-
Notifications
You must be signed in to change notification settings - Fork 0
/
phdThesis.bbl
5489 lines (5486 loc) · 243 KB
/
phdThesis.bbl
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
% $ biblatex auxiliary file $
% $ biblatex bbl format version 3.1 $
% Do not modify the above lines!
%
% This is an auxiliary file used by the 'biblatex' package.
% This file may safely be deleted. It will be recreated by
% biber as required.
%
\begingroup
\makeatletter
\@ifundefined{[email protected]}
{\@latex@error
{Missing 'biblatex' package}
{The bibliography requires the 'biblatex' package.}
\aftergroup\endinput}
{}
\endgroup
\refsection{0}
\datalist[entry]{nyt/global//global/global}
\entry{agirre.edmonds_2007a}{book}{}
\name{editor}{2}{}{%
{{un=0,uniquepart=base,hash=39f0a6a48ac08d60ef7340dad900caa8}{%
family={Agirre},
familyi={A\bibinitperiod},
given={Eneko},
giveni={E\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=24b98d30587be519af4b9a570ac168d0}{%
family={Edmonds},
familyi={E\bibinitperiod},
given={Philip},
giveni={P\bibinitperiod},
givenun=0}}%
}
\list{publisher}{1}{%
{Springer}%
}
\strng{namehash}{155be9fdcb76b8c710c8a3e286f1f508}
\strng{fullhash}{155be9fdcb76b8c710c8a3e286f1f508}
\strng{bibnamehash}{155be9fdcb76b8c710c8a3e286f1f508}
\strng{editorbibnamehash}{155be9fdcb76b8c710c8a3e286f1f508}
\strng{editornamehash}{155be9fdcb76b8c710c8a3e286f1f508}
\strng{editorfullhash}{155be9fdcb76b8c710c8a3e286f1f508}
\field{sortinit}{A}
\field{sortinithash}{2f401846e2029bad6b3ecc16d50031e2}
\field{extradatescope}{labelyear}
\field{labeldatesource}{}
\field{labelnamesource}{editor}
\field{labeltitlesource}{title}
\field{isbn}{9781402048084}
\field{langid}{english}
\field{series}{Text, Speech, and Language Technology}
\field{title}{Word {{Sense Disambiguation}}. {{Algorithms}} and {{Applications}}}
\field{volume}{33}
\field{year}{2007}
\field{dateera}{ce}
\endentry
\entry{arppe.etal_2010}{article}{}
\name{author}{5}{}{%
{{un=0,uniquepart=base,hash=96602c29717a4f4a85f6fdd7c7fcbfe2}{%
family={Arppe},
familyi={A\bibinitperiod},
given={Antti},
giveni={A\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=f4d029a014c1cbdcf8821664dd962c33}{%
family={Gilquin},
familyi={G\bibinitperiod},
given={Gaëtanelle},
giveni={G\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=72da2ce9309e6c355950843c12cf9ec6}{%
family={Glynn},
familyi={G\bibinitperiod},
given={Dylan},
giveni={D\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=f210829aff5abe5e67f4e62929cd6c17}{%
family={Hilpert},
familyi={H\bibinitperiod},
given={Martin},
giveni={M\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=4adf664c82f2d8c63cd1ef1f82e78858}{%
family={Zeschel},
familyi={Z\bibinitperiod},
given={Arne},
giveni={A\bibinitperiod},
givenun=0}}%
}
\strng{namehash}{7c1c82ec3c4b8464df93d42e7eb828c0}
\strng{fullhash}{d7428dc15641055198a567d5ad6464b0}
\strng{bibnamehash}{d7428dc15641055198a567d5ad6464b0}
\strng{authorbibnamehash}{d7428dc15641055198a567d5ad6464b0}
\strng{authornamehash}{7c1c82ec3c4b8464df93d42e7eb828c0}
\strng{authorfullhash}{d7428dc15641055198a567d5ad6464b0}
\field{sortinit}{A}
\field{sortinithash}{2f401846e2029bad6b3ecc16d50031e2}
\field{extradatescope}{labelyear}
\field{labeldatesource}{}
\field{labelnamesource}{author}
\field{labeltitlesource}{shorttitle}
\field{issn}{1749-5032, 1755-1676}
\field{journaltitle}{Corpora}
\field{langid}{english}
\field{month}{5}
\field{number}{1}
\field{shorttitle}{Cognitive {{Corpus Linguistics}}}
\field{title}{Cognitive {{Corpus Linguistics}}: Five Points of Debate on Current Theory and Methodology}
\field{volume}{5}
\field{year}{2010}
\field{dateera}{ce}
\field{pages}{1\bibrangedash 27}
\range{pages}{27}
\endentry
\entry{barcelona_2015}{incollection}{}
\name{author}{1}{}{%
{{un=0,uniquepart=base,hash=6610c1425c4b3a23d9fa1e4c3980e767}{%
family={Barcelona},
familyi={B\bibinitperiod},
given={Antonio},
giveni={A\bibinitperiod},
givenun=0}}%
}
\name{editor}{2}{}{%
{{hash=fa7fe3238711c38ea5e24bd3bd0f9ce8}{%
family={Dabrowska},
familyi={D\bibinitperiod},
given={Ewa},
giveni={E\bibinitperiod}}}%
{{hash=d7136499116ac5c94046cca179066f44}{%
family={Divjak},
familyi={D\bibinitperiod},
given={Dagmar},
giveni={D\bibinitperiod}}}%
}
\list{location}{1}{%
{Berlin; München; Boston}%
}
\list{publisher}{1}{%
{De Gruyter}%
}
\strng{namehash}{6610c1425c4b3a23d9fa1e4c3980e767}
\strng{fullhash}{6610c1425c4b3a23d9fa1e4c3980e767}
\strng{bibnamehash}{6610c1425c4b3a23d9fa1e4c3980e767}
\strng{authorbibnamehash}{6610c1425c4b3a23d9fa1e4c3980e767}
\strng{authornamehash}{6610c1425c4b3a23d9fa1e4c3980e767}
\strng{authorfullhash}{6610c1425c4b3a23d9fa1e4c3980e767}
\strng{editorbibnamehash}{ffe058cfa970f3acfa738467bd4a4d99}
\strng{editornamehash}{ffe058cfa970f3acfa738467bd4a4d99}
\strng{editorfullhash}{ffe058cfa970f3acfa738467bd4a4d99}
\field{sortinit}{B}
\field{sortinithash}{d7095fff47cda75ca2589920aae98399}
\field{extradatescope}{labelyear}
\field{labeldatesource}{}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{booktitle}{Handbook of {{Cognitive Linguistics}}}
