4cprotect.v.01, defend.v.02, inject.v.01, administer.v.04
[HTML]EFEFEF fastText (baseline)               fastText (DWRank)
c]@c@    AAEME triplet loss
(fastText + word2vec +
GloVe)  [HTML]EFEFEF        c]@c@ AAEME
(fastText + word2vec +
GloVe + TADW)
[HTML]EFEFEF indoctrinate.v.01              indoctrinate.v.01                   [HTML]EFEFEF teach.v.01                  inject.v.01

[HTML]EFEFEFinject.v.01      protect.v.01       [HTML]EFEFEFtreat.v.01      remove.v.01

defend.v.02       [HTML]EFEFEF insert.v.02                                                               inform.v.01

[HTML]EFEFEFremove.v.01             teach.v.01     [HTML]EFEFEFprevent.v.01                                                             change.v.01

[HTML]EFEFEFtreat.v.01          prevent.v.02            [HTML]EFEFEF    isolate.v.01                                            better.v.02

[HTML]EFEFEF destroy.v.01              inject.v.01        [HTML]EFEFEFinoculate.v.01       insert.v.02

[HTML]EFEFEF   prevent.v.02                  insert.v.02             [HTML]EFEFEF indoctrinate.v.01                                                          defend.v.02

[HTML]EFEFEFinsert.v.02                  defend.v.01               [HTML]EFEFEF kill.v.01                                                                 kill.v.01

[HTML]EFEFEF     teach.v.01           kill.v.01                [HTML]EFEFEF        change.v.01                               indoctrinate.v.01

[HTML]EFEFEFadminister.v.04          discriminate.v.02               [HTML]EFEFEF enable.v.01                                                         protect.v.01

graphSAGE     [HTML]EFEFEFTADW
Poincaré     [HTML]EFEFEF node2vec
defend.v.02     [HTML]EFEFEFprotect.v.01                                                    teach.v.01                                      [HTML]EFEFEFreceive.v.01

protect.v.01     [HTML]EFEFEF  indoctrinate.v.01          insert.v.02        [HTML]EFEFEFinsert.v.02

act.v.01        [HTML]EFEFEFteach.v.01                                                                   inform.v.01        [HTML]EFEFEFstay_in_place.v.01

negociate.v.01     [HTML]EFEFEF defend.v.02                                                                     treat.v.01      [HTML]EFEFEF get.v.01

attack.v.03     [HTML]EFEFEF prevent.v.02                                              train.v.01              [HTML]EFEFEF inject.v.01

prevent.v.02     [HTML]EFEFEF inject.v.01      deceive.v.02          [HTML]EFEFEFfall_asleep.v.01

insert.v.02      [HTML]EFEFEFprevent.v.01                                                                indoctrinate.v.01            [HTML]EFEFEF accept.v.02

demilitarize.v.01      [HTML]EFEFEF insert.v.02                                                      misinform.v.01       [HTML]EFEFEFsettle.v.04

disarm.v.02     [HTML]EFEFEFisolate.v.01                                                             gull.v.02                                 [HTML]EFEFEF resettle.v.01

foreswear.v.02     [HTML]EFEFEF discriminate.v.02                                                                  interact.v.01           [HTML]EFEFEF  discover.v.07

MAP scores for the taxonomy enrichment methods for the English datasets. Numbers in bold show the best model within the category, underlined numbers denote the best score across all the models. The combination of word embeddings (fastText, word2vec, GloVe) is denoted as words.

1c|2*method                                         3c|Nouns                                                                   3cVerbs

1c|                                                                1c1.6-3.0     1c1.7-3.0     1c|2.0-3.0     1c1.6-3.0     1c1.7-3.0     1c2.0-3.0

7cBaseline [6]

fastText [71]
0.338$\pm$0.002                 0.371$\pm$0.002                 0.400$\pm$0.004                 0.270$\pm$0.007                0.203$\pm$0.010                0.236$\pm$0.011

word2vec    [72]                                                                        0.142$\pm$0.001                          0.178$\pm$0.002               0.164$\pm$0.004           0.229$\pm$0.006       0.155$\pm$0.008          0.212$\pm$0.009

GloVe     [39]                                                                        0.232$\pm$0.002                          0.188$\pm$0.001               0.233$\pm$0.004            0.146$\pm$0.005       0.149$\pm$0.008          0.191$\pm$0.010

fastText [71]    0.314$\pm$0.001   0.373$\pm$0.003   0.418$\pm$0.004   0.286$\pm$0.007   0.218$\pm$0.008   0.254$\pm$0.012

word2vec [72]    0.244$\pm$0.001   0.271$\pm$0.003   0.298$\pm$0.004   0.099$\pm$0.005   0.118$\pm$0.008   0.141$\pm$0.010

