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fix typo and expand nlp goal like represent meaning and context
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edit approved Mar 10, 2020 at 10:11
Savrige
edit approved Mar 10, 2020 at 10:11
Savrige
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I agree with Sean's answer. NLP and text mining are usually used for different goals. Also, there is indeed an overlap and both definitions are vogue.

Other than the difference in goal, there is a difference in methods. Text mining techniques are usually shallow and do not consider the text structure.Usually Usually, text mining will use bag of words-of-words, n-grams and possibly stemming over that.

In NLP methods usually involve the testtext structure. You can find there sentence splitting, part of speech-of-speech tagging and parse tree construction. Also, NLP methods provide several techniques to capture context and meaning from text.

Typical text mining method will consider the following sentences to indicate happiness while typical NLP methods detectsdetect that they are not

  1. I am not happy
  2. I will be happy when it will rain
  3. If it will rain, I'll be happy.
  4. She asked whether I am happy
  5. Are you happy?

I agree with Sean's answer. NLP and text mining are usually used for different goals. Also, there is indeed an overlap and both definitions are vogue.

Other than the difference in goal, there is a difference in methods. Text mining techniques are usually shallow and do not consider the text structure.Usually, text mining will use bag of words, n-grams and possibly stemming over that.

In NLP methods usually involve the test structure. You can find there sentence splitting, part of speech tagging and parse tree construction.

Typical text mining method will consider the following sentences to indicate happiness while typical NLP methods detects that they are not

  1. I am not happy
  2. I will be happy when it will rain
  3. If it will rain, I'll be happy.
  4. She asked whether I am happy
  5. Are you happy?

I agree with Sean's answer. NLP and text mining are usually used for different goals. Also, there is indeed an overlap and both definitions are vogue.

Other than the difference in goal, there is a difference in methods. Text mining techniques are usually shallow and do not consider the text structure. Usually, text mining will use bag-of-words, n-grams and possibly stemming over that.

In NLP methods usually involve the text structure. You can find there sentence splitting, part-of-speech tagging and parse tree construction. Also, NLP methods provide several techniques to capture context and meaning from text.

Typical text mining method will consider the following sentences to indicate happiness while typical NLP methods detect that they are not

  1. I am not happy
  2. I will be happy when it will rain
  3. If it will rain, I'll be happy.
  4. She asked whether I am happy
  5. Are you happy?
deleted 4 characters in body
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Dawny33
Dawny33
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I agree with Sean's answer. NLP and and text mining are usually used for different goals. Also, there is indeed an overlap and both definitions are vogue.

Other than the difference in goal, there is a difference in methods. Text mining techniques are usually shallow and do not consider the text structure. UsuallyUsually, text mining will use bag of words, n-grams and possibly stemming over that.

In NLP methods usually involve the test structure. You can find there sentence splitting, part of speech tagging and parse tree construction.

Typical text mining method will consider the following sentences to indicate happiness while typical NLP methods detects that they are not

  1. I am not happy
  2. I will be happy when it will rain
  3. If it will rain, I'll be happy.
  4. She asked whether I am happy
  5. Are you happy?

I agree with Sean's answer. NLP and and text mining are usually used for different goals. Also, there is indeed an overlap and both definitions are vogue.

Other than the difference in goal, there is a difference in methods. Text mining techniques are usually shallow and do not consider the text structure. Usually text mining will use bag of words, n-grams and possibly stemming over that.

In NLP methods usually involve the test structure. You can find there sentence splitting, part of speech tagging and parse tree construction.

Typical text mining method will consider the following sentences to indicate happiness while typical NLP methods detects that they are not

  1. I am not happy
  2. I will be happy when it will rain
  3. If it will rain, I'll be happy.
  4. She asked whether I am happy
  5. Are you happy?

I agree with Sean's answer. NLP and text mining are usually used for different goals. Also, there is indeed an overlap and both definitions are vogue.

Other than the difference in goal, there is a difference in methods. Text mining techniques are usually shallow and do not consider the text structure.Usually, text mining will use bag of words, n-grams and possibly stemming over that.

In NLP methods usually involve the test structure. You can find there sentence splitting, part of speech tagging and parse tree construction.

Typical text mining method will consider the following sentences to indicate happiness while typical NLP methods detects that they are not

  1. I am not happy
  2. I will be happy when it will rain
  3. If it will rain, I'll be happy.
  4. She asked whether I am happy
  5. Are you happy?
Some examples to sentences that common NLP and text mining methods classify differently
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DaL
DaL
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  • 14
  • 13

I agree with Sean's answer. NLP and and text mining are usually used for different goals. Also, there is indeed an overlap and both definitions are vogue.

Other than the difference in goal, there is a difference in methods. Text mining techniques are usually shallow and do not consider the text structure. Usually text mining will use bag of words, n-grams and possibly stemming over that.

In NLP methods usually involve the test structure. You can find there sentence splitting, part of speech tagging and parse tree construction.

Typical text mining method will consider the following sentences to indicate happiness while typical NLP methods detects that they are not

  1. I am not happy
  2. I will be happy when it will rain
  3. If it will rain, I'll be happy.
  4. She asked whether I am happy
  5. Are you happy?

I agree with Sean's answer. NLP and and text mining are usually used for different goals. Also, there is indeed an overlap and both definitions are vogue.

Other than the difference in goal, there is a difference in methods. Text mining techniques are usually shallow and do not consider the text structure. Usually text mining will use bag of words, n-grams and possibly stemming over that.

In NLP methods usually involve the test structure. You can find there sentence splitting, part of speech tagging and parse tree construction.

I agree with Sean's answer. NLP and and text mining are usually used for different goals. Also, there is indeed an overlap and both definitions are vogue.

Other than the difference in goal, there is a difference in methods. Text mining techniques are usually shallow and do not consider the text structure. Usually text mining will use bag of words, n-grams and possibly stemming over that.

In NLP methods usually involve the test structure. You can find there sentence splitting, part of speech tagging and parse tree construction.

Typical text mining method will consider the following sentences to indicate happiness while typical NLP methods detects that they are not

  1. I am not happy
  2. I will be happy when it will rain
  3. If it will rain, I'll be happy.
  4. She asked whether I am happy
  5. Are you happy?
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DaL
DaL
  • 2.7k
  • 14
  • 13