Chapter 2 - Comparison with Existing Semantic-Domain Lexica

There were no specific scripts written for Chapter 2, in which I compared the results of the distributional calculations described in this documentation to the semantic-domain categorization published in the Louw-Nida Greek-English Lexicon of the New Testament Based on Semantic Domains. Instead, after deciding which words I wanted to investigate (I chose words that had an abnormally low similarity to their primary semantic sub-domain), I simply extracted the top 100 most similar words using the following few lines typed into any Python interpreter:

import numpy as np
import pandas as pd
i = pd.read_pickle('/PATH/TO/NT/DATA/NT/12/NT_IndexList_w=12_lems=True_min_occs=1_no_stops=False.pickle')
d = np.memmap('/PATH/TO/NT/DATA/NT/12/LL_cosine_12_lems=True_NT_min_occ=1_SVD_exp=1.0_no_stops=False_weighted=False.dat', dtype='float', shape=(len(i), len(i)))
s = pd.Series(d[i.index(WORD_UNDER_INVESTIGATION)],

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