GloVe [39]    0.283$\pm$0.001   0.329$\pm$0.003   0.377$\pm$0.004   0.182$\pm$0.007   0.159$\pm$0.008   0.203$\pm$0.011

7cDWRank-Meta (Meta-embeddings based on Word Embeddings)

concat (words)    0.335$\pm$0.001   0.386$\pm$0.003   0.386$\pm$0.003   0.270$\pm$0.007   0.194$\pm$0.009   0.226$\pm$0.011

SVD (words)     0.333$\pm$0.001   0.399$\pm$0.003   0.456$\pm$0.004   0.277$\pm$0.007   0.209$\pm$0.010   0.264$\pm$0.012

CAEMEwords)     0.321$\pm$0.001   0.386$\pm$0.003   0.448$\pm$0.005   0.278$\pm$0.007   0.205$\pm$0.008   0.266$\pm$0.015

AAEME (words)    0.322$\pm$0.001   0.384$\pm$0.003   0.453$\pm$0.004   0.271$\pm$0.007   0.218$\pm$0.008   0.273$\pm$0.012

CAEME triplet loss (words)     0.332$\pm$0.001   0.394$\pm$0.003   0.451$\pm$0.004   0.273$\pm$0.007   0.205$\pm$0.007   0.256$\pm$0.013

AAEME triplet loss (words)     0.335$\pm$0.001   0.391$\pm$0.003   0.453$\pm$0.004   0.280$\pm$0.008   0.212$\pm$0.007   0.262$\pm$0.014

GCN [62]     0.175$\pm$0.001   0.249$\pm$0.002   0.267$\pm$0.002   0.162$\pm$0.006   0.113$\pm$0.005   0.149$\pm$0.010

GAT [63]    0.000$\pm$0.000   0.252$\pm$0.002   0.000$\pm$0.000   0.081$\pm$0.003   0.064$\pm$0.004   0.000$\pm$0.000

GraphSAGE [79]    0.214$\pm$0.001   0.282$\pm$0.002   0.224$\pm$0.003   0.127$\pm$0.004   0.114$\pm$0.004   0.090$\pm$0.008

TADW [81] (on fastText)    0.350$\pm$0.001                          0.392$\pm$0.002                         0.435$\pm$0.004    0.268$\pm$0.007                          0.201$\pm$0.007                         0.217$\pm$0.010

Poincaré [76] (top-5 fastText associates)   0.185$\pm$0.001   0.211$\pm$0.002   0.229$\pm$0.002   0.208$\pm$0.006   0.147$\pm$0.006   0.172$\pm$0.012

node2vec [52] (top-5 fastText associates)   0.270$\pm$0.001   0.312$\pm$0.002   0.341$\pm$0.004   0.175$\pm$0.006   0.128$\pm$0.007   0.118$\pm$0.012

HOPE [83]      0.000$\pm$0.000                          0.000$\pm$0.000                          0.000$\pm$0.000                          0.000$\pm$0.000                          0.000$\pm$0.000                          0.000$\pm$0.000

7cDWRank-Meta (Meta-embeddings based on Word and Graph Embeddings)

SVD (words + node2vec)     0.343$\pm$0.001   0.383$\pm$0.003   0.434$\pm$0.005   0.272$\pm$0.006   0.194$\pm$0.009   0.239$\pm$0.011

CAEME (words + node2vec)     0.335$\pm$0.001   0.379$\pm$0.003   0.426$\pm$0.004   0.242$\pm$0.005   0.184$\pm$0.009   0.221$\pm$0.012

AAEME (words + node2vec)     0.350$\pm$0.001   0.394$\pm$0.003   0.446$\pm$0.004   0.252$\pm$0.007   0.184$\pm$0.008   0.208$\pm$0.012

SVD (words + TADW)     0.355$\pm$0.001   0.414$\pm$0.003   0.472$\pm$0.004    0.288$\pm$0.007    0.222$\pm$0.009   0.280$\pm$0.013

CAEME (words + TADW)     0.350$\pm$0.001   0.404$\pm$0.003   0.458$\pm$0.004   0.267$\pm$0.007   0.212$\pm$0.007   0.247$\pm$0.011

