I'd guess that for articles related to "ocean", sea will show up 10x more than lake and river. My students are always looking to improve their vocabulary and reading skills. With enough text data for each word, you can build synonym distances. Kangaroo Words: A Synonyms, Antonyms, and Context Clues Practice Game.(The words beach, waves and shitload each show up once, interestingly.) Removing stop words, we can see that sea shows up 4 times, while the other "bodies of water" words don't show up. Text = anslate(string.maketrans("",""), string.punctuation) # remove punctuationįor word, count in word_freq.most_common(10): ‘The ocean’ originally denoted the whole body of water regarded as encompassing the earth's single land mass. Middle English: from Old French occean, via Latin from Greek ōkeanos ‘great stream encircling the earth's disc’. Synonyms: a lot, a great/large amount, a great/good deal, plenty, quantities, an abundance, a profusion informallots, loads, heaps, bags, masses, stacks, oodles, tons, scads "they scramble across the beach to the ocean and plunge into the surf" Literarythe deep, the waves, the main, the foam, the profound ![]() Text = '''noun: ocean plural noun: oceansĪ very large expanse of sea, in particular each of the main areas into which the sea is divided geographically. 36 other terms for in certain contexts- words and phrases with similar meaning. Using a simple definition as an example, we can parse the text and count the most frequent words: sample python 2.7 code # -*- coding: utf-8 -*. txt filesĬompute frequencies of other words (with same part of speech) that appear in the. Remove stop words such as the from each of the. Let's then download (scrape) as many dictionary, thesaurus and encyclopedia entries as possible, and dump the results as text files ocean.txt, sea.txt, etc. ![]() ![]() Let's consider only topical words, so ocean, sea, lake, pond, river, and stream. This isn't a super answer, but maybe it can start some discussion. In contrast, a bidirectional language model could also gain context from with and you, which might help the model generate.
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