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Π£ΠΊΠ»ΠΎΠ½Π΅Π½ΠΈΠ΅ ΠΎΡ ΡΠΏΠ»Π°ΡΡ Π½Π°Π»ΠΎΠ³ΠΎΠ²: Π±ΠΈΠ±Π»ΠΈΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΠΉ Π°Π½Π°Π»ΠΈΠ· ΡΠΎΡΠ΅ΠΊ Π·ΡΠ΅Π½ΠΈΡ Π²Π»Π°ΡΡΠΈ, Π±ΠΈΠ·Π½Π΅ΡΠ° ΠΈ Π½Π°ΡΠΊΠΈ
Π‘ΡΠ°ΡΡΡ ΠΏΠΎΡΠ²ΡΡΠ΅Π½Π° Π°Π½Π°Π»ΠΈΠ·Ρ ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΉ, ΠΊΠ°ΡΠ°ΡΡΠΈΡ
ΡΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΡΠΊΠ»ΠΎΠ½Π΅Π½ΠΈΡ ΠΎΡ ΡΠΏΠ»Π°ΡΡ Π½Π°Π»ΠΎΠ³ΠΎΠ². ΠΡΠ° ΡΠ΅ΠΌΠ° ΠΏΡΠΈΠ²Π»Π΅ΠΊΠ°Π΅Ρ ΠΏΡΠΈΡΡΠ°Π»ΡΠ½ΠΎΠ΅ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ Π½Π΅ ΡΠΎΠ»ΡΠΊΠΎ Π½Π°ΡΡΠ½ΠΎΠ³ΠΎ ΡΠΎΠΎΠ±ΡΠ΅ΡΡΠ²Π°. Π ΡΡΠ°ΡΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΡΠ΅ΡΡΡ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠ΅ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ°ΡΠΈΠΊΠΈ Π½Π°ΡΡΠ½ΡΡ
ΡΠ°Π±ΠΎΡ ΠΏΠΎ ΡΠΊΠ»ΠΎΠ½Π΅Π½ΠΈΡ ΠΎΡ ΡΠΏΠ»Π°ΡΡ Π½Π°Π»ΠΎΠ³ΠΎΠ² ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΠΌ Π²ΠΎΠΏΡΠΎΡΠ°ΠΌ, ΠΎΠ±ΡΡΠΆΠ΄Π°Π΅ΠΌΡΠΌ Π·Π°ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠΎΠ²Π°Π½Π½ΡΠΌΠΈ Π»ΠΈΡΠ°ΠΌΠΈ. Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ° Π½Π°ΡΡΠ½ΡΡ
ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΉ ΠΏΠΎ Π΄Π°Π½Π½ΠΎΠΉ ΡΠ΅ΠΌΠ°ΡΠΈΠΊΠ΅ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π»Π°ΡΡ ΡΠ»Π΅ΠΊΡΡΠΎΠ½Π½Π°Ρ Π±Π°Π·Π° e-Library. Π ΠΊΡΡΠ³ Π·Π°ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠΎΠ²Π°Π½Π½ΡΡ
Π»ΠΈΡ, Π½Π°ΠΏΡΡΠΌΡΡ Π·Π°Π²ΠΈΡΡΡΠΈΡ
ΠΎΡ ΠΏΡΠ°Π²ΠΈΠ» Π½Π°Π»ΠΎΠ³ΠΎΠΎΠ±Π»ΠΎΠΆΠ΅Π½ΠΈΡ, Π²Ρ
ΠΎΠ΄ΡΡ Π±ΠΈΠ·Π½Π΅ΡΡΠΎΠΎΠ±ΡΠ΅ΡΡΠ²ΠΎ ΠΈ Π³ΠΎΡΡΠ΄Π°ΡΡΡΠ²Π΅Π½Π½ΡΠ΅ ΠΎΡΠ³Π°Π½Ρ. ΠΠ»Ρ Π½ΠΈΡ
ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ°ΠΌΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΏΠΎ ΠΈΡΡΠ»Π΅Π΄ΡΠ΅ΠΌΠΎΠΉ ΡΠ΅ΠΌΠ΅ ΡΠ²Π»ΡΡΡΡΡ ΡΠ»Π΅ΠΊΡΡΠΎΠ½Π½Π°Ρ Π±Π°Π·Π° ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΉ ΠΈΠ·Π΄Π°ΡΠ΅Π»ΡΡΠΊΠΎΠ³ΠΎ Π΄ΠΎΠΌΠ° Β«ΠΠΎΠΌΠΌΠ΅ΡΡΠ°Π½ΡΡΒ» ΠΈ Β«Π ΠΎΡΡΠΈΠΉΡΠΊΠ°Ρ Π³Π°Π·Π΅ΡΠ°Β». ΠΠ»Ρ Π°Π½Π°Π»ΠΈΠ·Π° ΠΎΡΠΎΠ±ΡΠ°Π½Π° 301 ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΡ Π·Π° 2013-2015 Π³Π³. ΠΠ·ΡΡΠ΅Π½ΠΈΠ΅ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ°ΡΠΈΠΊΠΈ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΎΡΡ ΠΏΡΡΠ΅ΠΌ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ Π² ΡΠ°Π·ΡΠ΅Π·Π΅ Π²ΠΈΠ΄ΠΎΠ² ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΉ. ΠΠ° ΠΏΠ΅ΡΠ²ΠΎΠΌ ΡΡΠ°ΠΏΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π±ΡΠ» Π²ΡΠΏΠΎΠ»Π½Π΅Π½ ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΠΉ ΠΊΠΎΠ½ΡΠ΅Π½Ρ-Π°Π½Π°Π»ΠΈΠ· ΠΏΠΎΡΡΠ΅Π΄ΡΡΠ²ΠΎΠΌ Π²ΡΡΠ²Π»Π΅Π½ΠΈΡ ΠΎΠ±ΡΠΈΡ
ΡΠ΅ΠΌ, ΠΎΠ±ΡΡΠΆΠ΄Π°Π΅ΠΌΡΡ
Π² ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΡΡ
