"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#set up standard corpora + vectors\n",
"vfair_all = Setup.make_standard_sampler_and_vecs('vfair',5,100,1) #window=5, dims=100, min_count=1\n",
"heartd_all = Setup.make_standard_sampler_and_vecs('heartd',5,100,1) #window=5, dims=100, min_count=1\n",
"\n",
"what = [['Vanity Fair (vfair)'],['Heart of Darkness (heartd)']]\n",
"for i,c in enumerate([vfair_all,heartd_all]):\n",
" sampler = c['sampler']\n",
" what[i].extend([sum(sampler.counts.values()), len(sampler.counts)])\n",
"\n",
"show_table(what, headers=['Corpus','Tokens','Types'], title=\"Corpora sizes\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Although I focus on small corpora for the examples, **the general points and principles hold for word embeddings for large corora as well**, as I will show in a separate post (and as we already saw in the introduction with the GloVe vectors). In other words, the work here is relevant to _downstream applications_ of word embeddings as well."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The distributions\n",
"\n",
"Let's take a look at (samples from) the distributions of similarities in _Vanity Fair_ (aka _vfair_). Each of the four methods is shown in turn."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"Similarity density distributions for vfair, 1000 samples"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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N55wrAe899R4AwycMz2r61u1bM+ioQWCw8J8LcxaHJw3nnCtyq+avYvWC1ezV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\nwF0WvAx0kdQ3X/E451w+zH1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