The document discusses analyzing function words in literary texts. It notes that while function words are typically ignored in text mining as "stop words", they may provide important insights into literary and linguistic aspects of a text. The document presents analysis of function word frequency profiles of 24 main characters across 5 Christmas stories by Charles Dickens to investigate how function words may reflect differences between characters.
The document discusses analyzing function words in literary texts using text mining techniques. It notes that while function words are typically ignored in text mining as "stop words", they make up about 50% of tokens in a text and play an important grammatical and syntactic role. It questions whether function words should also be considered meaningless from a semantic and literary perspective, and presents analysis of frequency profiles of function words in characters from Charles Dickens' Christmas Books to argue they may provide insights.
This document appears to be a report listing the grades of students in various subjects. It shows the names of students, their grades on a scale of 1 to 5 in classes like Social Sciences, Languages, Technologies and more. The grades are out of a total of 50. The top student received a total of 47.3 and the bottom student received a total of 27.3.
The document contains a table listing 45 players with their player level and numbers for their first, second and third attacks. It provides instructions to make both attacks no later than 18 hours into the war and to make the first two attacks as soon as possible within the first 12 hours. It also instructs cleaners to attack in the last 8 hours aiming for any remaining targets.
Oligomer is a byproduct of polyester manufacturing made up of small chains of ethylene terephthalate molecules. It is difficult to remove from polyester fibers and can cause issues during dyeing and spinning such as duller shades, dye spots, and clogged machine parts. Various post-treatment methods have been used to remove oligomers but often damage fibers or are not fully effective. Altranol products are effective at keeping oligomers dispersed during dyeing and alkaline treatments to prevent redepositing and allow more to be removed from fibers and machines.
This document provides the timetable for a train service between Molins de Rei and Maçanet-Massanes that runs until September 12, 2014. It connects with bus services to Blanes, Lloret de Mar, and Tossa de Mar. The timetable lists departure times from Monday to Friday between 6:40-23:40 hours from Molins de Rei to Blanes and Lloret de Mar, and between 6:40-20:25 hours to Tossa de Mar.
This document contains a train schedule from 4:50am to 2:19pm listing the times that trains on different lines (g, o, +) depart. It shows the frequent intervals that trains on each line leave, typically every 2-4 minutes. Over 50 separate train departures are scheduled from various stations on multiple train lines during this time period.
This document contains a chart listing pipe sizes, outside diameters, wall thicknesses, and weights. The pipe sizes range from 1/8 inch to 72 inches in nominal diameter. For each size, the chart provides the outside diameter in mm and the wall thickness in mm for various standard wall thickness designations including SCH 10, SCH 20, SCH 30, etc. Heavier wall thicknesses are shown for larger pipe sizes.
The document provides reference tables for refrigerant pressure-temperature charts for R22, R134a, and R407C refrigerants. It also includes charts converting between kg/cm2 and psi pressure units, as well as common conversion factors for units like mm, m, kg, kW, °C, and °F.
The document discusses analyzing function words in literary texts using text mining techniques. It notes that while function words are typically ignored in text mining as "stop words", they make up about 50% of tokens in a text and play an important grammatical and syntactic role. It questions whether function words should also be considered meaningless from a semantic and literary perspective, and presents analysis of frequency profiles of function words in characters from Charles Dickens' Christmas Books to argue they may provide insights.
This document appears to be a report listing the grades of students in various subjects. It shows the names of students, their grades on a scale of 1 to 5 in classes like Social Sciences, Languages, Technologies and more. The grades are out of a total of 50. The top student received a total of 47.3 and the bottom student received a total of 27.3.
The document contains a table listing 45 players with their player level and numbers for their first, second and third attacks. It provides instructions to make both attacks no later than 18 hours into the war and to make the first two attacks as soon as possible within the first 12 hours. It also instructs cleaners to attack in the last 8 hours aiming for any remaining targets.
Oligomer is a byproduct of polyester manufacturing made up of small chains of ethylene terephthalate molecules. It is difficult to remove from polyester fibers and can cause issues during dyeing and spinning such as duller shades, dye spots, and clogged machine parts. Various post-treatment methods have been used to remove oligomers but often damage fibers or are not fully effective. Altranol products are effective at keeping oligomers dispersed during dyeing and alkaline treatments to prevent redepositing and allow more to be removed from fibers and machines.
This document provides the timetable for a train service between Molins de Rei and Maçanet-Massanes that runs until September 12, 2014. It connects with bus services to Blanes, Lloret de Mar, and Tossa de Mar. The timetable lists departure times from Monday to Friday between 6:40-23:40 hours from Molins de Rei to Blanes and Lloret de Mar, and between 6:40-20:25 hours to Tossa de Mar.
This document contains a train schedule from 4:50am to 2:19pm listing the times that trains on different lines (g, o, +) depart. It shows the frequent intervals that trains on each line leave, typically every 2-4 minutes. Over 50 separate train departures are scheduled from various stations on multiple train lines during this time period.
This document contains a chart listing pipe sizes, outside diameters, wall thicknesses, and weights. The pipe sizes range from 1/8 inch to 72 inches in nominal diameter. For each size, the chart provides the outside diameter in mm and the wall thickness in mm for various standard wall thickness designations including SCH 10, SCH 20, SCH 30, etc. Heavier wall thicknesses are shown for larger pipe sizes.
The document provides reference tables for refrigerant pressure-temperature charts for R22, R134a, and R407C refrigerants. It also includes charts converting between kg/cm2 and psi pressure units, as well as common conversion factors for units like mm, m, kg, kW, °C, and °F.
This document contains tables with information about steel beams and columns including:
- Dimensions and cross-sectional areas of different steel beam and column sizes
- Weights in kg/m of steel beams and columns
- Cross-sectional areas in cm^2 of steel plates
- Dimensions are given in mm and information is provided for a range of standard sizes.
This document provides a table listing the weights in kilograms per meter of seamless steel tubes of varying diameters and wall thicknesses according to DIN 2448. The table includes the outer diameter, standard wall thickness, weight in kg/m, and other dimensional specifications for over 50 tube sizes ranging from 10.2 mm outer diameter up to 558.8 mm outer diameter.
1) This document provides a chi-square table for determining the critical value for rejecting the null hypothesis at different confidence levels and degrees of freedom.
2) The table lists the chi-square value for confidence levels from 99% to 0.1% and degrees of freedom ranging from 1 to 30.
3) If the calculated chi-square value is greater than the corresponding value in the table for a given confidence level and degrees of freedom, the null hypothesis can be rejected.
This document provides standard sectional dimensions and properties of equal angle steel and double angle steel. It includes dimensions such as length, width, thickness, radius of gyration, sectional area, unit weight, and moments of inertia. Properties are listed for standard sizes ranging from 25x25mm to 250x250mm.
This document provides standard sectional dimensions and properties of equal angle steel and double angle steel. It includes dimensions such as length, width, thickness, radius of gyration, sectional area, unit weight, and moments of inertia. Properties are listed for standard sizes ranging from 25x25mm to 250x250mm.
This document appears to be statistics or scores for various players over multiple dates from 1/5/2009 to 1/11/2009. It includes the player's name, scores on each date, total increase, and average increase. The highest overall increase was by the player "peace maker 1" with 15,240 points. The highest average increase per day was by the player "kalikagaros" with 113.17 points.
This document contains a schedule for the R4 train line between Sant Vicenç de Calders and Manresa, traveling through Vilafranca del Penedès. The schedule lists over 50 departure times on weekdays between 4:54 AM and 2:13 PM, with trains leaving approximately every 30 minutes. Major stops along the route include Cerdanyola Universitat.
