项目作者: ablx

项目描述 :
My Master's Thesis on Comparative Argument Mining (NLP)
高级语言: Jupyter Notebook
项目地址: git://github.com/ablx/comparative-arguments-thesis.git
创建时间: 2018-09-28T12:23:12Z
项目社区:https://github.com/ablx/comparative-arguments-thesis

开源协议:

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conf-full_paths_original_4_aip_True_1647056581063.pdf
conf-middle_paths_unrestricted_16_True_1647056581068.pdf
conf-Bag-Of-Words_False_1647056581092.pdf
conf-Contains JJR_False_1647056581096.pdf
conf-InferSent_False_1647056581100.pdf
conf-POS n-grams_False_1647056581103.pdf
conf-Word Embedding_False_1647056581105.pdf
conf-full_paths_original_4_aip_False_1647056581108.pdf
conf-middle_paths_unrestricted_16_False_1647056581111.pdf
conf-heldout_Bag-Of-Words_True_1647056581395.pdf
conf-heldout_Contains JJR_True_1647056581399.pdf
conf-heldout_InferSent_True_1647056581402.pdf
conf-heldout_POS n-grams_True_1647056581405.pdf
conf-heldout_Word Embedding_True_1647056581408.pdf
conf-heldout_full_paths_original_4_aip_True_1647056581411.pdf
conf-heldout_middle_paths_unrestricted_16_True_1647056581427.pdf
conf-heldout_Bag-Of-Words_False_1647056581431.pdf
conf-heldout_Contains JJR_False_1647056581434.pdf
conf-heldout_InferSent_False_1647056581437.pdf
conf-heldout_POS n-grams_False_1647056581440.pdf
conf-heldout_Word Embedding_False_1647056581443.pdf
conf-heldout_full_paths_original_4_aip_False_1647056581446.pdf
conf-heldout_middle_paths_unrestricted_16_False_1647056581449.pdf
conf-jl_heldout_full_paths_original_4_False_1647056581992.pdf
conf-jl_heldout_full_paths_original_4_True_1647056581995.pdf
conf-jl_heldout_middle_paths_unrestricted_16_False_1647056581998.pdf
conf-jl_heldout_middle_paths_unrestricted_16_True_1647056582001.pdf
h-f1-False_1647056582599.pdf
h-precision-False_1647056582613.pdf
h-precision-True_1647056582616.pdf
h-recall-False_1647056582643.pdf
h-recall-True_1647056582667.pdf
jl-f1-False_1647056582679.pdf
jl-f1-True_1647056582685.pdf
-dist_1647056584591.pdf
Alldomains-dist_1647056584595.pdf
Brands-dist_1647056584616.pdf
Compsci-dist_1647056584632.pdf
Random-dist_1647056584635.pdf
classifier_1647056586832.pdf
Alldomains-dist_1647056586853.pdf
Brands-dist_1647056586856.pdf
Compsci-dist_1647056586859.pdf
Random-dist_1647056586874.pdf
pre-dist_1647056586895.pdf
prea-dist_1647056586907.pdf
preb-dist_1647056586910.pdf
dectree_1647056586913.pdf
conf-InferSent_False_1647056586972.pdf
conf-InferSent_True_1647056587005.pdf
conf-middle_paths_unrestricted_16_False_1647056587013.pdf
conf-middle_paths_unrestricted_16_True_1647056587031.pdf
f1-False_1647056587045.pdf
f1-True_1647056587062.pdf
precision-False_1647056587075.pdf
precision-True_1647056587099.pdf
recall-False_1647056587110.pdf
recall-True_1647056587131.pdf
jl-f1-False_1647056587147.pdf
jl-f1-True_1647056587156.pdf
h-f1-False_1647056587173.pdf
h-f1-True_1647056587182.pdf
h-precision-False_1647056587195.pdf
h-precision-True_1647056587209.pdf
h-recall-False_1647056587234.pdf
h-recall-True_1647056587243.pdf
hypenet_example_1647056587263.pdf
lex_arch_1647056587274.pdf
nn_1647056587314.pdf
confidence_1647056587330.pdf
label_distribution_1647056587344.pdf
rnn_schema_1647056587360.pdf
conf-Bag-Of-Words_True_1647056581026.pdf
conf-Contains JJR_True_1647056581034.pdf
conf-InferSent_True_1647056581042.pdf
conf-POS n-grams_True_1647056581051.pdf
conf-Word Embedding_True_1647056581058.pdf