AAEME (words + TADW)      0.367$\pm$0.001   0.418$\pm$0.002    0.480$\pm$0.004   0.283$\pm$0.007   0.227$\pm$0.007   0.260$\pm$0.012

SVD (words + GCN)    0.323$\pm$0.001   0.385$\pm$0.003   0.443$\pm$0.004   0.260$\pm$0.005   0.209$\pm$0.009   0.249$\pm$0.011

CAEME (words + GCN)    0.331$\pm$0.001   0.395$\pm$0.003   0.457$\pm$0.004   0.251$\pm$0.006   0.207$\pm$0.009   0.235$\pm$0.012

AAEME (words + GCN)    0.331$\pm$0.001   0.392$\pm$0.003   0.456$\pm$0.004   0.243$\pm$0.006   0.200$\pm$0.008   0.228$\pm$0.012

SVD (words + GraphSAGE)     0.338$\pm$0.001   0.401$\pm$0.003   0.464$\pm$0.004   0.239$\pm$0.006   0.194$\pm$0.009   0.221$\pm$0.011

CAEME (words + GraphSAGE)    0.323$\pm$0.001   0.382$\pm$0.003   0.435$\pm$0.004   0.200$\pm$0.006   0.170$\pm$0.007   0.202$\pm$0.01

AAEME (words + GraphSAGE)    0.343$\pm$0.001   0.406$\pm$0.003   0.468$\pm$0.004   0.238$\pm$0.007   0.178$\pm$0.008   0.209$\pm$0.011

7cState-of-the-art Approaches

WBSR (Top-1 RUSSE'2020 for nouns)                                                                  1r0.333$\pm$0.002                  0.393$\pm$0.003              0.436$\pm$0.003                   0.252$\pm$0.006      0.206$\pm$0.011      0.252$\pm$0.013

l]@l@WBSR,                   no search engine features                             1r0.251$\pm$0.001                  0.309$\pm$0.003              0.344$\pm$0.004                   0.231$\pm$0.006     0.180$\pm$0.008     0.222$\pm$0.009

hypo2path rev [67]                     0.264$\pm$0.001                                 0.283$\pm$0.003            0.238$\pm$0.007                0.173$\pm$0.005     0.104$\pm$0.008       0.118$\pm$0.009

hypo2path [67]                           0.252$\pm$0.002                             0.261$\pm$0.002              0.208$\pm$0.006           0.162$\pm$0.005     0.093$\pm$0.006         0.067$\pm$0.008

hypo2path transformer                           0.218$\pm$0.002                             0.229$\pm$0.002              0.057$\pm$0.002          0.140$\pm$0.003       0.120$\pm$0.006        0.100$\pm$0.008
TaxoExpan [61]          0.004$\pm$0.000       0.003$\pm$0.000      0.054$\pm$0.002       0.001$\pm$0.000      0.000$\pm$0.000      0.000$\pm$0.000

MAP scores for the taxonomy enrichment methods for the Russian datasets. Numbers in bold show the best model within the category, underlined numbers denote the best score across all the models.

1c|2*method                                         2c|nouns                                2cverbs

1c|                                                                1cnon-restricted     restricted             1cnon-restricted      restricted

fastText [71]                                                                     0.414$\pm$0.001                                0.549$\pm$0.006             0.296$\pm$0.004            0.389$\pm$0.011

word2vec [72]                                                                    0.263$\pm$0.001                                0.427$\pm$0.006             0.343$\pm$0.004              0.445$\pm$0.013

5cDWRank (Word Embeddings)

fastText [71]   0.419$\pm$0.001   0.572$\pm$0.005   0.337$\pm$0.003   0.428$\pm$0.007

word2vec [72]   0.296$\pm$0.002   0.569$\pm$0.005   0.250$\pm $0.003   0.284$\pm$0.011

5cDWRank (Meta-embeddings based on Word Embeddings)

concat (words)   0.422$\pm$0.001   0.589$\pm$0.005   0.351$\pm$0.004   0.426$\pm$0.009

SVD (words)   0.461$\pm$0.001   0.600$\pm$0.005   0.426$\pm$0.005   0.475$\pm$0.010

CAEME (words)    0.400$\pm$0.001   0.561$\pm$0.005   0.342$\pm$0.003   0.416$\pm$0.008

AAEME (words)    0.456$\pm$0.001   0.582$\pm$0.005   0.368$\pm$0.004   0.442$\pm$0.009