This document provides specifications for various sizes of structural steel profiles including circular, rectangular, and square shapes. It lists the nominal and actual dimensions, wall thickness, static properties such as area, moment of inertia, radius of gyration, plastic modulus, and elastic modulus for bending and torsion. Properties are provided for different steel grades including black and galvanized.
This document analyzes hero pick/ban statistics from competitive Dota 2 games in April. It lists 44 heroes and their percentage of times picked, banned, and picked or banned. The most popular heroes were Lycan (picked 12.7% of the time, banned 87.3% of the time, for a total of 100% picked or banned), Ember Spirit (29.4% pick rate, 66.4% ban rate, 95.8% total), and Invoker (41.2% pick rate, 53.4% ban rate, 94.6% total). The least popular heroes were Legion Commander, Terrorblade, Huskar, Necrophos, and Ogre Magi, which
This document summarizes hero pick/ban statistics from competitive Dota 2 games in April. It shows that Lycan, Ember Spirit, and Invoker were among the most frequently picked heroes, being picked or banned in over 90% of games. It also lists each hero's individual pick rate, ban rate, and times picked or banned, with the least prevalent heroes in April being Legion Commander, Terrorblade, Huskar, and others not picked or banned at all.
R4 Sant Vicenç de Calders - Manresa, per Barcelona i SabadellPsc Polinyà
This document contains a schedule for the R4 train line between Sant Vicenç de Calders and Manresa, stopping in Vilafranca del Penedès. The schedule lists departure times from both locations between 4:56 AM and 1:10 PM on weekdays, with trains leaving each station approximately every 30 minutes. The last three sentences provide contact information for Rodalies de Catalunya, including their website, Twitter account, phone number, and address.
The document contains a code that should be copied into two text files named AccionesBonosUSA.txt. The code provides daily stock price and other financial data for various US companies and indices from 1987 to 1987. It lists dates, company ticker symbols, and corresponding price values.
This document contains a table listing basic values for L and H parameters corresponding to kinematic viscosity measurements in the 40-100°C system. The table includes kinematic viscosity values in mm2/s at 100°C along with the associated L and H parameters for determining kinematic viscosity at other temperatures between 40-100°C. There are over 70 rows of viscosity values ranging from 2 to 70 mm2/s at 100°C with calculations to estimate viscosity at different temperatures within the specified range.
This document contains precipitation data in millimeters for various Mexican states for the years 1985 and 1986. It provides monthly and annual precipitation totals for each state and nationally for both years. The states with the highest annual precipitation in 1985 included Chiapas, Oaxaca, Guerrero, and Tabasco. In 1986, the wettest states were Tabasco, Oaxaca, Veracruz, and Campeche. Nationally, precipitation levels were similar between the two years.
The document is an Erlang B traffic table that shows the maximum offered traffic load versus blocking probability (B) and number of lines (N). It provides traffic load values for various combinations of B (ranging from 0.01 to 40%) and N (ranging from 1 to 77 lines). The higher the traffic load, the higher the blocking probability will be for a given number of lines.
The document is an Erlang B traffic table that shows the maximum offered traffic load versus blocking probability (B) and number of lines (N). It provides traffic load values for various combinations of B (ranging from 0.01 to 40%) and N (ranging from 1 to 77 lines). The higher the traffic load, the higher the blocking probability will be for a given number of lines.
The document is an Erlang B traffic table that shows the maximum offered traffic load versus blocking probability (B) and number of lines (N). It provides traffic load values for various combinations of B ranging from 0.01 to 40% and N ranging from 1 to 77 lines. The higher the traffic load, the higher the blocking probability will be for a given number of lines.
The document appears to be weekly player statistics for an online game, listing the usernames of players and their scores from January 19th to 25th, 2009. It shows the username "Tallos" had the largest increase in score of 10,171 points over this period, rising from an average of 6.48% per day. The second largest increase belonged to the player "lion" who gained 5,610 points over the week.
The document contains an Erlang B traffic table that shows the maximum offered load versus B and N, where B is the blocking probability in % and N is the number of circuits. The table provides load values for different combinations of B ranging from 0.01 to 40% and N ranging from 1 to 77 circuits. The load values indicate the maximum number of external calls that can be offered to the system without exceeding the blocking probability B for a given number of circuits N.
Assessing the Influence of Transportation on the Tourism Industry in Nigeriagsochially
This research dissertation investigates the complex interplay between transportation and the tourism industry in Nigeria, aiming to unravel critical insights that contribute to the enhancement of the overall tourist experience. The study employs a multi-faceted approach, literature review establishes a robust theoretical framework, incorporating The Service Quality and Satisfaction Theory to guide the research questions and hypotheses.
The methodology involves the distribution of a structured questionnaire, ensuring a representative sample and facilitating a comprehensive analysis of the gathered data.
Key findings include the nuanced perceptions of transportation infrastructure adequacy, safety and security concerns, financial influences on travel decisions, and the cultural and ecological impacts of transportation choices. These findings culminate in a comprehensive set of recommendations for policymakers and practitioners in the Nigerian tourism industry. The findings contribute to the existing literature by providing actionable insights for policymakers, stakeholders, and researchers in the Nigerian tourism sector.
The recommendations encompass gender-sensitive planning, infrastructure enhancements, safety measures, and strategic interventions to address financial constraints, ensuring a holistic and sustainable development of the tourism industry in Nigeria.
Author: Imafidon Osademwingie Martins
BTW UK Visa Application Process, Uk Visa complete guide, Uk Visa fees, requirements and application process. Know all about uk visa and best way to apply for the uk visa. Get to know about the requirements that allows you for the faster visa appliaction. BTW UK Visa Application Process, Uk Visa complete guide, Uk Visa fees, requirements and application process. Know all about uk visa and best way to apply for the uk visa. Get to know about the requirements that allows you for the faster visa appliaction. BTW UK Visa Application Process, Uk Visa complete guide, Uk Visa fees, requirements and application process. Know all about uk visa and best way to apply for the uk visa. Get to know about the requirements that allows you for the faster visa appliaction. BTW UK Visa Application Process, Uk Visa complete guide, Uk Visa fees, requirements and application process. Know all about uk visa and best way to apply for the uk visa. Get to know about the requirements that allows you for the faster visa appliaction. BTW UK Visa Application Process, Uk Visa complete guide, Uk Visa fees, requirements and application process. Know all about uk visa and best way to apply for the uk visa. Get to know about the requirements that allows you for the faster visa appliaction. BTW UK Visa Application Process, Uk Visa complete guide, Uk Visa fees, requirements and application process. Know all about uk visa and best way to apply for the uk visa. Get to know about the requirements that allows you for the faster visa appliaction. BTW UK Visa Application Process, Uk Visa complete guide, Uk Visa fees, requirements and application process. Know all about uk visa and best way to apply for the uk visa. Get to know about the requirements that allows you for the faster visa appliaction. BTW UK Visa Application Process, Uk Visa complete guide, Uk Visa fees, requirements and application process. Know all about uk visa and best way to apply for the uk visa. Get to know about the requirements that allows you for the faster visa appliaction. Get information in this PDF and simplyfy your visa process.
This document contains tables with information about steel beams and columns including:
- Dimensions and cross-sectional areas of different steel beam and column sizes
- Weights in kg/m of steel beams and columns
- Cross-sectional areas in cm^2 of steel plates
- Dimensions are given in mm and information is provided for a range of standard sizes.
This document provides a table listing the weights in kilograms per meter of seamless steel tubes of varying diameters and wall thicknesses according to DIN 2448. The table includes the outer diameter, standard wall thickness, weight in kg/m, and other dimensional specifications for over 50 tube sizes ranging from 10.2 mm outer diameter up to 558.8 mm outer diameter.
1) This document provides a chi-square table for determining the critical value for rejecting the null hypothesis at different confidence levels and degrees of freedom.
