Natural Language Processing: Word Formation - Deepstash

Natural Language Processing: Word Formation

Morphology is the study of word structure.

Words consist of one or more morphemes (cats= cat+ s). A morpheme is the smallest unit of language.

Morphemes can be of several types:

  • stem (can stand on its own)
  • affix (can't stand on its own)

Word formation can develop through:

  • inflection: forms of the same word. E.g. word, words; work, worked
  • derivation: not applicable to all words in a class; the meaning changes: e.g. act -> actor
  • compunds: stardust = star + dust
  • clitisication: word + clitic. E.g.: we're, you're

For counting words:

  • tokens: distinct occurences of word strings
  • types: distinct words

Lemmatization is a vocabulary reduction process of mapping words to their stem. E.g. sang, sung, sings to sing  

Stemming is the process of reducing words to stems. E.g. information to inform, retrieval to retriev

Types of errors:

  • ommision: related words are not reduced to the same stem. E.g. European and Europe
  • commision: unrelated words reduced to the same stems. E.g. policy and police are reduced to polic 

Note: look at Minimum Distance Algorithm to determine the distance between words



Speech and Language Processing

by Daniel Jurafsky, James H. Martin


  • /word/ - matches any string containing the substring "word". Note: it's case sensitive.
  • /[ab]/ - disjunction of characters: "a" or" b"
  • /cat|dog/ - disjunction of string cat and dog. To be read as: String containing "cat" or "dog"
  • /pupp(y|ies)/ - To be read as: string containing "puppy" or "puppies"
  • /[a-z]/ - shows an interval: any letter between "a" and "z"
  • /[^a] / - "^" shows negation when it's the first character inside brackets. To be read as: not containing "a"
  • /^The/ - Here "^" matches only the string which starts with the expression that follows it. E.g. The dog barks.
  • /s.$/ - "$" matches the end of the line. E.g. expression that matches any string ending with .s
  • /words?/ - "?" implies zero or one instances of the previous character. E.g. word or words
  • /b.y/ - "." shows any character between b and y. E.g. for the expression "busy life", the answer would be "us"
  • /a*/ - "*" shows any string of 0 or more "a"'s. To be read as: "String containing no a's or 1 ore more a's
  • /[ab+] / - "+" shows 1 ore more occurences of the previous character. To be read as:Any character containing 1 ore more ab's.
  • /\bthe \b/ / "\b" matches a word boundary. To be read as "matches a string which contains "the" isolated by other words. E.g. "in the club" but not "the other", 
  •  \d - contains any digit
  • \D - contains any non-digit
  • {n} - n occurences of the previous char or expression
  • {,n} - up to n occurences of the previous char or expresssion
  • {n, m) - from n to m occurences of the previous char or expression



Deepstash helps you become inspired, wiser and productive, through bite-sized ideas from the best articles, books and videos out there.



Translation And Interpretation

They require an ability to be able to understand two or more languages and accurately express the content and information in the other language.

Translations need not be binary, but should sound natural without being too literal and wordy. The translator should be able to express the content in such a way that one cannot guess that it is a translation.


Our Image In A Professional Setting

In a professional setting, our identity is largely governed by the perception of our peers, colleagues and bosses,

Our ‘image’ depends on how they measure the impact of our behaviour and actions, how our character is perceived and how we are compared with others. We cultivate our image as what others think about us matters more than we would like to think.


  • Today, AI(Artificial Intelligence) can diagnose disease, translate languages, provide useful customer services, and drive a car for us.
  • Many companies use AI for automation, but to do that to displace human employees can backfire in the long run.

In extensive research involving fifteen hundred organizations, it is found that companies with the most significant improvements are those which join humans and machines to work in sync.