Experiments with Speculative Fiction in HathiTrust
THE FIRST MEN IN
THE MOON
MR. BEDFORD MEETS MR. CAVOR AT LYMPNE
As I sit down to write here amidst the
shadows of vine-leaves under the blue sky of
southern Italy, it comes to me with a certain
quality of astonishment that my participation
in these amazing adventures of Mr. Cavor
was, after all, the outcome of the purest acci-
dent. It might have been any one. I fell
into these things at a time when I thought
myself removed from the slightest possibility
of disturbing experiences. I had gone to
Lympne because I had imagined it the most
uneventful place in the world. " Here, at any
rate," said I, " I shall find peace and a chance
to work ! "
' And this book is the sequel. So utterly at
.json file no longer readable by human eyes (“consumptive” reading), yet containing “quantitative abstractions of a book’s written content” that we can explore through text analysis (“non-consumptive” reading):
":1,"l":1,"r":1,"o":1},"tokenPosCount":{"rate":
{"NN":1},"accident":{"NN":1},"IN":{"IN":1},
"astonishment":{"NN":1},"down":{"RB":1},
"slightest":{"JJS":1},"quality":{"NN":1},"find":
{"VB":1},"disturbing":{"JJ":1},"AT":{"IN":1},
"vine-leaves":{"NNS":1},"any":{"DT":2},"southern":
{"JJ":1},"myself":{"PRP":1},"have":{"VB":1},"is":
{"VBZ":1},"MOON":{"NN":1},"said":{"VBD":1},
"Lympne":{"NNP":1},"sit":{"VBP":1},"thought":
{"VBD":1},".":{".":5},"adventures":{"NNS":1},
"blue":{"JJ":1},"THE":{"DT":2},"world":{"NN":1},
"fell":{"VBD":1},"CAVOR":{"NNP":1},"all":{"DT":1},
"book":{"NN":1},"had":{"VBD":2},"imagined":
{"VBN":1},"it":{"PRP":2},"!":{".":1},"A":{"DT":1},
"a":{"DT":3},"And":{"CC":1},"utterly":{"RB":1},
"sky":{"NN":1},"shadows":{"NNS":1},"outcome":
{"NN":1},"Here":{"RB":1},"because":{"IN":1},"Mr.":
{"NNP":1},"purest":{"JJS":1},"removed":{"VBD":1},
"certain":{"JJ":1},"comes":{"VBZ":1},"MEN":
{"NNP":1},"I":{"PRP":7},"LYMPNE":{"NNP":1},"work":
{"VB":1},"that":{"WDT":1},"possibility":{"NN":1},
"to":{"TO":4},"participation":{"NN":1},"MEETS":
{"VBZ":1},",":{",":6},"most":{"RBS":1},"here":
{"RB":1},"these":{"DT":2},"was":{"VBD":1},"at":
{"IN":3},"been":{"VBN":1},"FIRST":{"NNP":1},"'":
{"''":1},"my":{"PRP$":1}
.json sample above is a feature, a “quantifiable marker of something measurable, a datum,” as Peter Organisciak and Boris Capitanu put it in their Programming Historian tutorial on text mining with HTRC. They continue:
A computer cannot understand the meaning of a sentence implicitly, but it can understand the counts of various words and word forms, or the presence or absence of stylistic markers, from which it can be trained to better understand text. Many text features are non-consumptive in that they don’t retain enough information to reconstruct the book text.