2) The table lists the chi-square value for confidence levels from 99% to 0.1% and degrees of freedom ranging from 1 to 30.
3) If the calculated chi-square value is greater than the corresponding value in the table for a given confidence level and degrees of freedom, the null hypothesis can be rejected.
This document provides standard sectional dimensions and properties of equal angle steel and double angle steel. It includes dimensions such as length, width, thickness, radius of gyration, sectional area, unit weight, and moments of inertia. Properties are listed for standard sizes ranging from 25x25mm to 250x250mm.
This document provides standard sectional dimensions and properties of equal angle steel and double angle steel. It includes dimensions such as length, width, thickness, radius of gyration, sectional area, unit weight, and moments of inertia. Properties are listed for standard sizes ranging from 25x25mm to 250x250mm.
This document appears to be statistics or scores for various players over multiple dates from 1/5/2009 to 1/11/2009. It includes the player's name, scores on each date, total increase, and average increase. The highest overall increase was by the player "peace maker 1" with 15,240 points. The highest average increase per day was by the player "kalikagaros" with 113.17 points.
This document contains a schedule for the R4 train line between Sant Vicenç de Calders and Manresa, traveling through Vilafranca del Penedès. The schedule lists over 50 departure times on weekdays between 4:54 AM and 2:13 PM, with trains leaving approximately every 30 minutes. Major stops along the route include Cerdanyola Universitat.
This document provides specifications for various sizes of structural steel profiles including circular, rectangular, and square shapes. It lists the nominal and actual dimensions, wall thickness, static properties such as area, moment of inertia, radius of gyration, plastic modulus, and elastic modulus for bending and torsion. Properties are provided for different steel grades including black and galvanized.
This document analyzes hero pick/ban statistics from competitive Dota 2 games in April. It lists 44 heroes and their percentage of times picked, banned, and picked or banned. The most popular heroes were Lycan (picked 12.7% of the time, banned 87.3% of the time, for a total of 100% picked or banned), Ember Spirit (29.4% pick rate, 66.4% ban rate, 95.8% total), and Invoker (41.2% pick rate, 53.4% ban rate, 94.6% total). The least popular heroes were Legion Commander, Terrorblade, Huskar, Necrophos, and Ogre Magi, which
This document summarizes hero pick/ban statistics from competitive Dota 2 games in April. It shows that Lycan, Ember Spirit, and Invoker were among the most frequently picked heroes, being picked or banned in over 90% of games. It also lists each hero's individual pick rate, ban rate, and times picked or banned, with the least prevalent heroes in April being Legion Commander, Terrorblade, Huskar, and others not picked or banned at all.
R4 Sant Vicenç de Calders - Manresa, per Barcelona i SabadellPsc Polinyà
This document contains a schedule for the R4 train line between Sant Vicenç de Calders and Manresa, stopping in Vilafranca del Penedès. The schedule lists departure times from both locations between 4:56 AM and 1:10 PM on weekdays, with trains leaving each station approximately every 30 minutes. The last three sentences provide contact information for Rodalies de Catalunya, including their website, Twitter account, phone number, and address.
The document contains a code that should be copied into two text files named AccionesBonosUSA.txt. The code provides daily stock price and other financial data for various US companies and indices from 1987 to 1987. It lists dates, company ticker symbols, and corresponding price values.
This document contains a table listing basic values for L and H parameters corresponding to kinematic viscosity measurements in the 40-100°C system. The table includes kinematic viscosity values in mm2/s at 100°C along with the associated L and H parameters for determining kinematic viscosity at other temperatures between 40-100°C. There are over 70 rows of viscosity values ranging from 2 to 70 mm2/s at 100°C with calculations to estimate viscosity at different temperatures within the specified range.
This document contains precipitation data in millimeters for various Mexican states for the years 1985 and 1986. It provides monthly and annual precipitation totals for each state and nationally for both years. The states with the highest annual precipitation in 1985 included Chiapas, Oaxaca, Guerrero, and Tabasco. In 1986, the wettest states were Tabasco, Oaxaca, Veracruz, and Campeche. Nationally, precipitation levels were similar between the two years.
The document is an Erlang B traffic table that shows the maximum offered traffic load versus blocking probability (B) and number of lines (N). It provides traffic load values for various combinations of B (ranging from 0.01 to 40%) and N (ranging from 1 to 77 lines). The higher the traffic load, the higher the blocking probability will be for a given number of lines.
The document is an Erlang B traffic table that shows the maximum offered traffic load versus blocking probability (B) and number of lines (N). It provides traffic load values for various combinations of B (ranging from 0.01 to 40%) and N (ranging from 1 to 77 lines). The higher the traffic load, the higher the blocking probability will be for a given number of lines.
The document is an Erlang B traffic table that shows the maximum offered traffic load versus blocking probability (B) and number of lines (N). It provides traffic load values for various combinations of B ranging from 0.01 to 40% and N ranging from 1 to 77 lines. The higher the traffic load, the higher the blocking probability will be for a given number of lines.
The document appears to be weekly player statistics for an online game, listing the usernames of players and their scores from January 19th to 25th, 2009. It shows the username "Tallos" had the largest increase in score of 10,171 points over this period, rising from an average of 6.48% per day. The second largest increase belonged to the player "lion" who gained 5,610 points over the week.
The document contains an Erlang B traffic table that shows the maximum offered load versus B and N, where B is the blocking probability in % and N is the number of circuits. The table provides load values for different combinations of B ranging from 0.01 to 40% and N ranging from 1 to 77 circuits. The load values indicate the maximum number of external calls that can be offered to the system without exceeding the blocking probability B for a given number of circuits N.
Similar to Tabata@ARG_Café_Kyoto_20120226.key (20)
Assessing the Influence of Transportation on the Tourism Industry in Nigeriagsochially
This research dissertation investigates the complex interplay between transportation and the tourism industry in Nigeria, aiming to unravel critical insights that contribute to the enhancement of the overall tourist experience. The study employs a multi-faceted approach, literature review establishes a robust theoretical framework, incorporating The Service Quality and Satisfaction Theory to guide the research questions and hypotheses.
The methodology involves the distribution of a structured questionnaire, ensuring a representative sample and facilitating a comprehensive analysis of the gathered data.
Key findings include the nuanced perceptions of transportation infrastructure adequacy, safety and security concerns, financial influences on travel decisions, and the cultural and ecological impacts of transportation choices. These findings culminate in a comprehensive set of recommendations for policymakers and practitioners in the Nigerian tourism industry. The findings contribute to the existing literature by providing actionable insights for policymakers, stakeholders, and researchers in the Nigerian tourism sector.
The recommendations encompass gender-sensitive planning, infrastructure enhancements, safety measures, and strategic interventions to address financial constraints, ensuring a holistic and sustainable development of the tourism industry in Nigeria.
Author: Imafidon Osademwingie Martins
BTW UK Visa Application Process, Uk Visa complete guide, Uk Visa fees, requirements and application process. Know all about uk visa and best way to apply for the uk visa. Get to know about the requirements that allows you for the faster visa appliaction. BTW UK Visa Application Process, Uk Visa complete guide, Uk Visa fees, requirements and application process. Know all about uk visa and best way to apply for the uk visa. Get to know about the requirements that allows you for the faster visa appliaction. BTW UK Visa Application Process, Uk Visa complete guide, Uk Visa fees, requirements and application process. Know all about uk visa and best way to apply for the uk visa. Get to know about the requirements that allows you for the faster visa appliaction. BTW UK Visa Application Process, Uk Visa complete guide, Uk Visa fees, requirements and application process. Know all about uk visa and best way to apply for the uk visa. Get to know about the requirements that allows you for the faster visa appliaction. BTW UK Visa Application Process, Uk Visa complete guide, Uk Visa fees, requirements and application process. Know all about uk visa and best way to apply for the uk visa. Get to know about the requirements that allows you for the faster visa appliaction. BTW UK Visa Application Process, Uk Visa complete guide, Uk Visa fees, requirements and application process. Know all about uk visa and best way to apply for the uk visa. Get to know about the requirements that allows you for the faster visa appliaction. BTW UK Visa Application Process, Uk Visa complete guide, Uk Visa fees, requirements and application process. Know all about uk visa and best way to apply for the uk visa. Get to know about the requirements that allows you for the faster visa appliaction. BTW UK Visa Application Process, Uk Visa complete guide, Uk Visa fees, requirements and application process. Know all about uk visa and best way to apply for the uk visa. Get to know about the requirements that allows you for the faster visa appliaction. Get information in this PDF and simplyfy your visa process.