CAEME triplet loss (words)   0.449$\pm$0.001   0.581$\pm$0.005   0.374$\pm$0.003   0.427$\pm$0.010

AAEME triplet loss (words)   0.474$\pm$0.001   0.593$\pm$0.006   0.399$\pm$0.004   0.449$\pm$0.010

5cDWRank (Graph embeddings)

GCN [57]   0.183$\pm$0.001   0.306$\pm$0.005   0.220$\pm$0.003   0.287$\pm$0.009

GAT [63]   0.142$\pm$0.001   0.318$\pm$0.004   0.000$\pm$0.000   0.000$\pm$0.000

GraphSAGE [79]   0.176$\pm$0.001   0.348$\pm$0.005   0.181$\pm$0.003   0.226$\pm$0.008

TADW [81]     0.417$\pm$0.001   0.562$\pm$0.005   0.328$\pm$0.003   0.423$\pm$0.008

Poincaré [76] (top-5 fastText associates)   0.336$\pm$0.001   0.476$\pm$0.005   0.244$\pm$0.004   0.339$\pm$0.009

node2vec [52] (top-5 fastText associates)   0.343$\pm$0.002   0.477$\pm$0.005   0.226$\pm$0.003   0.322$\pm$0.010

HOPE [83]    0.000$\pm$0.000                                   0.000$\pm$0.000            0.003$\pm$0.001            0.003$\pm$0.001

5cDWRank (Meta-embeddings based on Word and Graph Embeddings)

SVD (words + node2vec)   0.367$\pm$0.001   0.521$\pm$0.005   0.252$\pm$0.003   0.351$\pm$0.010

CAEME (words + node2vec)   0.370$\pm$0.001   0.533$\pm$0.005   0.267$\pm$0.003   0.362$\pm$0.010

AAEME (words + node2vec)   0.373$\pm$0.001   0.529$\pm$0.005   0.272$\pm$0.003   0.358$\pm$0.010

SVD (words + TADW)   0.469$\pm$0.001   0.604$\pm$0.006   0.394$\pm$0.005   0.455$\pm$0.010

CAEME (words + TADW)   0.429$\pm$0.001   0.571$\pm$0.005   0.349$\pm$0.003   0.437$\pm$0.009

AAEME (words + TADW)   0.461$\pm$0.001   0.584$\pm$0.005   0.362$\pm$0.004   0.439$\pm$0.009

SVD (words + GCN)   0.395$\pm$0.001   0.554$\pm$0.005   0.291$\pm$0.004   0.356$\pm$0.009

CAEME (words + GCN)   0.389$\pm$0.001   0.544$\pm$0.005   0.302$\pm$0.003   0.381$\pm$0.008

AAEME (words + GCN)   0.386$\pm$0.001   0.545$\pm$0.006   0.295$\pm$0.004   0.365$\pm$0.008

SVD (words + GraphSAGE)   0.410$\pm$0.001   0.603$\pm$0.005   0.336$\pm$0.004   0.426$\pm$0.009

CAEME (words + GraphSAGE)   0.321$\pm$0.001   0.541$\pm$0.005   0.266$\pm$0.004   0.345$\pm$0.007

AAEME (words + GraphSAGE)   0.409$\pm$0.001   0.577$\pm$0.006   0.323$\pm$0.004   0.419$\pm$0.009

5cState-of-the-art Approaches

WBSR (Top-1 RUSSE'2020 for nouns)                1c0.393$\pm$0.002                  0.552$\pm$0.005              0.293$\pm$0.004                   0.428$\pm$0.010

WBSR, no search engine features                                                                   1c0.369$\pm$0.002                  0.497$\pm$0.005              0.267$\pm$0.004                   0.387$\pm$0.009

Top-1 RUSSE'2020 for verbs: [33]                                                                     1c0.288$\pm$0.001                  0.418$\pm$0.006             0.341$\pm$0.004                  0.452$\pm$0.012

hypo2path [67]                    0.061$\pm$0.000                           0.097$\pm$0.002                 0.137$\pm$0.003            0.174$\pm$0.009

hypo2path rev [67]                       0.246$\pm$0.001                            0.342$\pm$0.006             0.151$\pm$0.003         0.194$\pm$0.008

hypo2path rev transformer [67]                    0.234$\pm$0.001                            0.331$\pm$0.004               0.152$\pm$0.003            0.201$\pm$0.008

TaxoExpan [61]           0.007$\pm$0.000       0.006$\pm$0.001       0.009$\pm$0.001      0.008$\pm$0.002