1. Natural Language Processing
Doha Abd EL-Fattah Anwer Rizk
Mennat allah Abd EL-Rahman Massoud
Department of Mathematics and Computer Science,
Faculty of Science, Alexandria University

2. Natural Language:
Human Language.

3. Language:

4. What is NLP?
Natural Language Processing is a field of Artificial intelligence and Linguistics.
Its aim is to make computers understand the statements or words written in human languages.
NLP takes string of words as an input and outputs structured representations capturing the meaning of those strings.

5. Brief History:
NLP began in the 1950s, just after World War II, when Alan Turing published an article titled "Computing Machinery and Intelligence" which proposed what is now called the Turing test (fig 1).
Fig 1: Turing test

6. Cont. Brief History:
Chomsky (1956) first considered finite-state machines as a way to characterize a grammar, and defined a finite-state language as a language generated by a finite-state grammar. These early models led to the field of formal language theory, which used algebra and set theory to define formal languages as sequences of symbols.

7. Cont. Brief History:
Early 1960s, speech and language processing had split into two paradigms: symbolic and stochastic.
The first was the work of Chomsky and others on formal language theory.
The second line of research was the work on reasoning and logical typified by Newell and Simon’s work on Logic Theorist and General Problem Solver. At this point early natural language understanding systems were built.

8. Cont. Brief History
From 1970 until 1994 saw an explosion in research in speech and language processing and the development of a number of research paradigms.
The Rise of Machine Learning: 2000–2007.

9. Some of NLP Applications:
Spell and Grammar Checking

10. Cont. Some of NLP Applications:
Predicting the next word that is highly probable to be typed by the user.

11. Cont. Some of NLP Applications:
Finding relevant information to the user’s query

12. Cont. Some of NLP Applications:
Translating a text from one language to another.

13. Cont. Some of NLP Applications:
Optical Character Recognition

14. Cont. Some of NLP Applications:
Speech recognition

15. Cont. Some of NLP Applications:
Information extraction

16. Required kinds of knowledge
To engaging in complex language behaviour requires:
Phonetics and Phonology— knowledge about linguistic sounds
Morphology— knowledge of the meaningful components of words
Syntax— knowledge of the structural relationships between words
Semantics—knowledge of meaning
Pragmatics— knowledge of the relationship of meaning to the goals and intentions of the speaker.
Discourse— knowledge about linguistic units larger than a single utterance

17. Why is NLP Hard?
One of the problems with teaching computers to understand natural language, is that much of the meaning in what people say is hidden in what they don't say. As humans, we trivially interpret the meaning of ambiguous words, written or spoken, according to their context.
For example: Humans will trivially refer NLP directly to natural language processing, rather than neuro-linguistic programming.

18. Ambiguity Lexical Ambiguity
Lexical ambiguity can occur when a word has more than one meaning.
Ex: She is looking for a match.
Match has more than one meaning, it could be:
A contest in which people or teams compete against each other in a sport.
A short, thin piece of wood or cardboard used to light a fire.

19. Ambiguity Syntactic Ambiguity Ex: Fed raises interest rates.
VP: verb phrase, NP: Noun phrase

20. Other Things Making NLP Difficult:
Non-Standard English
Like in twitter; unusual spelling of words, hashtags,…etc.
Segmentation problems