\field{day}{31}
\field{isbn}{978-3-11-029202-2}
\field{month}{1}
\field{title}{Metonymy}
\field{year}{2015}
\field{dateera}{ce}
\field{pages}{143\bibrangedash 167}
\range{pages}{25}
\endentry
\entry{baroni.etal_2014}{inproceedings}{}
\name{author}{3}{}{%
{{un=0,uniquepart=base,hash=06fd48b6938d182f11c5bf4e2da76f87}{%
family={Baroni},
familyi={B\bibinitperiod},
given={Marco},
giveni={M\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=ae781f5d08ccad40b8a4e1b1e10c0310}{%
family={Dinu},
familyi={D\bibinitperiod},
given={Georgiana},
giveni={G\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=c8f9a92b64e8eece864795345f744b04}{%
family={Kruszewski},
familyi={K\bibinitperiod},
given={Germán},
giveni={G\bibinitperiod},
givenun=0}}%
}
\list{location}{1}{%
{Baltimore, Maryland}%
}
\list{publisher}{1}{%
{Association for Computational Linguistics}%
}
\strng{namehash}{b965ba19e60358733938cb1e3c08eb5b}
\strng{fullhash}{b965ba19e60358733938cb1e3c08eb5b}
\strng{bibnamehash}{b965ba19e60358733938cb1e3c08eb5b}
\strng{authorbibnamehash}{b965ba19e60358733938cb1e3c08eb5b}
\strng{authornamehash}{b965ba19e60358733938cb1e3c08eb5b}
\strng{authorfullhash}{b965ba19e60358733938cb1e3c08eb5b}
\field{sortinit}{B}
\field{sortinithash}{d7095fff47cda75ca2589920aae98399}
\field{extradatescope}{labelyear}
\field{labeldatesource}{}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{abstract}{Context-predicting models (more commonly known as embeddings or neural language models) are the new kids on the distributional semantics block. Despite the buzz surrounding these models, the literature is still lacking a systematic comparison of the predictive models with classic, count-vector-based distributional semantic approaches. In this paper, we perform such an extensive evaluation, on a wide range of lexical semantics tasks and across many parameter settings. The results, to our own surprise, show that the buzz is fully justified, as the context-predicting models obtain a thorough and resounding victory against their count-based counterparts.}
\field{booktitle}{Proceedings of the 52nd {{Annual Meeting}} of the {{Association}} for {{Computational Linguistics}} ({{Volume}} 1: {{Long Papers}})}
\field{eventtitle}{Proceedings of the 52nd {{Annual Meeting}} of the {{Association}} for {{Computational Linguistics}} ({{Volume}} 1: {{Long Papers}})}
\field{langid}{english}
\field{title}{Don't Count, Predict! {{A}} Systematic Comparison of Context-Counting vs. Context-Predicting Semantic Vectors}
\field{year}{2014}
\field{dateera}{ce}
\field{pages}{238\bibrangedash 247}
\range{pages}{10}
\verb{file}
\verb C\:\\Users\\u0118974\\Zotero\\storage\\8NE8WIHL\\Baroni et al. - 2014 - Don't count, predict! A systematic comparison of c.pdf
\endverb
\endentry
\entry{baroni.lenci_2011}{inproceedings}{}
\name{author}{2}{}{%
{{un=0,uniquepart=base,hash=06fd48b6938d182f11c5bf4e2da76f87}{%
family={Baroni},
familyi={B\bibinitperiod},
given={Marco},
giveni={M\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=5ca65e378c2b71d06107979d273315ed}{%
family={Lenci},
familyi={L\bibinitperiod},
given={Alessandro},
giveni={A\bibinitperiod},
givenun=0}}%
}
\list{location}{1}{%
{Edinburgh, Scotland, UK}%
}
\list{publisher}{1}{%
{Association for Computational Linguistics}%
}
\strng{namehash}{678ce56e249f4fcec448e9e1796fdd44}
\strng{fullhash}{678ce56e249f4fcec448e9e1796fdd44}
\strng{bibnamehash}{678ce56e249f4fcec448e9e1796fdd44}
\strng{authorbibnamehash}{678ce56e249f4fcec448e9e1796fdd44}
\strng{authornamehash}{678ce56e249f4fcec448e9e1796fdd44}
\strng{authorfullhash}{678ce56e249f4fcec448e9e1796fdd44}
\field{sortinit}{B}
\field{sortinithash}{d7095fff47cda75ca2589920aae98399}
\field{extradatescope}{labelyear}
\field{labeldatesource}{}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{abstract}{We introduce BLESS, a data set specifically designed for the evaluation of distributional semantic models. BLESS contains a set of tuples instantiating different, explicitly typed semantic relations, plus a number of controlled random tuples. It is thus possible to assess the ability of a model to detect truly related word pairs, as well as to perform in-depth analyses of the types of semantic relations that a model favors. We discuss the motivations for BLESS, describe its construction and structure, and present examples of its usage in the evaluation of distributional semantic models.}
\field{booktitle}{Proceedings of the {{GEMS}} 2011 {{Workshop}} on {{Geometrical Models}} of {{Natural Language Semantics}}}
\field{day}{31}
\field{eventtitle}{{{EMNLP}} 2011}
\field{langid}{english}
\field{month}{7}
\field{title}{How We {{BLESSed}} Distributional Semantic Evaluation}
\field{year}{2011}
\field{dateera}{ce}
\field{pages}{1\bibrangedash 10}
\range{pages}{10}
\verb{file}
\verb C\:\\Users\\u0118974\\Documents\\Bibliography\\Baroni y Lenci - How we BLESSed distributional semantic evaluation.pdf
\endverb
\endentry
