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arxmliv-embeddings-082017.md

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    title: arXMLiv 08.2017 - Word Embeddings; Token Model

    Part of the arXMLiv project at the KWARC research group

    Author

    • Deyan Ginev

    Current release

    • 08.2017

    Accessibility and License

    The content of this Dataset is licensed to SIGMathLing members for research and tool development purposes.

    Access is restricted to SIGMathLing members under the SIGMathLing Non-Disclosure-Agreement as for most arXiv articles, the right of distribution was only given (or assumed) to arXiv itself.

    Contents

    • A 5 billion token model for the arXMLiv 08.2017 dataset
      • token_model.zip
    • 300 dimensional GloVe word embeddings for the arXMLiv 08.2017 dataset
      • glove.arxmliv.5B.300d.zip and vocab.arxmliv.zip
    • 300d GloVe word embeddings for individual subsets
      • glove.subsets.zip
    • the main arXMLiv dataset is available separately here

    Token Model Statistics

    subset documents paragraphs sentences
    no_problem 112,088 3,760,015 17,684,762
    warning 574,638 35,215,866 144,166,524
    error 401,644 28,555,173 111,798,273
    complete 1,088,370 67,531,054 273,649,559
    subset words formulas inline cite numeric literals
    no_problem 355,253,671 17,020,161 2,991,053 9,913,009
    warning 2,514,340,590 219,167,820 20,163,304 65,294,846
    error 1,946,207,151 169,247,016 14,458,082 51,730,645
    complete 4,815,801,412 405,434,997 37,612,439 126,938,500

    GloVe Model Statistics

    subset tokens unique words unique words (freq 5+ )
    no_problem 384,951,086 490,134 170,615
    warning 2,817,734,902 1,200,887 422,524
    error 2,180,119,361 1,889,392 518,609
    complete 5,382,805,349 2,573,974 746,673

    Citing this Resource

    Please cite the main dataset when using the word embeddings, as they are generated and distributed jointly. Instructions here

    Download

    Download link (SIGMathLing members only)

    Generated via

    Generation Parameters

    • token model distributed as 3 subsets - no_problem, warning and error. complete model is derived via:

        cat token_model_no_problem.txt \
            token_model_warning.txt \
            token_model_error.txt > token_model_complete.txt
    • llamapun v0.1, corpus_token_model example used for token model extraction

      • used llamapun math-aware sentence and word tokenization
      • processed logical paragraphs only (excluded non-textual modalities, e.g. tables, figures and their captions, bibliographies)
      • marked up formulas replaced with mathformula token
      • marked up inline citations replaced with citationelement token
      • numeric literals replaced with NUM token
      • ignored sentences with unnaturally long words (>30 characters) - almost always due to latexml conversion errors
    • GloVe repository at sha 76507

      • build/vocab_count -min-count 5
      • build/cooccur -memory 32.0 -window-size 15
      • build/shuffle -memory 32.0
      • build/glove -threads 16 -x-max 100 -iter 25 -vector-size 300 -binary 2

    Examples and baselines

    GloVe in-built evaluation (non-expert tasks e.g. language, relationships, geography)

    1. no_problem
    • Total accuracy: 26.49% (3665/13833)
    • Highest score: "gram3-comparative.txt", 75.83% (1010/1332)
    1. warning
    • Total accuracy: 31.16% (4989/16013)
    • Highest score: "gram3-comparative.txt", 75.45% (1005/1332)
    1. error
    • Total accuracy: 29.63% (4997/16867)
    • Highest score: "gram3-comparative.txt", 76.58% (1020/1332)
    1. complete
    • Total accuracy: 32.86% (5770/17562)
    • Highest score: "gram3-comparative.txt", 78.53% (1046/1332)
    1. demo baseline: text8 demo (first 100M characters of Wikipedia)
    • Total accuracy: 23.91% (4262/17827)
    • Highest score: "capital-common-countries.txt", 62.65% (317/506)

    Evaluation note: These in-built evlauation runs are provided as a sanity check that the generated GloVe models pass a basic baseline against the non-expert tasks in the default GloVe suite. One would need a scienctific discourse tailored set of test cases to evaluate the arXiv-based models competitively.

    Measuring word analogy

    In a cloned GloVe repository, start via:

    python eval/python/word_analogy.py --vocab_file vocab.arxmliv.txt --vectors_file glove.arxmliv.5B.300d.txt 
    1. abelian is to group as disjoint is to ?
    • Top hit: union, cosine distance 0.644784
    1. convex is to concave as positive is to ?
    • Top hit: negative, cosine distance 0.802866
    1. finite is to infinite as abelian is to ?
    • Top hit: nonabelian, cosine distance 0.664235
    1. quantum is to classical as bottom is to ?
    • Top hit: top, cosine distance 0.719843
    1. eq is to proves as figure is to ?
    • Top hit: shows, cosine distance 0.674743

    Nearest word vectors

    In a cloned GloVe repository, start via:

    python eval/python/distance.py --vocab_file vocab.arxmliv.txt --vectors_file glove.arxmliv.5B.300d.txt 
    1. lattice

      Word: lattice  Position in vocabulary: 311
      
                                     Word   Cosine distance
      
      -----------------------------------------------------
      
                                 lattices		0.811057
      
                                honeycomb		0.657262
      
                                   finite		0.625146
      
                               triangular		0.608218
      
                                  spacing		0.605435
    2. entanglement

      Word: entanglement  Position in vocabulary: 1293
      
                                     Word   Cosine distance
      
      -----------------------------------------------------
      
                                entangled		0.763964
      
                             multipartite		0.730231
      
                                 fidelity		0.653443
      
                              concurrence		0.652454
      
                            environemtnal		0.646705
      
                               negativity		0.646165
      
                                  quantum		0.639032
      
                                  discord		0.624222
      
                              nonlocality		0.610661
      
                               tripartite		0.609896
    3. forgetful

      Word: forgetful  Position in vocabulary: 10697
      
                                     Word   Cosine distance
      
      -----------------------------------------------------
      
                                  functor		0.723019
      
                                 functors		0.653969
      
                                 morphism		0.626222
    4. eigenvalue

      Word: eigenvalue  Position in vocabulary: 1212
      
                                     Word   Cosine distance
      
      -----------------------------------------------------
      
                              eigenvalues		0.878527
      
                              eigenvector		0.766371
      
                            eigenfunction		0.761923
      
                             eigenvectors		0.747451
      
                           eigenfunctions		0.707346
      
                               eigenspace		0.661539
      
                            corresponding		0.629746
      
                                laplacian		0.627187
      
                                 operator		0.627130
      
                                    eigen		0.620933
    5. riemannian

      Word: riemannian  Position in vocabulary: 2026
      
                                     Word   Cosine distance
      
      -----------------------------------------------------
      
                                 manifold		0.766196
      
                                manifolds		0.745785
      
                                   metric		0.714120
      
                                curvature		0.672975
      
                                  metrics		0.670006
      
                                  finsler		0.665079
      
                                    ricci		0.657058
      
                                euclidean		0.650198
      
                                  endowed		0.626307
      
                               riemmanian		0.621626
      
                                riemanian		0.618022