. ΠΠ°ΡΠ΅ΠΌ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΡΡ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· ΡΠ΅ΡΠ΅Π· ΡΡΠ°Π²Π½Π΅Π½ΠΈΠ΅ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΉ ΠΏΠΎ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΠΎΠΉ ΡΠ΅ΠΌΠ΅ ΠΈΠ· ΠΊΠ°ΠΆΠ΄ΠΎΠ³ΠΎ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ°. ΠΠ»Ρ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° ΠΈ Π²ΠΈΠ·ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π»ΠΈΡΡ ΠΌΠ΅ΡΠΎΠ΄Ρ Π±ΠΈΠ±Π»ΠΈΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° ΠΈ ΠΊΠ°ΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ. Π Π°ΡΡΠ΅ΡΡ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΠ»ΠΈΡΡ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ΄ΡΠΊΡΠ° QDA Miner v.5.0 ΠΌΠΎΠ΄ΡΠ»Ρ WordStat v.7.1.7. Π ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π±ΡΠ»ΠΈ ΡΠ΄Π΅Π»Π°Π½Ρ Π²ΡΠ²ΠΎΠ΄Ρ, ΡΡΠΎ ΡΠ°ΠΌΡΠΌΠΈ ΠΏΠΎΠΏΡΠ»ΡΡΠ½ΡΠΌΠΈ ΡΠ΅ΠΌΠ°ΠΌΠΈ, ΠΈΠ½ΡΠ΅ΡΠ΅Ρ ΠΊ ΠΊΠΎΡΠΎΡΡΠΌ Π½Π΅ ΠΌΠ΅Π½ΡΠ΅ΡΡΡ, ΡΠ²Π»ΡΡΡΡΡ: ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ Π·Π°ΠΊΠΎΠ½ΠΎΠ΄Π°ΡΠ΅Π»ΡΡΡΠ²Π°, Π·Π°ΠΊΠΎΠ½ΠΎΡΠ²ΠΎΡΡΠ΅ΡΡΠ²ΠΎ ΠΈ ΡΡΠΈΠ»Π΅Π½ΠΈΠ΅ ΠΏΡΠΈΠ½ΡΠΆΠ΄Π΅Π½ΠΈΡ. Π’Π΅ΠΌΡ, ΠΊ ΠΊΠΎΡΠΎΡΡΠΌ Π·Π° ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΠΌΡΠΉ ΠΏΠ΅ΡΠΈΠΎΠ΄ ΡΠ½ΠΈΠ·ΠΈΠ»ΡΡ ΠΈΠ½ΡΠ΅ΡΠ΅Ρ, ΠΊΠ°ΡΠ°ΡΡΡΡ ΠΌΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΡΡ
Π°ΡΠΏΠ΅ΠΊΡΠΎΠ² Π½Π°Π»ΠΎΠ³ΠΎΠΎΠ±Π»ΠΎΠΆΠ΅Π½ΠΈΡ, ΡΠ΅Π½Π΅Π²ΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ, ΡΠΎΠ±ΡΡΠ²Π΅Π½Π½ΠΎΡΡΠΈ ΠΈ ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΈΠΉ. ΠΡΠΌΠ΅ΡΠ΅Π½ΠΎ Π²ΡΡΠ°ΠΆΠ΅Π½Π½ΠΎΠ΅ Π²ΠΎΠ·ΡΠ°ΡΡΠ°Π½ΠΈΠ΅ ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠ° ΡΠΎΠΎΠ±ΡΠ΅ΡΡΠ²Π° ΠΊ ΡΠΈΡΠΌΠ°ΠΌ-ΠΎΠ΄Π½ΠΎΠ΄Π½Π΅Π²ΠΊΠ°ΠΌ, ΡΡΠΊΠΎΠ²ΠΎΠ΄ΡΡΠ²Ρ ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΉ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΊ Π²ΠΎΠΏΡΠΎΡΠ°ΠΌ ΡΡΡΠ°ΡΠΎΠ² ΠΈ ΠΏΠ΅Π½ΠΈ. ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΎ Π²ΡΡΠ²ΠΈΡΡ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΠ΅ Π½Π΅ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠ΅ ΡΠ΅ΠΌ, ΠΎΠ±ΡΡΠΆΠ΄Π°Π΅ΠΌΡΡ
Π±ΠΈΠ·Π½Π΅ΡΠΎΠΌ ΠΈ Π²Π»Π°ΡΡΡΡ, ΠΏΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ Ρ ΡΠ΅ΠΌΠ°ΠΌΠΈ Π½Π°ΡΡΠ½ΡΡ
ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΉ. Π Π°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΡΠ΅ Π² Π½Π°ΡΡΠ½ΡΡ
ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΡΡ
ΡΠ΅ΠΌΡ (ΡΠ΅Π½Π΅Π²Π°Ρ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠ°, ΠΊΠΎΡΡΡΠΏΡΠΈΡ, ΡΠΈΡΠΌΡ-ΠΎΠ΄Π½ΠΎΠ΄Π½Π΅Π²ΠΊΠΈ, Π²Π·Π½ΠΎΡΡ Π½Π° ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎΠ΅ ΡΡΡΠ°Ρ
ΠΎΠ²Π°Π½ΠΈΠ΅), Π³ΠΎΡΠ°Π·Π΄ΠΎ ΡΠ΅ΠΆΠ΅ Π²ΡΡΡΠ΅ΡΠ°ΡΡΡΡ Π½Π° ΡΠ΅ΡΡΡΡΠ°Ρ
ΠΈΠ·Π΄Π°ΡΠ΅Π»ΡΡΠΊΠΎΠ³ΠΎ Π΄ΠΎΠΌΠ° Β«ΠΠΎΠΌΠΌΠ΅ΡΡΠ°Π½ΡΡΒ» ΠΈ Π² Β«Π ΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π³Π°Π·Π΅ΡΠ΅Β», ΡΠΎΡΡΠ΅Π΄ΠΎΡΠ°ΡΠΈΠ²Π°ΡΡΠΈΡ
ΠΎΡΠ½ΠΎΠ²Π½ΠΎΠ΅ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ Π½Π° Π²ΠΎΠΏΡΠΎΡΠ°Ρ