Our excursions in tahiti offer stunning lagoon tours, vibrant marine life encounters, and cultural experiences. We ensure unforgettable adventures amidst breathtaking landscapes and serene waters. For more information, mail us at tracey@uniquetahiti.com.
Un viaje a Buenos Aires y sus alrededoresJudy Hochberg
A travelogue of my recent trip to Argentina, most to Buenos Aires, but including excursion to Iguazú waterfalls, Tigre, and Colonia del Sacramento in Uruguay
Wayanad-The-Touristry-Heaven to the tour.pptxcosmo-soil
Wayanad, nestled in Kerala's Western Ghats, is a lush paradise renowned for its scenic landscapes, rich biodiversity, and cultural heritage. From trekking Chembra Peak to exploring ancient Edakkal Caves, Wayanad offers thrilling adventures and serene experiences. Its vibrant economy, driven by agriculture and tourism, highlights a harmonious blend of nature, tradition, and modernity.
How To Talk To a Live Person at American Airlinesflyn goo
This page by FlynGoo can become your ultimate guide to connecting with a live person at American Airlines. Have you ever felt lost in the automated maze of customer service menus? FlynGoo is here to rescue you from endless phone trees and automated responses. With just a click or a call to a specific number, we ensure you get the human touch you deserve. No more frustration, no more waiting on hold - we simplify the process, making your travel experience smoother and more enjoyable.
How do I plan a Kilimanjaro Climb?
Planning to climb Mount Kilimanjaro is an exciting yet detailed process. Here’s a step-by-step guide to help you prepare for this incredible adventure.
The Power of a Glamping Go-To-Market Accelerator Plan.pptxRezStream
Unlock the secrets to success with our comprehensive 8-Step Glamping Accelerator Go-To-Market Plan! Watch our FREE webinar, where you'll receive expert guidance and invaluable insights on every aspect of launching and growing your glamping business.
Best Places to Stay in New Brunswick, Canada.Mahogany Manor
New Brunswick, a picturesque province in eastern Canada, offers a plethora of unique and charming places to stay for every kind of traveler. From the historic allure of Fredericton and the vibrant culture of Saint John to the natural beauty of Fundy National Park and the serene coastal towns like St. Andrews by-the-Sea, there's something for everyone. Whether you prefer luxury resorts, cozy inns, rustic lodges, or budget-friendly options, the best places to stay in New Brunswick ensure a memorable stay, allowing you to fully immerse yourself in the province's rich history, stunning landscapes, and warm hospitality.
https://www.mmanor.ca/blog/best-5-bed-and-breakfast-new-brunswick-canada
4. Marley was dead: to begin with. There is no doubt whatever
about that. The register of his burial was signed by the
clergyman, the clerk, the undertaker, and the chief mourner.
Scrooge signed it. And Scrooge's name was good upon 'Change,
for anything he chose to put his hand to.
Old Marley was as dead as a door-nail.
Mind! I don't mean to say that I know, of my own knowledge,
what there is particularly dead about a door-nail. I might have
been inclined, myself, to regard a coffin-nail as the deadest piece
of ironmongery in the trade. But the wisdom of our ancestors is
in the simile; and my unhallowed hands shall not disturb it, or the
Country's done for. You will therefore permit me to repeat,
emphatically, that Marley was as dead as a door-nail.
4
5. Marley was dead: to begin with. There is no doubt whatever
about that. The register of his burial was signed by the
clergyman, the clerk, the undertaker, and the chief mourner.
Scrooge signed it. And Scrooge's name was good upon 'Change,
for anything he chose to put his hand to.
Old Marley was as dead as a door-nail.
Mind! I don't mean to say that I know, of my own knowledge,
what there is particularly dead about a door-nail. I might have
been inclined, myself, to regard a coffin-nail as the deadest piece
of ironmongery in the trade. But the wisdom of our ancestors is
in the simile; and my unhallowed hands shall not disturb it, or the
Country's done for. You will therefore permit me to repeat,
emphatically, that Marley was as dead as a door-nail.
—Charles Dickens, A Christmas Carol (1843) 5
6. Function words(機能語)
the, of, and, in, to, I, you, . . .
高頻度で生起する単語
頻度上位60項目のトークン数はテクストの総トークン数
(総語数)の約50%をカバー
テクストマイニングでも通常は stop words(ゴミ)として
排除(無視)される項目。いわんや,文学批評で取り上げ
られることなど殆ど無い。
6
7. Function words(機能語)
the, of, and, in, to, I, you, . . .
高頻度で生起する単語
頻度上位60項目のトークン数はテクストの総トークン数
(総語数)の約50%をカバー
テクストマイニングでも通常は stop words(ゴミ)として
排除(無視)される項目。いわんや,文学批評で取り上げ
られることなど殆ど無い。
7
8. Function words(機能語)
the, of, and, in, to, I, you, . . .
高頻度で生起する単語
頻度上位60項目のトークン数はテクストの総トークン数
(総語数)の約50%をカバー
テクストマイニングでも通常は stop words(ゴミ)として
排除(無視)される項目。いわんや,文学批評で取り上げ
られることなど殆ど無い。
8
9. Function words(機能語)
the, of, and, in, to, I, you, . . .
高頻度で生起する単語
頻度上位60項目のトークン数はテクストの総トークン数
(総語数)の約50%をカバー
テクストマイニングでも通常は stop words(ゴミ)として
排除(無視)される項目。いわんや,文学批評で取り上げ
られることなど殆ど無い。これらの語はテクスト中で統
語的,文法的に存在するが,意味的,文学的には空気の
ような無標のものでしかない。
9
12. Table 1. Christmas Booksを構成する五作品
Abbrev. Title Date Total of tokens Tokens in dialogue
Carol A Christmas Carol 1843 28,420 7,917
Chimes The Chimes 1844 30,805 13,240
Cricket The Cricket on the Hearth 1845 31,832 11,627
Battle The Battle of Life 1846 29,598 13,325
Haunted The Haunted Man 1848 33,949 15,559
11
17. 6.0
4.0 X
É Mrs TETTERBY
Tackleton X
Tetterby
É
CLEMENCY
2.0 ÉÉ Toby
Scrooge XDOTÉ
É Caleb
Alderman MEGX É
PC 2 (11.84%)
0.0 É John X
William É MARION
É Alfred
Redlaw É X XMILLY
Snitchey É Philip BERTHA
ÉÉ
-2.0 Warden Will
É
Dr Jeddler
-4.0
-6.0 É
Ghost
É
Sir Joseph
-8.0
-8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0
PC 1 (15.04%)
Fig. 1 Christmas Booksの主要登場人物24人の相互関係:高頻度語60タイプを変数として 16
18. 0.3
not
do
are you it
0.2
what know all I
there how dear
come if was
she
0.1 that(d) am had
good very her
me
they never
to(i) at
PC 2 (11.84%)
will(md) been so(a.d.)