\entry{bolognesi_2020}{book}{}
\name{author}{1}{}{%
{{un=0,uniquepart=base,hash=eea5a756e37ade06362b13842c243ce8}{%
family={Bolognesi},
familyi={B\bibinitperiod},
given={Marianna},
giveni={M\bibinitperiod},
givenun=0}}%
}
\list{location}{1}{%
{Amsterdam}%
}
\list{publisher}{1}{%
{John Benjamins Publishing Company}%
}
\strng{namehash}{eea5a756e37ade06362b13842c243ce8}
\strng{fullhash}{eea5a756e37ade06362b13842c243ce8}
\strng{bibnamehash}{eea5a756e37ade06362b13842c243ce8}
\strng{authorbibnamehash}{eea5a756e37ade06362b13842c243ce8}
\strng{authornamehash}{eea5a756e37ade06362b13842c243ce8}
\strng{authorfullhash}{eea5a756e37ade06362b13842c243ce8}
\field{sortinit}{B}
\field{sortinithash}{d7095fff47cda75ca2589920aae98399}
\field{extradatescope}{labelyear}
\field{labeldatesource}{}
\field{labelnamesource}{author}
\field{labeltitlesource}{shorttitle}
\field{day}{15}
\field{isbn}{978-90-272-0801-9 978-90-272-6042-0}
\field{langid}{english}
\field{month}{11}
\field{series}{Converging {{Evidence}} in {{Language}} and {{Communication Research}}}
\field{shorttitle}{Where {{Words Get}} Their {{Meaning}}}
\field{title}{Where {{Words Get}} Their {{Meaning}}: {{Cognitive}} Processing and Distributional Modelling of Word Meaning in First and Second Language}
\field{year}{2020}
\field{dateera}{ce}
\endentry
\entry{vandale_klein}{book}{}
\name{author}{3}{}{%
{{un=0,uniquepart=base,hash=6bfca4fd0fe11285905c16cb7045f29a}{%
family={Boon},
familyi={B\bibinitperiod},
given={Ton},
giveni={T\bibinitperiod},
givenun=0,
prefix={den},
prefixi={d\bibinitperiod},
prefixun=0}}%
{{un=0,uniquepart=base,hash=46219cd67a96f13b96f4e1a2dcfb9040}{%
family={Geeraerts},
familyi={G\bibinitperiod},
given={Dirk},
giveni={D\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=81f1192c28884c3e63e1c443d64f442a}{%
family={Arts},
familyi={A\bibinitperiod},
given={Marjan},
giveni={M\bibinitperiod},
givenun=0}}%
}
\list{location}{1}{%
{Utrecht}%
}
\list{publisher}{1}{%
{Van Dale}%
}
\strng{namehash}{734d35be1c175047784417be732d7753}
\strng{fullhash}{734d35be1c175047784417be732d7753}
\strng{bibnamehash}{734d35be1c175047784417be732d7753}
\strng{authorbibnamehash}{734d35be1c175047784417be732d7753}
\strng{authornamehash}{734d35be1c175047784417be732d7753}
\strng{authorfullhash}{734d35be1c175047784417be732d7753}
\field{sortinit}{B}
\field{sortinithash}{d7095fff47cda75ca2589920aae98399}
\field{extradatescope}{labelyear}
\field{labeldatesource}{}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{annotation}{OCLC: 851785979}
\field{isbn}{978-90-6648-432-0}
\field{langid}{dutch}
\field{title}{Van Dale klein woordenboek van de Nederlandse taal}
\field{year}{2007}
\field{dateera}{ce}
\endentry
\entry{bostock.etal_2011}{article}{}
\name{author}{3}{}{%
{{un=0,uniquepart=base,hash=93edefe397e68432e91458e3809ac708}{%
family={Bostock},
familyi={B\bibinitperiod},
given={M.},
giveni={M\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=6eddf9c3d02a5c17b0f893a37612027d}{%
family={Ogievetsky},
familyi={O\bibinitperiod},
given={V.},
giveni={V\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=9abad54f5a971afca28bc1c1e90bf629}{%
family={Heer},
familyi={H\bibinitperiod},
given={J.},
giveni={J\bibinitperiod},
givenun=0}}%
}
\strng{namehash}{b6947b4db4c8cb7dc002e084ed71d8d1}
\strng{fullhash}{b6947b4db4c8cb7dc002e084ed71d8d1}
\strng{bibnamehash}{b6947b4db4c8cb7dc002e084ed71d8d1}
\strng{authorbibnamehash}{b6947b4db4c8cb7dc002e084ed71d8d1}
\strng{authornamehash}{b6947b4db4c8cb7dc002e084ed71d8d1}
\strng{authorfullhash}{b6947b4db4c8cb7dc002e084ed71d8d1}
\field{sortinit}{B}
\field{sortinithash}{d7095fff47cda75ca2589920aae98399}
\field{extradatescope}{labelyear}
\field{labeldatesource}{}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{abstract}{Data-Driven Documents (D3) is a novel representation-transparent approach to visualization for the web. Rather than hide the underlying scenegraph within a toolkit-specific abstraction, D3 enables direct inspection and manipulation of a native representation: the standard document object model (DOM). With D3, designers selectively bind input data to arbitrary document elements, applying dynamic transforms to both generate and modify content. We show how representational transparency improves expressiveness and better integrates with developer tools than prior approaches, while offering comparable notational efficiency and retaining powerful declarative components. Immediate evaluation of operators further simplifies debugging and allows iterative development. Additionally, we demonstrate how D3 transforms naturally enable animation and interaction with dramatic performance improvements over intermediate representations.}
\field{issn}{1077-2626}
\field{journaltitle}{IEEE Transactions on Visualization and Computer Graphics}
\field{langid}{english}
\field{month}{12}
\field{number}{12}
\field{shortjournal}{IEEE Trans. Visual. Comput. Graphics}
\field{title}{{{D}}³ {{Data}}-{{Driven Documents}}}
\field{volume}{17}
\field{year}{2011}
