Name-dropping in 18th Century Public Discourse Aleksi Jalavala 1 , - - PowerPoint PPT Presentation
Name-dropping in 18th Century Public Discourse Aleksi Jalavala 1 , Annika Pensola 1 , Bruno Sartini 3 , David Rosson 2 , Peeter Tinits 5 , Selina Lehtoranta 1 , Sophie Schneider 4 , Veera Oksala 1 mon Hengchen 1 , Tanja Sily 1 Team leaders: Si 1
Aleksi Jalavala1, Annika Pensola1, Bruno Sartini3, David Rosson2, Peeter Tinits5, Selina Lehtoranta1, Sophie Schneider4, Veera Oksala1 Team leaders: Si mon Hengchen1 , Tanja Säily1
1University of Helsinki 2Aalto University 3University of Bologna 4Potsdam University of Applied Sciences 5Tallinn University
Which personal names are frequently mentioned in 18th century British publications? On the basis of the frequency and co-occurrence of individual names, what kind of patterns can we detect that are characteristic of genres and time periods?
Eighteenth Century Collections Online (ECCO)
(180,000 titles, 32 million pages)
They do not ddfcovcr much taste or ingenuity in building their hoifllts; though the defe& is rather in the delign than the execution. Those of the lower people are poor huts, thole of the better are larger and more comfortable. Their houfcs, properly speaking, are thatched roofs or sheds supported by pofgs and r:fters dilpofed in a tolerably judicious manner.
Eighteenth Century Collections Online (ECCO)
(180,000 titles, 32 million pages)
and keyword analysis
Keyness analysis
1) Data Extraction - subsets from ECCO Corpus 2) Named Entity Extraction from the subsets 3) NER Validation: both qualitative (manual checking) and quantitative (automatic match with DBPedia) 4) Sampling of the data 5) Visualization of the data through Networks and metadata filtering 6) Qualitative Examination of the results in the Visualization 7) Refinement of the quantitative techniques based on the feedback of the qualitative examination (Repeat step 3 until we are satisfied)
1) Data Extraction - subsets from ECCO Corpus 2) Named Entity Extraction from the subsets 3) NER Validation: both qualitative (manual checking) and quantitative (automatic match with DBPedia) 4) Sampling of the data 5) Visualization of the data through Networks and metadata filtering 6) Qualitative Examination of the results in the Visualization 7) Refinement of the quantitative techniques based on the feedback of the qualitative examination (Repeat step 3 until we are satisfied)
1) Data Extraction - subsets from ECCO Corpus 2) Named Entity Extraction from the subsets 3) NER Validation: both qualitative (manual checking) and quantitative (automatic match with DBPedia) 4) Sampling of the data 5) Visualization of the data through Networks and metadata filtering 6) Qualitative Examination of the results in the Visualization 7) Refinement of the quantitative techniques based on the feedback of the qualitative examination (Repeat step 3 until we are satisfied)
1) Data Extraction - subsets from ECCO Corpus 2) Named Entity Extraction from the subsets 3) NER Validation: both qualitative (manual checking) and quantitative (automatic match with DBPedia) 4) Sampling of the data 5) Visualization of the data through Networks and metadata filtering 6) Qualitative Examination of the results in the Visualization 7) Refinement of the quantitative techniques based on the feedback of the qualitative examination (Repeat step 3 until we are satisfied)
1) Data Extraction - subsets from ECCO Corpus 2) Named Entity Extraction from the subsets 3) NER Validation: both qualitative (manual checking) and quantitative (automatic match with DBPedia) 4) Sampling of the data 5) Visualization of the data through Networks and metadata filtering 6) Qualitative Examination of the results in the Visualization 7) Refinement of the quantitative techniques based on the feedback of the qualitative examination (Repeat step 3 until we are satisfied)
1) Data Extraction - subsets from ECCO Corpus 2) Named Entity Extraction from the subsets 3) NER Validation: both qualitative (manual checking) and quantitative (automatic match with DBPedia) 4) Sampling of the data 5) Visualization of the data through Networks and metadata filtering 6) Qualitative Examination of the results in the Visualization 7) Refinement of the quantitative techniques based on the feedback of the qualitative examination (Repeat step 3 until we are satisfied)
1) Data Extraction - subsets from ECCO Corpus 2) Named Entity Extraction from the subsets 3) NER Validation: both qualitative (manual checking) and quantitative (automatic match with DBPedia) 4) Sampling of the data 5) Visualization of the data through Networks and metadata filtering 6) Qualitative Examination of the results in the Visualization 7) Refinement of the quantitative techniques based on the feedback of the qualitative examination (Repeat step 3 until we are satisfied)
○ Religion more static, others more dynamic ○ Classics and religious figures, e.g. Jesus, Cicero, Virgil, are mentioned across genres & remain constant throughout the century ■ hints at name-dropping as a proof of one’s education
○ Religion more static, others more dynamic ○ Classics and religious figures, e.g. Jesus, Cicero, Virgil, are mentioned across genres & remain constant throughout the century ■ hints at name-dropping as a proof of one’s education
○ Cluster similar to modules in ECCO ○ A way to approximate genres?
○ Religion more static, others more dynamic ○ Classics and religious figures, e.g. Jesus, Cicero, Virgil, are mentioned across genres & remain constant throughout the century ■ hints at name-dropping as a proof of one’s education
○ Cluster similar to modules in ECCO ○ A way to approximate genres?
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@GenreAndStyle
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