here we have when
is one for
0 can
that(c)
or would
your but
a to(p)
him
on(p) be has he
-0.1 as
and
my
of
with Frequency
no(det)
-0.2 the
this his
0 20 40
in
-0.3
-0.3 -0.2 -0.1 0 0.1 0.2 0.3
PC 1 (15.03%)
Fig. 2 Christmas Booksの高頻度語60タイプの相互関係 17
19. 6.0
4.0 X
É Mrs TETTERBY
Tackleton X
Tetterby
É
CLEMENCY
2.0 ÉÉ Toby
Scrooge XDOTÉ
É Caleb
Alderman MEGX É
PC 2 (11.84%)
0.0 É John X
William É MARION
É Alfred
Redlaw É X XMILLY
Snitchey É Philip BERTHA
ÉÉ
-2.0 Warden Will
É
Dr Jeddler
-4.0
-6.0 É
Ghost
É
Sir Joseph
-8.0
-8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0
PC 1 (15.04%)
Fig. 1 Christmas Booksの主要登場人物24人の相互関係:高頻度語60タイプを変数として 18
20. 6.0
4.0 X
É Mrs TETTERBY
Tackleton X
Tetterby
É
CLEMENCY
2.0 ÉÉ Toby
Scrooge XDOTÉ
É Caleb
Alderman MEGX É
PC 2 (11.84%)
0.0 É John X
William É MARION
É Alfred
Redlaw É X XMILLY
Snitchey É Philip BERTHA
ÉÉ
-2.0 Warden Will
É
Dr Jeddler
-4.0
É Male characters
-6.0 É
Ghost
É X FEMALE CHARACTERS
Sir Joseph
-8.0
-8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0
PC 1 (15.04%)
Fig. 1 Christmas Booksの主要登場人物24人の相互関係:高頻度語60タイプを変数として 19
21. 6.0
4.0 X
É Mrs TETTERBY
Tackleton X
Tetterby
É
CLEMENCY
2.0 ÉÉ Toby
Scrooge XDOTÉ
É Caleb
Alderman MEGX É
PC 2 (11.84%)
0.0 É John X
William É MARION
É Alfred
Redlaw É X XMILLY
Snitchey É Philip BERTHA
ÉÉ
-2.0 Warden Will
É
Dr Jeddler
-4.0
É Male characters
-6.0 É
Ghost
É X FEMALE CHARACTERS
Sir Joseph
-8.0
-8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0
PC 1 (15.04%)
Fig. 1 Christmas Booksの主要登場人物24人の相互関係:高頻度語60タイプを変数として 19
22. 6.0
4.0 X
É Mrs TETTERBY
Tackleton X
Tetterby
É
CLEMENCY
2.0 ÉÉ Toby
Scrooge XDOTÉ
É Caleb
Alderman MEGX É
PC 2 (11.84%)
0.0 É John X
William É MARION
É Alfred
Redlaw É X XMILLY
Snitchey É Philip BERTHA
ÉÉ
-2.0 Warden Will
É
Dr Jeddler
-4.0
É Male characters
-6.0 É
Ghost
É X FEMALE CHARACTERS
Sir Joseph
-8.0
-8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0
PC 1 (15.04%)
Fig. 1 Christmas Booksの主要登場人物24人の相互関係:高頻度語60タイプを変数として 20
23. 6.0
4.0 X
É Mrs TETTERBY
Tackleton X
Tetterby
É
CLEMENCY
2.0 ÉÉ Toby
Scrooge XDOT É
É Caleb
Alderman MEGX É
PC 2 (11.84%)
0.0 É John X
William É MARION
É Alfred
Redlaw É X XMILLY
Snitchey É Philip BERTHA
Dominant ÉÉ Submissive
-2.0 Warden Will
É
支配的 Dr Jeddler 順良
威圧的 献身的
-4.0
強権的 自己犠牲的
É Male characters
-6.0 É
Ghost
É X FEMALE CHARACTERS
Sir Joseph
-8.0
-8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0
PC 1 (15.04%)
Fig. 1 Christmas Booksの主要登場人物24人の相互関係:高頻度語60タイプを変数として 20
24. 6.0
4.0 X
É Mrs TETTERBY
Tackleton X
Tetterby
É
CLEMENCY
2.0 ÉÉ Toby
Scrooge XDOTÉ
É Caleb
Alderman MEGX É
PC 2 (11.84%)
0.0 É John X
William É MARION
É Alfred
Redlaw É X XMILLY
Snitchey É Philip BERTHA
ÉÉ
-2.0 Warden Will
É
Dr Jeddler
-4.0
É Male characters
-6.0 É
Ghost
É X FEMALE CHARACTERS
Sir Joseph
-8.0
-8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0
PC 1 (15.04%)
Fig. 1 Christmas Booksの主要登場人物24人の相互関係:高頻度語60タイプを変数として 21
25. 6.0
対話的
口語的
4.0 庶民的 X
É Mrs TETTERBY
Tackleton X
Tetterby
É
CLEMENCY
2.0 ÉÉ Toby
Scrooge XDOTÉ
É Caleb
Alderman MEGX É
PC 2 (11.84%)
0.0 É John X
William É MARION
É Alfred
Redlaw É X XMILLY
Snitchey É Philip BERTHA
ÉÉ
-2.0 Warden Will
É
Dr Jeddler
-4.0 独白的
衒学的
‘モンスター’ É Male characters
-6.0 É
Ghost
É X FEMALE CHARACTERS
Sir Joseph
-8.0
-8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0
PC 1 (15.04%)
Fig. 1 Christmas Booksの主要登場人物24人の相互関係:高頻度語60タイプを変数として 21
36. 6.0
4.0 X
É Mrs TETTERBY
Tackleton X
Tetterby
É
CLEMENCY
2.0 ÉÉ Toby
Scrooge XDOTÉ
É Caleb
Alderman MEGX É
PC 2 (11.84%)
0.0 É John X
William É MARION
É Alfred
Redlaw É X XMILLY
Snitchey É Philip BERTHA
ÉÉ
-2.0 Warden Will
É
Dr Jeddler
-4.0
É Male characters
-6.0 É
Ghost
É X FEMALE CHARACTERS
Sir Joseph
-8.0
-8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0
PC 1 (15.04%)
Fig. 1
Principal component scores for the 24 major characters:
based on the 30 most common word-types in the corpus 32
37. 6.0
Interpersonal,
vernacular
4.0 X
É Mrs TETTERBY
Tackleton X
Tetterby
É
CLEMENCY
2.0 ÉÉ Toby
Scrooge XDOT É
É Caleb
Alderman MEGX É
PC 2 (11.84%)
0.0 É John X
William É MARION
É Alfred
Redlaw É X XMILLY
Snitchey É Philip BERTHA
ÉÉ
-2.0 Warden Will
É
Dr Jeddler
-4.0
É Male characters
-6.0 É
Monologic, Ghost
É X FEMALE CHARACTERS
Latinate Sir Joseph
-8.0
-8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0
Dominant PC 1 (15.04%) Submissive
Fig. 1
Principal component scores for the 24 major characters:
based on the 30 most common word-types in the corpus 32
38. 0.3
not
do
are you it
0.2
what know all I
there how dear
come if was
she
0.1 that(d) am had
good very her
me
they never
to(i) at
PC 2 (11.84%)
will(md) been so(a.d.)
here we have when
is one for
0 can
that(c)
or would
your but
a to(p)
him
on(p) be has he
-0.1 as
and
my
of
with Frequency
no(det)
-0.2 the
this his
0 20 40
in
-0.3
-0.3 -0.2 -0.1 0 0.1 0.2 0.3
PC 1 (15.03%)
Fig. 2 Principal Components Analysis of the 30 most common word-types in the
language of dialogue: based on the 24 major characters 33
39. 0.3
not
Interpersonal, do
vernacular are you it
0.2
what know all I
there how dear
come if was
she
0.1 that(d) am had
good very her
me
they never
to(i) at
PC 2 (11.84%)
will(md) been so(a.d.)