Π·Π°ΠΊΠΎΠ½ΠΎΡΠ²ΠΎΡΡΠ΅ΡΡΠ²Π° ΠΈ ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½ΠΈΡ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΉ Π² Π·Π°ΠΊΠΎΠ½ΠΎΠ΄Π°ΡΠ΅Π»ΡΡΡΠ²Π΅. ΠΠ½Π°Π»ΠΈΠ· Π²Π·Π°ΠΈΠΌΠΎΡΠ²ΡΠ·Π΅ΠΉ Π² ΡΠ΅ΠΊΡΡΠ°Ρ
Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ°ΠΌΠΈ ΠΈ Π³ΠΎΠ΄ΠΎΠΌ ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΈ ΠΏΠΎΠΊΠ°Π·Π°Π», ΡΡΠΎ ΡΠ΅ΠΌΡ Π½Π°ΡΡΠ½ΡΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ ΡΠ±Π»ΠΈΠΆΠ°ΡΡΡΡ Ρ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ°ΠΌΠΈ, ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΠΌΡΠΌΠΈ Π²Π»Π°ΡΡΡΡ, Π° Π±ΠΈΠ·Π½Π΅Ρ-ΡΠΎΠΎΠ±ΡΠ΅ΡΡΠ²ΠΎ Π² Π±ΠΎΠ»ΡΡΠ΅ΠΉ ΡΡΠ΅ΠΏΠ΅Π½ΠΈ Π²ΠΎΠ²Π»Π΅ΠΊΠ°Π΅ΡΡΡ ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½ΠΈΠ΅ ΠΏΡΠ°Π²ΠΎΠ²ΠΎΠΉ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ°ΡΠΈΠΊΠΈ, Ρ. Π΅. ΡΠΎΡΠΊΠ° Π·ΡΠ΅Π½ΠΈΡ Π²Π»Π°ΡΡΠΈ Π²ΠΎ ΠΌΠ½ΠΎΠ³ΠΎΠΌ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ΅Ρ ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½ΠΈΠ΅ ΡΠ΅ΠΌΡ ΡΠΊΠ»ΠΎΠ½Π΅Π½ΠΈΡ ΠΎΡ Π½Π°Π»ΠΎΠ³ΠΎΠ² Π±ΠΈΠ·Π½Π΅Ρ-ΡΠΎΠΎΠ±ΡΠ΅ΡΡΠ²ΠΎΠΌ, ΠΈ Π½Π°ΡΡΠ½ΡΠΌΠΈ ΠΊΡΡΠ³Π°ΠΌΠΈ. Π’Π°ΠΊΠΈΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠΌ, Π±ΠΈΠ±Π»ΠΈΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ Π°Π½Π°Π»ΠΈΠ·Π° ΡΠ΅ΠΊΡΡΠΎΠ² ΠΌΠΎΠ³ΡΡ ΠΏΡΠΈΠΌΠ΅Π½ΡΡΡΡΡ Π΄Π»Ρ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ Π½Π°ΡΡΠ½ΡΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ, ΡΠΎΡΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΎΠ±Π·ΠΎΡΠΎΠ² Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΡ ΠΈ ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΠΎΠΈΡΠΊΠ° ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ.This article analyzes the publications relating to the problem of tax evasion. This topic is attractive not only for the academic community, but also for public at whole. The article explores to what extent the scientific publications on tax evasion correspond to practical issues discussed among the stakeholders. We used the electronic database of e-Library as a source of scientific publications on the subject. The principal stakeholders directly dependent on the taxation are the taxpayers and public authorities. We used the electronic database of publications Β«KommersantΒ» publishing house and the Β«Rossiyskaya GazetaΒ» to reflect issues discussed among the stakeholders. We selected for analyze 301 publications for the period of 2013-2015. The study was conducted by comparing the publication activity by types and period of publications. In the first stage of the study we have done the qualitative content analysis by identification the common themes discussed in hole sample of publications. Then, a quantitative analysis was conducted by comparing the distribution of publications on a particular topic