\field{dateera}{ce}
\field{pages}{2301\bibrangedash 2309}
\range{pages}{9}
\verb{file}
\verb C\:\\Users\\u0118974\\Zotero\\storage\\ZK8DLG2A\\Bostock e.a. - 2011 - D³ Data-Driven Documents.pdf
\endverb
\endentry
\entry{bullinaria.levy_2007}{article}{}
\name{author}{2}{}{%
{{un=0,uniquepart=base,hash=7acf5da41fee96dbc63c96424264f6d0}{%
family={Bullinaria},
familyi={B\bibinitperiod},
given={John\bibnamedelima A.},
giveni={J\bibinitperiod\bibinitdelim A\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=bdd77c49e77aa45b039660e77eb4dba6}{%
family={Levy},
familyi={L\bibinitperiod},
given={Joseph\bibnamedelima P.},
giveni={J\bibinitperiod\bibinitdelim P\bibinitperiod},
givenun=0}}%
}
\strng{namehash}{a9d815e25ef1cf5772f1bac386996447}
\strng{fullhash}{a9d815e25ef1cf5772f1bac386996447}
\strng{bibnamehash}{a9d815e25ef1cf5772f1bac386996447}
\strng{authorbibnamehash}{a9d815e25ef1cf5772f1bac386996447}
\strng{authornamehash}{a9d815e25ef1cf5772f1bac386996447}
\strng{authorfullhash}{a9d815e25ef1cf5772f1bac386996447}
\field{sortinit}{B}
\field{sortinithash}{d7095fff47cda75ca2589920aae98399}
\field{extradatescope}{labelyear}
\field{labeldatesource}{}
\field{labelnamesource}{author}
\field{labeltitlesource}{shorttitle}
\field{abstract}{The idea that at least some aspects of word meaning can be induced from patterns of word co-occurrence is becoming increasingly popular. However, there is less agreement about the precise computations involved, and the appropriate tests to distinguish between the various possibilities. It is important that the effect of the relevant design choices and parameter values are understood if psychological models using these methods are to be reliably evaluated and compared. In this article, we present a systematic exploration of the principal computational possibilities for formulating and validating representations of word meanings from word co-occurrence statistics. We find that, once we have identified the best procedures, a very simple approach is surprisingly successful and robust over a range of psychologically relevant evaluation measures.}
\field{day}{1}
\field{issn}{1554-3528}
\field{journaltitle}{Behavior Research Methods}
\field{langid}{english}
\field{month}{8}
\field{number}{3}
\field{shortjournal}{Behavior Research Methods}
\field{shorttitle}{Extracting Semantic Representations from Word Co-Occurrence Statistics}
\field{title}{Extracting Semantic Representations from Word Co-Occurrence Statistics: {{A}} Computational Study}
\field{volume}{39}
\field{year}{2007}
\field{dateera}{ce}
\field{pages}{510\bibrangedash 526}
\range{pages}{17}
\verb{file}
\verb C\:\\Users\\u0118974\\Zotero\\storage\\INNN5B2A\\Bullinaria y Levy - 2007 - Extracting semantic representations from word co-o.pdf
\endverb
\endentry
\entry{campello.etal_2013}{inproceedings}{}
\name{author}{3}{}{%
{{un=0,uniquepart=base,hash=382d8e986b9afdbfedb661be5cf9ce33}{%
family={Campello},
familyi={C\bibinitperiod},
given={Ricardo\bibnamedelimb J.\bibnamedelimi G.\bibnamedelimi B.},
giveni={R\bibinitperiod\bibinitdelim J\bibinitperiod\bibinitdelim G\bibinitperiod\bibinitdelim B\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=8608b4a10cbfee7eb81b652407a3305a}{%
family={Moulavi},
familyi={M\bibinitperiod},
given={Davoud},
giveni={D\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=e155d1575870b682f3029b1ee02ff893}{%
family={Sander},
familyi={S\bibinitperiod},
given={Joerg},
giveni={J\bibinitperiod},
givenun=0}}%
}
\name{editor}{5}{}{%
{{hash=56af930e1f5e2c9a791b71e7b347c0ba}{%
family={Pei},
familyi={P\bibinitperiod},
given={Jian},
giveni={J\bibinitperiod}}}%
{{hash=42b42d9d10bbc93b4d7dbd5b26a8cfdf}{%
family={Tseng},
familyi={T\bibinitperiod},
given={Vincent\bibnamedelima S.},
giveni={V\bibinitperiod\bibinitdelim S\bibinitperiod}}}%
{{hash=259f81b9fe9f9737014c9eb11c08df15}{%
family={Cao},
familyi={C\bibinitperiod},
given={Longbing},
giveni={L\bibinitperiod}}}%
{{hash=d26f438bb203e0655769b9af6932fe72}{%
family={Motoda},
familyi={M\bibinitperiod},
given={Hiroshi},
giveni={H\bibinitperiod}}}%
{{hash=06023c96555b95e222d92ae0de2c2291}{%
family={Xu},
familyi={X\bibinitperiod},
given={Guandong},
giveni={G\bibinitperiod}}}%
}
\list{location}{1}{%
{Berlin, Heidelberg}%
}
\list{publisher}{1}{%
{Springer}%
}
\strng{namehash}{84276f8f7e0bd597fd1ee130e754f627}
\strng{fullhash}{84276f8f7e0bd597fd1ee130e754f627}
\strng{bibnamehash}{84276f8f7e0bd597fd1ee130e754f627}
\strng{authorbibnamehash}{84276f8f7e0bd597fd1ee130e754f627}
\strng{authornamehash}{84276f8f7e0bd597fd1ee130e754f627}
\strng{authorfullhash}{84276f8f7e0bd597fd1ee130e754f627}
\strng{editorbibnamehash}{c4e66fdf2ee9ba6df93fd5fff6f7da5e}
\strng{editornamehash}{0f7210bb6168b72933fbd7a462c750c1}
\strng{editorfullhash}{c4e66fdf2ee9ba6df93fd5fff6f7da5e}
\field{sortinit}{C}
\field{sortinithash}{4d103a86280481745c9c897c925753c0}
\field{extradatescope}{labelyear}