here we have when
is one for
0 can
that(c)
or would
your but
a to(p)
him
on(p) be has he
-0.1 as
and
my
of
with Frequency
no(det)
Monologic, -0.2 the
Latinate this his
0 20 40
in
-0.3
-0.3 -0.2 -0.1 0 0.1 0.2 0.3
Dominant PC 1 (15.03%)
Submissive
Fig. 2 Principal Components Analysis of the 30 most common word-types in the
language of dialogue: based on the 24 major characters 33
40. 6.0
4.0 X
É Mrs TETTERBY
Tackleton X
Tetterby
É
CLEMENCY
2.0 ÉÉ Toby
Scrooge XDOTÉ
É Caleb
Alderman MEGX É
PC 2 (11.84%)
0.0 É John X
William É MARION
É Alfred
Redlaw É X XMILLY
Snitchey É Philip BERTHA
ÉÉ
-2.0 Warden Will
É
Dr Jeddler
-4.0
É Male characters
-6.0 É
Ghost
É X FEMALE CHARACTERS
Sir Joseph
-8.0
-8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0
PC 1 (15.04%)
Fig. 1 Christmas Booksの主要登場人物24人の相互関係:高頻度語60タイプを変数として 34
41. 6.0
4.0 X
É Mrs TETTERBY
Tackleton X
Tetterby
É
CLEMENCY
2.0 ÉÉ Toby
Scrooge XDOTÉ
É Caleb
Alderman MEGX É
PC 2 (11.84%)
0.0 É John X
William É MARION
É Alfred
Redlaw É X XMILLY
Snitchey É Philip BERTHA
ÉÉ
-2.0 Warden Will
É
Dr Jeddler
-4.0
É Male characters
-6.0 É
Ghost
É X FEMALE CHARACTERS
Sir Joseph
-8.0
-8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0
PC 1 (15.04%)
Fig. 1 Christmas Booksの主要登場人物24人の相互関係:高頻度語60タイプを変数として 34
42. 3.0
Tackleton Tetterby
Alderman É É
2.0 Scrooge Mrs TETTERBY
É
É X
BERTHA
1.0 Redlaw Caleb DOT X
PC 2 (9.78%)
X MARION
É É X CLEMENCY X
0.0 Toby É Will John MILLY
É
Warden É Alfred ÉÉ MEG X
William É X
-1.0 Philip É
Dr Jeddler Snitchey É Male characters
-2.0 É É
Ghost
Sir Joseph É É X FEMALE CHARACTERS
-3.0
-6.0 -4.0 -2.0 0.0 2.0 4.0 6.0
PC 1 (33.44%)
Fig. 3
言語と性差:21項目の性差マーカーの生起頻度に基づく
35
43. 0.6
Frequency/1,000
you
0.5
0 20 40
0.4
will(md)
0.3 me
0.2 what dear
good had
PC 2 (9.8%)
how never
0.1 so(a.d.)
was
0 when
is
him
-0.1 that(c)
of would
-0.2
a
-0.3 the
he and
-0.4
-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4
PC 1 (33.4%)
Fig. 4
Gender-oriented Difference in idiolects: Word plot for
the 21 most common ‘marker’ words in the corpus
36
44. 140
E Male Characters
X FEMALE CHARACTERS Scrooge#4
H
120 H Scrooge in 5 Parts
Scrooge#5
H
100
Sir Joseph Scrooge#3
Alderman E H
E
Scrooge#2
H Caleb
E
PC 3 (12.97%)
80 Dr Jeddler Tackleton
E EGhost
E
E Redlaw
Toby E
William E
MILLY X
Scrooge#1 Will E X BERTHA
H
60 Alfred
Tetterby EE EJohn
Snitchey X MEG Warden
E
E X DOT
40 X
CLEMENCY MRS TETTERBY
X
Philip
E
20
MARION
X
0
-60 -40 -20 0 20 40 60 80
PC 2 (16.22%)
Fig. 5
A Christmas Carol における Scrooge 改心の軌跡:
頻度上位 20 タイプの生起頻度行列に基づく主成分分析の結果 37
45. 140
E Male Characters
X FEMALE CHARACTERS Scrooge#4
H
120 H Scrooge in 5 Parts
Scrooge#5
H
100
Sir Joseph Scrooge#3
Alderman E H
E
Scrooge#2
H Caleb
E
PC 3 (12.97%)
80 Dr Jeddler Tackleton
E EGhost
E
E Redlaw
Dominant William
Toby
E X
E Submissive
Scrooge#1 MILLY
支配的 H
Will E X BERTHA 順良
60 Alfred
威圧的 Tetterby EE EJohn 献身的
Snitchey X MEG Warden
強権的 E X DOT
E
自己犠牲的
40 X
CLEMENCY MRS TETTERBY
X
Philip
E
20
MARION
X
0
-60 -40 -20 0 20 40 60 80
PC 2 (16.22%)
Fig. 5
A Christmas Carol における Scrooge 改心の軌跡:
頻度上位 20 タイプの生起頻度行列に基づく主成分分析の結果 37
46. Important thematic contrasts like those between
social superiors and social inferiors, callousness versus
sympathy, cynicism versus family love and fellow
feeling, and so on, are underscored by the
differentiation of idiolects or the variation within a
single idiolect.
38
47. Among the words lying to the LEFT in Fig 1 and
characterise the idiolects of characters demonstrating
dominance and habits of command, the first person
pronoun you seems to play the most significant role,
and other words, such as are and will, function in
relation to you or, to put it in another way, they are in
frequent co-occurrence with you.
39
48. (1)A marked recourse to you know
‘It’s my place to give advice, you know, because I’m a
Justice. You know I’m a Justice, don’t you?’ (Chimes:
172)
‘You will come to the wedding? We are in the same
boat, you know’ (Cricket: 43).
40
49. (2) Assertive statements using you
‘What right have you to be merry? what reason have you to
be merry? You’re poor enough.’ (Scrooge, Carol: 48); ‘After you
are married, you’ll quarrel with your husband, and come to be
a distressed wife. You may think not: but you will, because I tell
you so’ (Alderman, Chimes: 172).
41
50. (3) Impolite vocatives:
Tackleton addresses John with ‘you dog!’ (Cricket: 45), Edward
with ‘you vagabond’ (113), and Alderman Cute calls Richard ‘you
dull dog’ (Chimes: 173), and ‘you silly fellow’ (173).
These vocatives stand in marked contrast with more intimate
vocatives, like ‘my dear’ or ‘my darling’ to which gentle and
tender-hearted characters, such as Toby, Caleb, John, Philip, and
so on, frequently resort. The differences in the use of vocatives
indicate, with concise expressiveness, the relationships between
the characters as well as their attitude to each other.
42
51. (4) The sarcastic or taunting use of ‘you’.
This type of usage frequently appears in Scrooge’s idiolect
before he is haunted by the ghosts: (in the scene where his
nephew invites Scrooge to the Christmas dinner)
‘What LEFT have you to be merry? what reason have you to
be merry? You’re poor enough.’ (Carol: 48);
in The Chimes it is often seen in the Alderman’s relentless
bantering of Meg:
‘After you are married, you’ll quarrel with your husband, and
come to be a distressed wife. You may think not: but you will,
because I tell you so’ (Chimes: 172).