from each source. We used bibliometric analysis method for the quantitative and bibliographic mapping method to visualize the results of research. Calculations were performed using the software QDA Miner v.5.0 module WordStat v.7.1.7. As a result, studies have concluded that the most popular topics of interest for which no changes are: changes in legislation, legislation and increased enforcement. Using the results of the conducted study, we can identify the main similarities and differences between the monitored sources. We can see the special attention to the: Legislation changes, Law enforcement, Entrepreneurship. Marked reduction of interest can be noted regarding to the following topics: International aspects of taxation, Shadow economy, Ownership, property, investment. The growth of interest can be noted in relation to the following topics: Directorship, Article of the Tax Code, Short-lived companies, Arrears and fines. The study revealed a certain disparity between the topics discussed among academic community and stakeholders. The topics discussed in the majority of scientific texts (shadow economy, corruption, the firm one-day, social security contributions), a much rarer can be found in the publication of Β«KommersantΒ» and Β«Rossiyskaya GazetaΒ» which focuses mainly on matters of legislation. Analysis of the relationships in the texts according to the source and year of publication showed that research topics converge with issues considered by the public authorities. The business community more involved in discussion the legal issues, because the government notion works upon the impression about tax evasion of the business community and academia. Thus, bibliometric text analysis techniques can be used for research, preparation of literature reviews and thematic information retrieval
Aspect-Controlled Neural Argument Generation
We rely on arguments in our daily lives to deliver our opinions and base them
on evidence, making them more convincing in turn. However, finding and
formulating arguments can be challenging. In this work, we train a language
model for argument generation that can be controlled on a fine-grained level to
generate sentence-level arguments for a given topic, stance, and aspect. We
define argument aspect detection as a necessary method to allow this
fine-granular control and crowdsource a dataset with 5,032 arguments annotated
with aspects. Our evaluation shows that our generation model is able to
generate high-quality, aspect-specific arguments. Moreover, these arguments can
be used to improve the performance of stance detection models via data
augmentation and to generate counter-arguments. We publish all datasets and
code to fine-tune the language model