\field{labeldatesource}{}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{abstract}{We propose a theoretically and practically improved density-based, hierarchical clustering method, providing a clustering hierarchy from which a simplified tree of significant clusters can be constructed. For obtaining a “flat” partition consisting of only the most significant clusters (possibly corresponding to different density thresholds), we propose a novel cluster stability measure, formalize the problem of maximizing the overall stability of selected clusters, and formulate an algorithm that computes an optimal solution to this problem. We demonstrate that our approach outperforms the current, state-of-the-art, density-based clustering methods on a wide variety of real world data.}
\field{booktitle}{Advances in {{Knowledge Discovery}} and {{Data Mining}}}
\field{isbn}{978-3-642-37456-2}
\field{langid}{english}
\field{series}{Lecture {{Notes}} in {{Computer Science}}}
\field{title}{Density-{{Based Clustering Based}} on {{Hierarchical Density Estimates}}}
\field{year}{2013}
\field{dateera}{ce}
\field{pages}{160\bibrangedash 172}
\range{pages}{13}
\verb{file}
\verb C\:\\Users\\u0118974\\Zotero\\storage\\S5V7562X\\Campello et al. - 2013 - Density-Based Clustering Based on Hierarchical Den.pdf
\endverb
\keyw{Cluster Tree,Core Object,Density Threshold,Hierarchical Cluster Method,Minimum Span Tree}
\endentry
\entry{card.etal_1999}{book}{}
\name{author}{3}{}{%
{{un=0,uniquepart=base,hash=8a77110e452e26554c7c2b09868876c0}{%
family={Card},
familyi={C\bibinitperiod},
given={Stuart\bibnamedelima K.},
giveni={S\bibinitperiod\bibinitdelim K\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=a7acbc93172ff11ebe499a3f0ca83686}{%
family={Mackinlay},
familyi={M\bibinitperiod},
given={Jock\bibnamedelima D.},
giveni={J\bibinitperiod\bibinitdelim D\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=39afccc0f55ae7821df79ffd677c90b0}{%
family={Shneiderman},
familyi={S\bibinitperiod},
given={Ben},
giveni={B\bibinitperiod},
givenun=0}}%
}
\list{location}{1}{%
{San Francisco, Calif}%
}
\list{publisher}{1}{%
{Morgan Kaufmann Publishers}%
}
\strng{namehash}{bf4b89988843985aac047efedf5000a7}
\strng{fullhash}{bf4b89988843985aac047efedf5000a7}
\strng{bibnamehash}{bf4b89988843985aac047efedf5000a7}
\strng{authorbibnamehash}{bf4b89988843985aac047efedf5000a7}
\strng{authornamehash}{bf4b89988843985aac047efedf5000a7}
\strng{authorfullhash}{bf4b89988843985aac047efedf5000a7}
\field{sortinit}{C}
\field{sortinithash}{4d103a86280481745c9c897c925753c0}
\field{extradatescope}{labelyear}
\field{labeldatesource}{}
\field{labelnamesource}{author}
\field{labeltitlesource}{shorttitle}
\field{isbn}{978-1-55860-533-6}
\field{series}{The {{Morgan Kaufmann}} Series in Interactive Technologies}
\field{shorttitle}{Readings in Information Visualization}
\field{title}{Readings in Information Visualization: Using Vision to Think}
\field{year}{1999}
\field{dateera}{ce}
\keyw{Computer graphics,Image processing,Information visualization}
\endentry
\entry{R-shiny}{manual}{}
\name{author}{10}{}{%
{{un=0,uniquepart=base,hash=3ad4f9244dbfff3b38eeeec6d91fbd7d}{%
family={Chang},
familyi={C\bibinitperiod},
given={Winston},
giveni={W\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=c94e860a0102157c3ad5932d1028762d}{%
family={Cheng},
familyi={C\bibinitperiod},
given={Joe},
giveni={J\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=c1f21e57d853466471e4fe8476b2e1f1}{%
family={Allaire},
familyi={A\bibinitperiod},
given={JJ},
giveni={J\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=c63711bd8fa68302eb02c665cf15d9ef}{%
family={Sievert},
familyi={S\bibinitperiod},
given={Carson},
giveni={C\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=af1b370f466fc885a0ee42a7ac26f068}{%
family={Schloerke},
familyi={S\bibinitperiod},
given={Barret},
giveni={B\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=25ce25b9d444076e0963bcf9fa3a6f92}{%
family={Xie},
familyi={X\bibinitperiod},
given={Yihui},
giveni={Y\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=be724c24a24555a4e7052dfe72f85e79}{%
family={Allen},
familyi={A\bibinitperiod},
given={Jeff},
giveni={J\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=8ec1111ac0c875e804db9ca5a116c726}{%
family={McPherson},
familyi={M\bibinitperiod},
given={Jonathan},
giveni={J\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=313f7bbff8ef91faa4282fc50f414b45}{%
family={Dipert},
familyi={D\bibinitperiod},
given={Alan},
giveni={A\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=655f754eef2ca97f45fe94cc0cdd31d6}{%
family={Borges},
familyi={B\bibinitperiod},
given={Barbara},
giveni={B\bibinitperiod},
givenun=0}}%
}
\strng{namehash}{4e6a81e5ff55ba85bd08bc7d45345e3b}
\strng{fullhash}{63b3bab4209163647c49b61a0afa440d}
\strng{bibnamehash}{63b3bab4209163647c49b61a0afa440d}
\strng{authorbibnamehash}{63b3bab4209163647c49b61a0afa440d}
\strng{authornamehash}{4e6a81e5ff55ba85bd08bc7d45345e3b}
\strng{authorfullhash}{63b3bab4209163647c49b61a0afa440d}
\field{sortinit}{C}
\field{sortinithash}{4d103a86280481745c9c897c925753c0}