43
Editor's Notes
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
As Table 1 shows, Christmas Books is a set of five texts published annually in 1843-1848 with the exception of 1847. The following analysis is based on the corpus of the language of dialogue in Christmas Books.\n
Tables 2 give a list of the 30 most common word-types1 in the descending order of their frequency for each of the 24 major characters. \n
Tables 2 give a list of the 30 most common word-types1 in the descending order of their frequency for each of the 24 major characters. \n
For the purpose of this study, the ‘major characters’ comprise the twenty-three who speak more than 1200 words apiece and Alderman Cute (Chimes) who speaks 1192 words. The threshold should be set here because if a character’s total word-tokens are too small in number, the statistical analysis might not ensure stable results. \n
Principal Components Analysis (PCA) is one of the linear techniques for reducing a large number of variables to a smaller number of components so that (usually the first few) principal components account for relationships among sets of many interrelated variables.\n
Fig 1 represent interrelationships among the 24 characters. Fig 1, which plots the first two PCs, shows similarity, resemblance or otherwise among the idiolects in terms of their frequency-profiles. Points (characters) close to each other exhibit a similar tendency in the use of the very common words.\n\n
Fig 2, which projects the two strongest PCs, shows similarity or contrast among the words in their pattern of high or low frequencies over the range of the 24 idiolects. Again, points (words) in the close proximity to each other share a similar pattern over the 24 characters. Words in the positive area and those in the negative area along the vertical, or the horizontal, axis in Fig 2 are mutually opposed: that is, when a set of words run high in one character’s idiolect, the other tend to fall away.\n
It is important to note, since Figs 1 and 2 correspond to each other, a comparison of the two plots will lead us to realise that characters lying in the right of Fig 1 are given to the frequent use of words lying in the same direction (and comparative avoidance of words in opposite direction) in Fig 2 and vice versa, and that those lying towards the top of Fig 1 are characterised by a pronounced recourse to words located towards the top (and a relatively sparse use of words lying to the bottom) of Fig 2 and vice versa.\n\n
Another notable feature is that most female characters find themselves in the upper RIGHT quadrant, and that, with an imaginary diagonal line running from the upper-LEFT to the lower-bottom as a rough boundary, the formal speakers lie in the lower-LEFT triangle and the colloquial speakers in the upper-RIGHT triangle respectively. Two seeming anomalies (Redlaw and Snitchey) will be given due attention later.\n\n
To the RIGHT, there are Milly, another gentle and self-devoted woman, Clemency, who can be considered as a forerunner of Peggotty in David Copperfield, John the honest uxorious carrier, Caleb the toy-maker, who gives infinite paternal love to his blind daughter, Will, Philip, and Toby, who also share similar characteristics.\n\n
To the RIGHT, there are Milly, another gentle and self-devoted woman, Clemency, who can be considered as a forerunner of Peggotty in David Copperfield, John the honest uxorious carrier, Caleb the toy-maker, who gives infinite paternal love to his blind daughter, Will, Philip, and Toby, who also share similar characteristics.\n\n
To the RIGHT, there are Milly, another gentle and self-devoted woman, Clemency, who can be considered as a forerunner of Peggotty in David Copperfield, John the honest uxorious carrier, Caleb the toy-maker, who gives infinite paternal love to his blind daughter, Will, Philip, and Toby, who also share similar characteristics.\n\n
To the RIGHT, there are Milly, another gentle and self-devoted woman, Clemency, who can be considered as a forerunner of Peggotty in David Copperfield, John the honest uxorious carrier, Caleb the toy-maker, who gives infinite paternal love to his blind daughter, Will, Philip, and Toby, who also share similar characteristics.\n\n
To the RIGHT, there are Milly, another gentle and self-devoted woman, Clemency, who can be considered as a forerunner of Peggotty in David Copperfield, John the honest uxorious carrier, Caleb the toy-maker, who gives infinite paternal love to his blind daughter, Will, Philip, and Toby, who also share similar characteristics.\n\n
To the RIGHT, there are Milly, another gentle and self-devoted woman, Clemency, who can be considered as a forerunner of Peggotty in David Copperfield, John the honest uxorious carrier, Caleb the toy-maker, who gives infinite paternal love to his blind daughter, Will, Philip, and Toby, who also share similar characteristics.\n\n
To the RIGHT, there are Milly, another gentle and self-devoted woman, Clemency, who can be considered as a forerunner of Peggotty in David Copperfield, John the honest uxorious carrier, Caleb the toy-maker, who gives infinite paternal love to his blind daughter, Will, Philip, and Toby, who also share similar characteristics.\n\n
To the RIGHT, there are Milly, another gentle and self-devoted woman, Clemency, who can be considered as a forerunner of Peggotty in David Copperfield, John the honest uxorious carrier, Caleb the toy-maker, who gives infinite paternal love to his blind daughter, Will, Philip, and Toby, who also share similar characteristics.\n\n
To the RIGHT, there are Milly, another gentle and self-devoted woman, Clemency, who can be considered as a forerunner of Peggotty in David Copperfield, John the honest uxorious carrier, Caleb the toy-maker, who gives infinite paternal love to his blind daughter, Will, Philip, and Toby, who also share similar characteristics.\n\n
To the RIGHT, there are Milly, another gentle and self-devoted woman, Clemency, who can be considered as a forerunner of Peggotty in David Copperfield, John the honest uxorious carrier, Caleb the toy-maker, who gives infinite paternal love to his blind daughter, Will, Philip, and Toby, who also share similar characteristics.\n\n
Along the vertical axis, we can see a gradual transition from a highly inter-personal, subjective, and simple style (the top) to a highly impersonal, descriptive, and disquisitory style (the bottom). \n
Along the vertical axis, we can see a gradual transition from a highly inter-personal, subjective, and simple style (the top) to a highly impersonal, descriptive, and disquisitory style (the bottom). \n
Along the vertical axis, we can see a gradual transition from a highly inter-personal, subjective, and simple style (the top) to a highly impersonal, descriptive, and disquisitory style (the bottom). \n
Along the vertical axis, we can see a gradual transition from a highly inter-personal, subjective, and simple style (the top) to a highly impersonal, descriptive, and disquisitory style (the bottom). \n
Along the vertical axis, we can see a gradual transition from a highly inter-personal, subjective, and simple style (the top) to a highly impersonal, descriptive, and disquisitory style (the bottom). \n
Along the vertical axis, we can see a gradual transition from a highly inter-personal, subjective, and simple style (the top) to a highly impersonal, descriptive, and disquisitory style (the bottom). \n
Along the vertical axis, we can see a gradual transition from a highly inter-personal, subjective, and simple style (the top) to a highly impersonal, descriptive, and disquisitory style (the bottom). \n
Along the vertical axis, we can see a gradual transition from a highly inter-personal, subjective, and simple style (the top) to a highly impersonal, descriptive, and disquisitory style (the bottom). \n
Important thematic contrasts like those between social superiors and social inferiors, callousness versus sympathy, cynicism versus family love and fellow feeling, and so on, are underscored by the differentiation of idiolects or the variation within a single idiolect. \nThese results are not engineered but made visible as a result of letting the data speak for themselves.\n\n
Important thematic contrasts like those between social superiors and social inferiors, callousness versus sympathy, cynicism versus family love and fellow feeling, and so on, are underscored by the differentiation of idiolects or the variation within a single idiolect. \nThese results are not engineered but made visible as a result of letting the data speak for themselves.\n\n
Important thematic contrasts like those between social superiors and social inferiors, callousness versus sympathy, cynicism versus family love and fellow feeling, and so on, are underscored by the differentiation of idiolects or the variation within a single idiolect. \nThese results are not engineered but made visible as a result of letting the data speak for themselves.\n\n
Important thematic contrasts like those between social superiors and social inferiors, callousness versus sympathy, cynicism versus family love and fellow feeling, and so on, are underscored by the differentiation of idiolects or the variation within a single idiolect. \nThese results are not engineered but made visible as a result of letting the data speak for themselves.\n\n