A Scalable Asynchronous Distributed Algorithm for Topic Modeling
Learning meaningful topic models with massive document collections which
contain millions of documents and billions of tokens is challenging because of
two reasons: First, one needs to deal with a large number of topics (typically
in the order of thousands). Second, one needs a scalable and efficient way of
distributing the computation across multiple machines. In this paper we present
a novel algorithm F+Nomad LDA which simultaneously tackles both these problems.
In order to handle large number of topics we use an appropriately modified
Fenwick tree. This data structure allows us to sample from a multinomial
distribution over items in time. Moreover, when topic counts
change the data structure can be updated in time. In order to
distribute the computation across multiple processor we present a novel
asynchronous framework inspired by the Nomad algorithm of
\cite{YunYuHsietal13}. We show that F+Nomad LDA significantly outperform
state-of-the-art on massive problems which involve millions of documents,
billions of words, and thousands of topics
Reading Habits in Different Communities
Reading is foundational to learning and the information acquisition upon which people make decisions. For centuries, the capacity to read has been a benchmark of literacy and involvement in community life. In the 21st Century, across all types of U.S. communities, reading is a common activity that is pursued in myriad ways. As technology and the digital world expand and offer new types of reading opportunities, residents of urban, suburban, and rural communities at times experience reading and e-reading differently. In the most meaningful ways, these differences are associated with the demographic composition of differentkinds of communities -- the age of the population, their overall level of educational attainment, and the general level of household income.Several surveys by the Pew Research Center's Internet & American Life Project reveal interesting variations among communities in the way their residents read and use reading-related technology and institutions
1st INCF Workshop on Sustainability of Neuroscience Databases
The goal of the workshop was to discuss issues related to the sustainability of neuroscience databases, identify problems and propose solutions, and formulate recommendations to the INCF. The report summarizes the discussions of invited participants from the neuroinformatics community as well as from other disciplines where sustainability issues have already been approached. The recommendations for the INCF involve rating, ranking, and supporting database sustainability
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