\field{extradatescope}{labelyear}
\field{labeldatesource}{year}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{note}{R package version 1.6.0}
\field{title}{shiny: Web Application Framework for R}
\field{year}{2021}
\verb{urlraw}
\verb https://shiny.rstudio.com/
\endverb
\verb{url}
\verb https://shiny.rstudio.com/
\endverb
\endentry
\entry{church.hanks_1989}{inproceedings}{}
\name{author}{2}{}{%
{{un=0,uniquepart=base,hash=5d1216680e4eb77f96dd788b6d3a03c8}{%
family={Church},
familyi={C\bibinitperiod},
given={Kenneth\bibnamedelima Ward},
giveni={K\bibinitperiod\bibinitdelim W\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=1114530166d28c419ef0cf78a9482c68}{%
family={Hanks},
familyi={H\bibinitperiod},
given={Patrick},
giveni={P\bibinitperiod},
givenun=0}}%
}
\list{publisher}{1}{%
{Association for Computational Linguistics}%
}
\strng{namehash}{12adf6edf1ed6a31b6a97c672cae07b9}
\strng{fullhash}{12adf6edf1ed6a31b6a97c672cae07b9}
\strng{bibnamehash}{12adf6edf1ed6a31b6a97c672cae07b9}
\strng{authorbibnamehash}{12adf6edf1ed6a31b6a97c672cae07b9}
\strng{authornamehash}{12adf6edf1ed6a31b6a97c672cae07b9}
\strng{authorfullhash}{12adf6edf1ed6a31b6a97c672cae07b9}
\field{sortinit}{C}
\field{sortinithash}{4d103a86280481745c9c897c925753c0}
\field{extradatescope}{labelyear}
\field{labeldatesource}{}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{abstract}{The term word association is used in a very particular sense in the psycholinguistic literature. (Generally speaking, subjects respond quicker than normal to the word nurse if it follows a highly associated word such as doctor.) We will extend the term to provide the basis for a statistical description of a variety of interesting linguistic phenomena, ranging from semantic relations of the doctor/nurse type (content word/content word) to lexico-syntactic co-occurrence constraints between verbs and prepositions (content word/function word). This paper will propose an objective measure based on the information theoretic notion of mutual information, for estimating word association norms from computer readable corpora. (The standard method of obtaining word association norms, testing a few thousand :mbjects on a few hundred words, is both costly and unreliable.) The proposed measure, the association ratio, estimates word association norms directly from computer readable corpora, making it possible to estimate norms for tens of thousands of words.}
\field{booktitle}{{{ACL}} '89: {{Proceedings}} of the 27th Annual Meeting on {{Association}} for {{Computational Linguistic}}}
\field{langid}{english}
\field{month}{6}
\field{title}{Word Association Norms, Mutual Information, and Lexicography}
\field{year}{1989}
\field{dateera}{ce}
\field{pages}{76\bibrangedash 83}
\range{pages}{8}
\endentry
\entry{cox.cox_2008}{incollection}{}
\name{author}{2}{}{%
{{un=0,uniquepart=base,hash=d991b86047784961f91aa30ba9bc714b}{%
family={Cox},
familyi={C\bibinitperiod},
given={Michael\bibnamedelimb A.\bibnamedelimi A.},
giveni={M\bibinitperiod\bibinitdelim A\bibinitperiod\bibinitdelim A\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=9ac675d0140a1981e7feb32d362dc97b}{%
family={Cox},
familyi={C\bibinitperiod},
given={Trevor\bibnamedelima F.},
giveni={T\bibinitperiod\bibinitdelim F\bibinitperiod},
givenun=0}}%
}
\name{editor}{3}{}{%
{{hash=9834d4293a7c46d6815ed6430adf8698}{%
family={Chen},
familyi={C\bibinitperiod},
given={Chun-houh},
giveni={C\bibinithyphendelim h\bibinitperiod}}}%
{{hash=9569d8255a1089c8b1c86050c6e7a35e}{%
family={Härdle},
familyi={H\bibinitperiod},
given={Wolfgang},
giveni={W\bibinitperiod}}}%
{{hash=fef02797c475524db141bc41263fd993}{%
family={Unwin},
familyi={U\bibinitperiod},
given={Antony},
giveni={A\bibinitperiod}}}%
}
\list{location}{1}{%
{Berlin}%
}
\list{publisher}{1}{%
{Springer}%
}
\strng{namehash}{e4ed4055729d0d5da4c19a3918577bf0}
\strng{fullhash}{e4ed4055729d0d5da4c19a3918577bf0}
\strng{bibnamehash}{e4ed4055729d0d5da4c19a3918577bf0}
\strng{authorbibnamehash}{e4ed4055729d0d5da4c19a3918577bf0}
\strng{authornamehash}{e4ed4055729d0d5da4c19a3918577bf0}
\strng{authorfullhash}{e4ed4055729d0d5da4c19a3918577bf0}
\strng{editorbibnamehash}{31b0dbee65b758b95f961d0daa579add}
\strng{editornamehash}{31b0dbee65b758b95f961d0daa579add}
\strng{editorfullhash}{31b0dbee65b758b95f961d0daa579add}
\field{sortinit}{C}
\field{sortinithash}{4d103a86280481745c9c897c925753c0}
\field{extradatescope}{labelyear}
\field{labeldatesource}{}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{annotation}{OCLC: ocm76799021}
\field{booktitle}{Handbook of Data Visualization}
\field{isbn}{978-3-540-33036-3}
\field{series}{Springer Handbooks of Computational Statistics}
\field{title}{Multidimensional {{Scaling}}}
\field{year}{2008}
\field{dateera}{ce}
\field{pages}{315\bibrangedash 348}
\range{pages}{34}
\keyw{Information visualization}
\endentry
\entry{croft_2003}{incollection}{}
\name{author}{1}{}{%
{{un=0,uniquepart=base,hash=b04ec902fecc6ee3aa901faf48874ea5}{%
