![]() ![]() ![]() The distribution of tags is very skewed: a few tags are very frequent, but the frequencies drop off rapidly. What kind of distribution do you see in the histogram, and how does it compare to the histogram of sentence lengths? 5 least frequent tags are SYM (Symbols), UH (interjection), FW (foreign words), LS (list marker) and WP$ (possessive wh-pronoun). NN, IN, NNP, DT, NNS labels for common noun, preposition, proper noun, determiner, and plural common noun respectively are the 5 most frequent tags. How does this compare with your intuition in the previous section ? What are the 5 most frequent and least frequent tags in this data. Sorted(tag_ems(), key=lambda x: x, reverse=True) List the tags in the descreasing order of frequency. How many distinct tags are present in the data ? Plot_histogram(sorted(tag_ems(), key=lambda x: x, reverse=True)) Using plot_histogram, plot a histogram of the tag distribution with tags on the x-axis and their counts on the y-axis, ordered by descending frequency. VB (base form), VBD (past tense), VBG (gerund/present participle), VBN (past participle), VBP (sing. There are six different tags for main verbs. How many different tags are available for main verbs and what are they? NN (noun singular) and NNP (proper noun singular) are the tags for singular nouns and NNS (noun plural) and NNPS (proper noun plural) are the tags for plural nouns. We can distinguish singular and plural nouns in this tagset. For example, in "both the books", "the" is a determiner and "both" is a pre-determiner.Ĭan you distinguish singular and plural nouns using this tagset? If so, how? As the name says, pre-determiners occur before determiners. What is the difference between the tags DT and PDT?ĭT is the POS tag for determiners and PDT is the tag for pre-determiners. Tags like UH (interjection), LS (list marker) might be less common. In English data, determiners (DT), nouns (NN, NNS, NNP etc.), verbs (VB, VBD, VBP etc.), prepositions (IN) might be more frequent. Based on your intution guess the most and least frequent tags in a data. ![]()
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