The document discusses analyzing color term usage and frequency changes over time in texts from the Early English Books Online corpus. It notes things to consider like spelling variations, part of speech, and ambiguity, and recommends tools like the Corpus Query Processor website and semantic/wordclass annotation to help with the analysis. Finally, it acknowledges some limitations and challenges around the dataset, tagging accuracy, and visualizing large results.

1. Colour terms in EEBO
What colour terms are used when in the EEBO texts? How do
frequencies change? What words accompany colour terms?
Things to consider:
● what are colour terms and how do we find them?
● spelling variation
● inflected forms
● polysemy and other forms of ambiguity
● relative frequencies
Things that help:
● http://cqpweb.lancs.ac.uk/
● Wordclass annotation
● Semantic annotation
● Search, concordance, collocation, distribution, downloads
● Variant spellings in OED
Ylva Berglund Prytz, Sam Cottrell, @harryharrold, Martin Wynne

2. Colour terms
Because color exhibits both biological and linguistic aspects, it has
become a deeply studied domain that addresses the relationship between
language and thought.
(http://en.wikipedia.org/wiki/Linguistic_relativity_and_the_color_naming_d
ebate)
Today every natural language that has words for colors is considered to
have from two to twelve basic color terms. All other colors are considered
by most speakers of that language to be variants of these basic color
terms. English contains eleven basic color terms: "black", "white", "red",
"green", "yellow", "blue", "brown", "orange", "pink", "purple", and "gray".
(http://en.wikipedia.org/wiki/Color_term)

3. Methodology
Two datasets:
● All words tagged as part of the semantic field 'colour',
with 'year of publication' metadata
● For the colours of the rainbow, all occurrences for all
variant spellings (from the OED) tagged as
adjectives
In addition:
● For selected words, find all statistically significant
noun collocations

4. Words of caution
● Not entirely sure what's in the dataset at
cqpweb.lancs.ac.uk (44000 texts?)
● Semantic tagging not 100% accurate (and
never will be)
● Wordclass tagging not 100% accurate
● Difficult to visualize the larger datasets

7. 110473 white
87210 colour
73728 black
57386 red
47111 dyed
41917 green
41173 shadow
34275 bright
33939 golden
26418 colours
23263 yellow
17112 pale
15346 Golden
14986 Colours
13665 shadows
13570 Colour
13493 Black
13413 brightness
13038 purple
12060 Red
11021 dim
10685 ag
9807 coloured
9509 stain
8632 Green
7636 darkened
7033 stripes
7019 shade
6791 gray
61633 red
1820 sed
399 redder
197 reid
116 reddest
71 redest
17 readest
16 ried
9 riddest
7 rodest
6 ryddest
3 rydest
3 roder
3 roddest
2 seddest
2 ryid
2 raddest
1 ruddest
1 rottest
1 ridest
1 readdest