family={Croft},
familyi={C\bibinitperiod},
given={William},
giveni={W\bibinitperiod},
givenun=0}}%
}
\name{editor}{2}{}{%
{{hash=1d39404b0acd8eb321be0298a0385431}{%
family={Dirven},
familyi={D\bibinitperiod},
given={René},
giveni={R\bibinitperiod}}}%
{{hash=b7ce3198b0ef94bb3a00fab59c2a2e47}{%
family={Pörings},
familyi={P\bibinitperiod},
given={Ralf},
giveni={R\bibinitperiod}}}%
}
\list{location}{1}{%
{Berlin; New York, NY}%
}
\list{publisher}{1}{%
{Mouton de Gruyter}%
}
\strng{namehash}{b04ec902fecc6ee3aa901faf48874ea5}
\strng{fullhash}{b04ec902fecc6ee3aa901faf48874ea5}
\strng{bibnamehash}{b04ec902fecc6ee3aa901faf48874ea5}
\strng{authorbibnamehash}{b04ec902fecc6ee3aa901faf48874ea5}
\strng{authornamehash}{b04ec902fecc6ee3aa901faf48874ea5}
\strng{authorfullhash}{b04ec902fecc6ee3aa901faf48874ea5}
\strng{editorbibnamehash}{2a8a3c2e31e11c2206ac6d2dffa963bc}
\strng{editornamehash}{2a8a3c2e31e11c2206ac6d2dffa963bc}
\strng{editorfullhash}{2a8a3c2e31e11c2206ac6d2dffa963bc}
\field{sortinit}{C}
\field{sortinithash}{4d103a86280481745c9c897c925753c0}
\field{extradatescope}{labelyear}
\field{labeldatesource}{}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{booktitle}{Metaphor and Metonymy in Comparison and Contrast}
\field{isbn}{978-3-11-017374-1 3-11-017374-3 978-3-11-017374-1}
\field{langid}{english}
\field{series}{Mouton Reader}
\field{title}{The Role of Domains in the Interpretation of Metaphors and Metonymies}
\field{year}{2003}
\field{dateera}{ce}
\field{pages}{161\bibrangedash 206}
\range{pages}{46}
\keyw{11.1a;12.1a;11.3a,Aufsatzsammlung,Kognitive Linguistik,Metapher,Metaphor,Metonymie,Metonyms}
\endentry
\entry{depascale_2019}{thesis}{}
\name{author}{1}{}{%
{{un=0,uniquepart=base,hash=8d4d2b3c1c4662c93653c979d820903d}{%
family={De\bibnamedelima Pascale},
familyi={D\bibinitperiod\bibinitdelim P\bibinitperiod},
given={S.},
giveni={S\bibinitperiod},
givenun=0}}%
}
\name{editora}{2}{}{%
{{hash=d31a4f3d6ed720c7dcd4a9231c454af7}{%
family={Marzo},
familyi={M\bibinitperiod},
given={S.},
giveni={S\bibinitperiod}}}%
{{hash=60e9c3a5fc2c971f198d02c741ad73d1}{%
family={Speelman},
familyi={S\bibinitperiod},
given={D.},
giveni={D\bibinitperiod}}}%
}
\list{institution}{1}{%
{KU Leuven}%
}
\list{location}{1}{%
{Leuven}%
}
\strng{namehash}{8d4d2b3c1c4662c93653c979d820903d}
\strng{fullhash}{8d4d2b3c1c4662c93653c979d820903d}
\strng{bibnamehash}{8d4d2b3c1c4662c93653c979d820903d}
\strng{authorbibnamehash}{8d4d2b3c1c4662c93653c979d820903d}
\strng{authornamehash}{8d4d2b3c1c4662c93653c979d820903d}
\strng{authorfullhash}{8d4d2b3c1c4662c93653c979d820903d}
\strng{editorabibnamehash}{2fd5f479885f159fe830f939529fd967}
\strng{editoranamehash}{2fd5f479885f159fe830f939529fd967}
\strng{editorafullhash}{2fd5f479885f159fe830f939529fd967}
\field{sortinit}{D}
\field{sortinithash}{6f385f66841fb5e82009dc833c761848}
\field{extradatescope}{labelyear}
\field{labeldatesource}{}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{abstract}{Type-based distributional semantics as embodied in vector space models has proven to be a successful method for the retrieval of near-synonyms in large corpora. These words have then been used as variants of lexical sociolinguistic variables (e.g.: return and winst for the concept profit) in lectometric studies, that is, the aggregate-level study of lexical distances between linguistic varieties, in particular pluricentric languages such as Dutch. However, a limitation of type-based vector space models is that all senses of a word are lumped together into one vector representation, making it harder to control for polysemy and subtle contextual distinctions. In addition, operating at the lexeme level, these type-based vector space models are not able to pick out the relevant corpus occurrences that are the input for the lectometric distance calculations. The main goal of this PhD project is to introduce token-based vector space models in lexical lectometric research, in order to gain better semantic control during the composition of lexical variables. Token-based vector space models address the abovementioned shortcomings by disambiguating different senses of lexical variants. This technique is able to model the semantics of individual tokens (i.e. 'usage occurrences') of a word in a corpus and to represent them as token clouds in multidimensional vector space, with clusters of tokens revealing distinct senses of the word. By superimposing the token clouds of the lexical variants, one can distinguish which meanings are shared by near-synonyms and determine the 'semantic envelope of variation' of the lexical alternation. For instance, the variant return is polysemous in Netherlandic Dutch, with the two readings 'profit' and 'return game', but not in Belgian Dutch, where only the 'profit' sense is found. By isolating the cluster of tokens with the meaning 'profit' one can identify the near-synonymous tokens of the variants winst and return. The fine-tuning of vector space model-based lectometry targeted in this PhD contributes to the scaling up of lexical variationist research, by providing methods for dealing with corpora whose size exceeds manual analysis. At the same time, token-based models comply with the need of detailed analysis by allowing to zoom in on the behavior of individual tokens in order to determine more subtle contextual distinctions. This PhD project is situated in a larger research endeavor ("Nephological Semantics - Using token clouds for meaning detection in variationist linguistics", BOF C1 project 3H150305) that aims at detailed understanding of token-based vector representations for lexical, semantic and variational research.}
