GPT-2 Generated “Natural Language Processing” Article 122

Input: Nlp will take jobs

Generated Text: from NLP nodes to perform basic transformations. To test this, start

# nltk python-2.7.6 import nltk

and enter the following into the interactive Shell and hit enter:

t_TRUE_P(x)

This is equivalent to

True

– matches anything

– matches everything

P(x)

– matches anything

– matches the sames token

x

P(P(P(P(P(P(

t_FALSE_P(x)

t_FALSE

t_TRUE_P(x)

t_TRUE

t_FALSE_P(x)

t_FALSE

t_TRUE_P(x)

t_TRUE

In fact, any Ruby pattern will do, it really

doesn’t matter what you put in.

After you enter a pattern, enter the required input and hit return.

Nltk will take the input and build a regular expression for you based on

the input.

Output

What you expected to see a) True b) [0123456789][0-9.]* c) [A-Z]* d) [0-9][A-Z]*/[^0-9]*/

Parses input.

Input: [‘A’..’Z’]

Parsed with:

[u’A’,’Z’].union([u’a’,’z’])

Output:

{u’a’: 1, u’tz’: 9}

Input: [‘a’,’z’..’_’]

Parsed with:

{x: 0, x1: 0, x2: 1, x3: 2, x4: 3, x5: 4}

Output:

{0: 2, 4: 152579, 5: 614, 7: 99899}

Input: (‘abcdefg’, u’t-aeiouy’..u”, u’tC-c’,’u’th-i’..’?’, u’Z’,

u’defghijkmeeentjiesztal’s_’..u’t0n1m’), {u’ZZ’: u’t0n3m’, u’defghijk’: u’seaxadagu’, u’syt1rer’,

u’syt2rer’, u’syt3rer’}, {u’d’: 20, u’t’: 30, u’tkhm’: 45, u’tpkwth’: 60}

Parses:

(ab c)b=(|[aeiouy][-+]?::[a-f][\d._/]+)c=[aeiouy][-+]?d

=(‘abcdefghi’)[-+]?(|[aeiouy][-+]?::[a-f][\d._/]+)

Output:

(a b)c ([aeiou]?::[a-f][\d.]+)

A few words on variables:

[-+]? – wildcards allowed in the pattern

? – optional

Example: {0:2,4:152579,5:614}

{0:A2,4:A2} – same as {A2:A}

{0:([aeiouy][-+]?::[a-f][\d._/]+)c:[aeiouy][-+]?d} – same as {c:[aeiouy][-+]?d}

{~} – remove leading and trailing whitespaces
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The NLP software packages include tools to read text and make simple grammatical decisions. Grapheme-Interpreter-Yacc is the oldest known NLP package; it was written in 1965 by William Safire for the IBM-PC. Since its release, it has been significantly improved and now supports over 95 languages.

A major advance in the mid-1990s was the introduction of Neural Networks. These neural networks parse data rather than generating it – effectively teaching a computer to understand the language contained in documents in the same way that humans do.

Today, most modern NLP systems use Deep Learning algorithms, which allow them to identify and predict keyword phrases, noun phrases, and verb tenses from a variety of sources, such as news articles, order forms, and standard workbooks.

Natural Language Processing (NLP) refers to the study of information extraction and analysis using computers. The ability of a computer to understand human


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