word Cosine distance
+ Word Cosine distance
---------------------------------------------------------
- entangled 0.803443
+ entangled 0.795067
+
+ multipartite 0.745711
- multipartite 0.744602
+ concurrence 0.708164
- negativity 0.698730
+ negativity 0.695089
- concurrence 0.693703
+ quantum 0.666254
- tripartite 0.669840
+ tripartite 0.653771
- discord 0.660572
+ fidelity 0.651990
- fidelity 0.657391
+ teleportation 0.639430
- quantum 0.655452
+ nonlocality 0.626717
- teleportation 0.628923
+ discord 0.622995
- qubits 0.627504
+ qubit 0.622836
- bipartite 0.622791
+ bipartite 0.614907
- entangling 0.621139
+ qubits 0.613029
- nonlocality 0.619905
+ entropy 0.612276
- qubit 0.615623
- entropy 0.601869
```
3. **forgetful**
```
- Word: forgetful Position in vocabulary: 12259
+ Word: forgetful Position in vocabulary: 12229
- Word Cosine distance
+ Word Cosine distance
---------------------------------------------------------
- functor 0.749501
+ functor 0.731004
- functors 0.686806
+ functors 0.667090
- morphism 0.632394
+ morphisms 0.605955
- morphisms 0.610589
+ morphism 0.604947
```
@@ -221,80 +233,88 @@ python2 eval/python/distance.py --vocab_file vocab.arxmliv.txt --vectors_file gl
```
Word: eigenvalue Position in vocabulary: 1527
- Word Cosine distance
+ Word Cosine distance
---------------------------------------------------------
- eigenvalues 0.903885
+ eigenvalues 0.894346
- eigenvector 0.781512
+ eigenvector 0.775584
- eigenvectors 0.774260
+ eigenfunction 0.772961
- eigenfunction 0.751316
+ eigenvectors 0.762914
- eigenfunctions 0.707166
+ eigenfunctions 0.700270
- eigenspace 0.683321
+ eigenspace 0.686408
- eigen 0.657366
+ eigen 0.664881
- laplacian 0.649859
+ laplacian 0.646244
- matrix 0.645466
+ eigenstate 0.629338
- eigenmode 0.628024
+ eigenmode 0.626229
- operator 0.620245
+ largest 0.620355
- eigenmodes 0.610912
+ matrix 0.618085
- largest 0.607076
+ eigenmodes 0.605928
+
+ operator 0.602806
+
+ smallest 0.600443
- eigenstates 0.603603
```
5. **riemannian**
```
- Word: riemannian Position in vocabulary: 2285
+ Word: riemannian Position in vocabulary: 2428
- Word Cosine distance
+ Word Cosine distance
---------------------------------------------------------
- manifold 0.771125
+ manifolds 0.780788
+
+ manifold 0.771704
+
+ metric 0.725227
+
+ finsler 0.686441
- manifolds 0.770408
+ ricci 0.678393
- metric 0.709820
+ curvature 0.677207
- finsler 0.699053
+ metrics 0.660825
- curvature 0.672640
+ euclidean 0.659125
- ricci 0.667813
+ noncompact 0.647109
- riemmanian 0.661929
+ conformally 0.643647
- euclidean 0.645167
+ riemmanian 0.641671
- metrics 0.641648
+ submanifold 0.632707
- submanifold 0.638131
+ kahler 0.623857
- kahler 0.635828
+ geodesic 0.621973
- riemanian 0.626252
+ submanifolds 0.617170
- noncompact 0.623363
+ endowed 0.616036
- geodesic 0.620316
+ riemanian 0.608523
- submanifolds 0.613058
+ hyperbolic 0.603709
- endowed 0.608804
+ submersion 0.600120
- foliation 0.601818
```
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