\field{editoratype}{collaborator}
\field{langid}{english}
\field{title}{Token-Based Vector Space Models as Semantic Control in Lexical Lectometry}
\field{type}{PhD Dissertation}
\field{year}{2019}
\field{dateera}{ce}
\endentry
\entry{desaussure_1971}{book}{useprefix=true}
\name{author}{1}{}{%
{{un=0,uniquepart=base,hash=bb17d9f07970a67196f1264271701ad5}{%
family={Saussure},
familyi={S\bibinitperiod},
given={Ferdinand},
giveni={F\bibinitperiod},
givenun=0,
prefix={de},
prefixi={d\bibinitperiod}}}%
}
\name{editor}{2}{}{%
{{hash=e211e921d3476bd196f8ca11d60c5315}{%
family={Bally},
familyi={B\bibinitperiod},
given={Charles},
giveni={C\bibinitperiod}}}%
{{hash=e2bb986108cbbf32c15275819dfb7ea6}{%
family={Sechehaye},
familyi={S\bibinitperiod},
given={Albert},
giveni={A\bibinitperiod}}}%
}
\list{location}{1}{%
{Paris}%
}
\list{publisher}{1}{%
{Payot}%
}
\strng{namehash}{bb17d9f07970a67196f1264271701ad5}
\strng{fullhash}{bb17d9f07970a67196f1264271701ad5}
\strng{bibnamehash}{bb17d9f07970a67196f1264271701ad5}
\strng{authorbibnamehash}{bb17d9f07970a67196f1264271701ad5}
\strng{authornamehash}{bb17d9f07970a67196f1264271701ad5}
\strng{authorfullhash}{bb17d9f07970a67196f1264271701ad5}
\strng{editorbibnamehash}{f3190cfcce584cb8d34790ad2b85ccb3}
\strng{editornamehash}{f3190cfcce584cb8d34790ad2b85ccb3}
\strng{editorfullhash}{f3190cfcce584cb8d34790ad2b85ccb3}
\field{sortinit}{d}
\field{sortinithash}{6f385f66841fb5e82009dc833c761848}
\field{extradatescope}{labelyear}
\field{labeldatesource}{}
\field{labelnamesource}{author}
\field{labeltitlesource}{title}
\field{origyear}{1916}
\field{title}{Cours de Linguistique Génerale}
\field{year}{1971}
\field{dateera}{ce}
\field{origdateera}{ce}
\endentry
\entry{BERT}{online}{}
\name{author}{4}{}{%
{{un=0,uniquepart=base,hash=13202969e372bc82318f9629cbdd199b}{%
family={Devlin},
familyi={D\bibinitperiod},
given={Jacob},
giveni={J\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=a45784fe7163b45f11d166564f5d24b6}{%
family={Chang},
familyi={C\bibinitperiod},
given={Ming-Wei},
giveni={M\bibinithyphendelim W\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=8dde73b4194f5bc4230c4808f3fc1534}{%
family={Lee},
familyi={L\bibinitperiod},
given={Kenton},
giveni={K\bibinitperiod},
givenun=0}}%
{{un=0,uniquepart=base,hash=b92aa283415413bb8d2a1548716d0c7d}{%
family={Toutanova},
familyi={T\bibinitperiod},
given={Kristina},
giveni={K\bibinitperiod},
givenun=0}}%
}
\strng{namehash}{ad8e2e2a80d28ade21e86852b804fc9b}
\strng{fullhash}{e8bccd48302a14eeba57d9dce2f49ef4}
\strng{bibnamehash}{e8bccd48302a14eeba57d9dce2f49ef4}
\strng{authorbibnamehash}{e8bccd48302a14eeba57d9dce2f49ef4}
\strng{authornamehash}{ad8e2e2a80d28ade21e86852b804fc9b}
\strng{authorfullhash}{e8bccd48302a14eeba57d9dce2f49ef4}
\field{sortinit}{D}
\field{sortinithash}{6f385f66841fb5e82009dc833c761848}
\field{extradatescope}{labelyear}
\field{labeldatesource}{}
\field{labelnamesource}{author}
\field{labeltitlesource}{shorttitle}
\field{abstract}{We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5\% (7.7\% point absolute improvement), MultiNLI accuracy to 86.7\% (4.6\% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).}
\field{day}{24}
\field{eprinttype}{arxiv}
\field{month}{5}
\field{shorttitle}{{{BERT}}}
\field{title}{{{BERT}}: {{Pre}}-Training of {{Deep Bidirectional Transformers}} for {{Language Understanding}}}
\field{year}{2019}
\field{dateera}{ce}
\verb{eprint}
\verb 1810.04805
\endverb
\verb{file}
\verb C\:\\Users\\u0118974\\Zotero\\storage\\JPIUWCYQ\\Devlin et al. - 2019 - BERT Pre-training of Deep Bidirectional Transform.pdf;C\:\\Users\\u0118974\\Zotero\\storage\\85K98SLY\\1810.html
\endverb
\keyw{Computer Science - Computation and Language}
\endentry
\entry{devries.etal_2019}{online}{useprefix=true}
\name{author}{6}{}{%
{{un=0,uniquepart=base,hash=461988889428df760813460d89180695}{%
family={Vries},
familyi={V\bibinitperiod},
given={Wietse},
giveni={W\bibinitperiod},
givenun=0,
prefix={de},
prefixi={d\bibinitperiod}}}%
{{un=0,uniquepart=base,hash=49ce779dc0d97d2a269e49c771deb0be}{%
family={Cranenburgh},
familyi={C\bibinitperiod},
given={Andreas},
giveni={A\bibinitperiod},
givenun=0,
prefix={van},
prefixi={v\bibinitperiod}}}%
{{un=0,uniquepart=base,hash=c126dbf9092eacff66786a1a0d1e81ca}{%
family={Bisazza},
familyi={B\bibinitperiod},