Important thematic contrasts like those between social superiors and social inferiors, callousness versus sympathy, cynicism versus family love and fellow feeling, and so on, are underscored by the differentiation of idiolects or the variation within a single idiolect. \nThese results are not engineered but made visible as a result of letting the data speak for themselves.\n\n
Important thematic contrasts like those between social superiors and social inferiors, callousness versus sympathy, cynicism versus family love and fellow feeling, and so on, are underscored by the differentiation of idiolects or the variation within a single idiolect. \nThese results are not engineered but made visible as a result of letting the data speak for themselves.\n\n
Important thematic contrasts like those between social superiors and social inferiors, callousness versus sympathy, cynicism versus family love and fellow feeling, and so on, are underscored by the differentiation of idiolects or the variation within a single idiolect. \nThese results are not engineered but made visible as a result of letting the data speak for themselves.\n\n
Important thematic contrasts like those between social superiors and social inferiors, callousness versus sympathy, cynicism versus family love and fellow feeling, and so on, are underscored by the differentiation of idiolects or the variation within a single idiolect. \nThese results are not engineered but made visible as a result of letting the data speak for themselves.\n\n
Important thematic contrasts like those between social superiors and social inferiors, callousness versus sympathy, cynicism versus family love and fellow feeling, and so on, are underscored by the differentiation of idiolects or the variation within a single idiolect. \nThese results are not engineered but made visible as a result of letting the data speak for themselves.\n\n
Important thematic contrasts like those between social superiors and social inferiors, callousness versus sympathy, cynicism versus family love and fellow feeling, and so on, are underscored by the differentiation of idiolects or the variation within a single idiolect. \nThese results are not engineered but made visible as a result of letting the data speak for themselves.\n\n
Overall pattern of character differentiation based the frequency of function words.\n
Overall pattern of character differentiation based the frequency of function words.\n
Overall pattern of character differentiation based the frequency of function words.\n
Overall pattern of character differentiation based the frequency of function words.\n
Overall pattern of character differentiation based the frequency of function words.\n
Overall pattern of character differentiation based the frequency of function words.\n
Overall pattern of character differentiation based the frequency of function words.\n
Overall pattern of character differentiation based the frequency of function words.\n
\n
\n
\n
\n
\n
\n
\n
\n
Another notable feature is that most female characters find themselves in the upper RIGHT quadrant, and that, with an imaginary diagonal line running from the upper-LEFT to the lower-bottom as a rough boundary, the formal speakers lie in the lower-LEFT triangle and the colloquial speakers in the upper-RIGHT triangle respectively. Two seeming anomalies (Redlaw and Snitchey) will be given due attention later.\n\n
This is the first of the results of CA.\nIn this diagram, items close to each other are similar to each other. The greater the distance between text symbols, the larger difference they have in the collocation of “gentleman”.\n
This is the first of the results of CA.\nIn this diagram, items close to each other are similar to each other. The greater the distance between text symbols, the larger difference they have in the collocation of “gentleman”.\n
To the RIGHT, there are Milly, another gentle and self-devoted woman, Clemency, who can be considered as a forerunner of Peggotty in David Copperfield, John the honest uxorious carrier, Caleb the toy-maker, who gives infinite paternal love to his blind daughter, Will, Philip, and Toby, who also share similar characteristics.\n\n
To the RIGHT, there are Milly, another gentle and self-devoted woman, Clemency, who can be considered as a forerunner of Peggotty in David Copperfield, John the honest uxorious carrier, Caleb the toy-maker, who gives infinite paternal love to his blind daughter, Will, Philip, and Toby, who also share similar characteristics.\n\n
To the RIGHT, there are Milly, another gentle and self-devoted woman, Clemency, who can be considered as a forerunner of Peggotty in David Copperfield, John the honest uxorious carrier, Caleb the toy-maker, who gives infinite paternal love to his blind daughter, Will, Philip, and Toby, who also share similar characteristics.\n\n
To the RIGHT, there are Milly, another gentle and self-devoted woman, Clemency, who can be considered as a forerunner of Peggotty in David Copperfield, John the honest uxorious carrier, Caleb the toy-maker, who gives infinite paternal love to his blind daughter, Will, Philip, and Toby, who also share similar characteristics.\n\n
Important thematic contrasts like those between social superiors and social inferiors, callousness versus sympathy, cynicism versus family love and fellow feeling, and so on, are underscored by the differentiation of idiolects or the variation within a single idiolect. \nThese results are not engineered but made visible as a result of letting the data speak for themselves.\n\n
We will see this if we examine uses of you in idiolects of typical authoritarians, Alderman Cute, in whose speech you ranks the topmost of all the word-types, and Tackleton (Cricket), who uses it with the second highest frequency.\nThe use of you associated with dominance and habits of command can be roughly classified into four categories. \n\n
(1) The first type is a marked recourse to you know to demand that the addressee should agree with the addresser’s statements. A typical example is seen in the passage where the Alderman discourages Meg from marrying her poor fiancé: ‘It’s my place to give advice, you know, because I’m a Justice. You know I’m a Justice, don’t you?’ (Chimes: 172), or where Tackleton requires John and his wife to come to his wedding with pressing hospitality: ‘You will come to the wedding? We are in the same boat, you know’ (Cricket: 43). The use of tag question form, are you or don’t you, is of a similar nature. \n\n
(2) The second type is a case of Assertive statements using you. Frequent use of this type of expression seems to illustrate the application of physical force or moral pressure or exercise of authority to gain power over the inferiors and to make them obedient.\n\n
(3) The third type is the use of an impolite vocative: for instance, Tackleton addresses John with ‘you dog!’ (Cricket: 45), Edward with ‘you vagabond’ (113), and Alderman Cute calls Richard ‘you dull dog’ (Chimes: 173), and ‘you silly fellow’ (173). These vocatives stand in marked contrast with more intimate vocatives, like ‘my dear’ or ‘my darling’ to which gentle and tender-hearted characters, such as Toby, Caleb, John, Philip, and so on, frequently resort. The differences in the use of vocatives indicate, with concise expressiveness, the relationships between the characters as well as their attitude to each other.\n\n
(4) The fourth category is the sarcastic or taunting use of ‘you’. This type of usage frequently appears in Scrooge’s idiolect before he is haunted by the ghosts: (in the scene where his nephew invites Scrooge to the Christmas dinner) ‘What LEFT have you to be merry? what reason have you to be merry? You’re poor enough.’ (Carol: 48); in The Chimes it is often seen in the Alderman’s relentless bantering of Meg: ‘After you are married, you’ll quarrel with your husband, and come to be a distressed wife. You may think not: but you will, because I tell you so’ (Chimes: 172).\n\n
The garrulous Mrs Tetterby, Clemency the unintellectual but good servant, and Tackleton occupy the positive end. The pompous, socially-conscious Sir Joseph, and the ghost of Redlaw the man of ‘higher cultivation and profounder thought’ (Haunted: 269)2 are found at the bottom.\n\n
The garrulous Mrs Tetterby, Clemency the unintellectual but good servant, and Tackleton occupy the positive end. The pompous, socially-conscious Sir Joseph, and the ghost of Redlaw the man of ‘higher cultivation and profounder thought’ (Haunted: 269)2 are found at the bottom.\n\n
The garrulous Mrs Tetterby, Clemency the unintellectual but good servant, and Tackleton occupy the positive end. The pompous, socially-conscious Sir Joseph, and the ghost of Redlaw the man of ‘higher cultivation and profounder thought’ (Haunted: 269)2 are found at the bottom.\n\n
The garrulous Mrs Tetterby, Clemency the unintellectual but good servant, and Tackleton occupy the positive end. The pompous, socially-conscious Sir Joseph, and the ghost of Redlaw the man of ‘higher cultivation and profounder thought’ (Haunted: 269)2 are found at the bottom.\n\n