Skip to main content
SpringerLink
Log in

Natural Language Processing

  • Chapter
  • First Online:
Fundamentals of Artificial Intelligence

Abstract

The abundant volume of natural language text in the connected world, though having a large content of knowledge, but it is becoming increasingly difficult to disseminate it by a human to discover the knowledge/wisdom in it, specifically within any given time limits. The automated NLP is aimed to do this job effectively and with accuracy, like a human does it (for a limited of amount text). This chapter presents the challenges of NLP, progress so far made in this field, NLP applications, components of NLP, and grammar of English language—the way machine requires it. In addition, covers the specific areas like probabilistic parsing, ambiguities and their resolution, information extraction, discourse analysis, NL question-answering, commonsense interfaces, commonsense thinking and reasoning, causal-diversity, and various tools for NLP. Finally, the chapter summary, and a set of relevant exercises are presented.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 84.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Kheer: A sweet dish, like porridge, popular among the Indians.

  2. 2.

    Anaphora: making use of a pronoun or similar word instead of repeating a word used earlier.

  3. 3.

    Context: Relationship with adjacent and related words in a sentence, or phrase, or a paragraph.

References

  1. Cambria E, White B (2014) Jumping NLP curves: a review of natural language processing research. IEEE Computat Intell Mag 5:48–57

    Article  Google Scholar 

  2. Chomsky N (1956) Three models for the description of language. IRE Trans Inform Theory 2(3):113–124

    Article  Google Scholar 

  3. Chowdhary KR, Bansal VS (2006) Information Extraction from Natural Language Texts. J of the Institution of Engineers(India), 87:14–19

    Google Scholar 

  4. Chowdhary KR (2004) Natural Language Processing for Word Sense Disambiguation and Information Extraction. PhD Thesis, JNV University, Jodhpur (India)

    Google Scholar 

  5. Jurafsky D, Martin J H (2002) Speech and Language Processing - An Intro to Natural Language Processing, Computational Linguistics, and Speech Recognition. Pearson Education Asia, ISBN 81-7808-594-1

    Google Scholar 

  6. Lenat D, Guha R (1989) Building Large Knowledge-Based Systems: Representation and Inference. The Cyc Project. Addison-Wesley, Boston, MA

    Google Scholar 

  7. Lin J (2007) An Exploration of the Principles Underlying Redundancy-Based Factoid Question Answering. ACM Trans on Inf Sys 25(2):1–55

    Article  Google Scholar 

  8. Miller AM (1995) Wordnet: A Lexical Database for English. Communications of the ACM 38(11):39–41

    Article  Google Scholar 

  9. Minsky M (1968) Semantic Information Processing. MA, MIT Press, Cambridge

    MATH  Google Scholar 

  10. Minsky M (2000) Commonsense-based Interfaces. Communications of the ACM 43(8):67–73

    Article  Google Scholar 

  11. Mueller E (1998) Natural Language Processing with Thought-Treasure. Erik T. Mueller, New York

    Google Scholar 

  12. https://www.nltk.org/, Cited 19 Dec 2017

  13. Pearl J (1985) Bayesian networks: A model of self-activated memory for evidential reasoning. UCLA comput Sci, Irvine, CA: Tech Rep CSD-850017

    Google Scholar 

  14. Ralph G (1994) Computational Linguistics - An Introduction, Studies in Natural Language Processing, Cambridge Univ Press

    Google Scholar 

  15. Reiter R (1980) A logic for default reasoning. Artificial Intelligence 13:81–132

    Article  MathSciNet  Google Scholar 

  16. Ronald C et al (1997) Survey of the state of art in human language technology – Studies in Natural Language Processing, Cambridge Univ Press

    Google Scholar 

  17. Schank R (1975) Conceptual Information Processing. Elsevier Sc Inc, Amsterdam, The Netherlands

    MATH  Google Scholar 

  18. Singh P (2002) The open mind common sense project. http://www.kurzweilai.net/ Cited 19 Dec 2017

  19. Sowa J (1987) Semantic networks. Encyclopedia of Artificial Intelligence, S. Shapiro edn, Wiley, New York

    Google Scholar 

  20. Baldwin T (2009) et al (2009) Prepositions in Applications: A Survey and Introduction to the Special Issue. Computational Linguistics, Vol 35, 2:119–150

    Google Scholar 

  21. Turmo J et al (2006) Adaptive Information Extraction. ACM Computing Surveys 38(2):1–47

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Jodhpur Institute of Engineering and Technology, Jodhpur, Rajasthan, India

    K. R. Chowdhary

Authors
  1. K. R. Chowdhary
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. R. Chowdhary .

Exercises

Exercises

  1. 1.

    What are the challenges of NLP?

  2. 2.

    Give one example of the following ambiguities:

    1. a.

      Phonetic

    2. b.

      Syntactic

    3. c.

      Pragmatic

  3. 3.

    What are the applications of NLP?

  4. 4.

    Develop the parse-tree to generate the sentence “Rajan slept on the bench” using following rewrite rules:

  5. 5.

    Draw the tree for the following phrases:

    1. a.

      after 5 pm.

    2. b.

      on Tuesday.

    3. c.

      From Delhi.

    4. d.

      Any delay at Mumbai.

  6. 6.

    Draw the tree structures for the following sentences:

    1. a.

      I would like to fly on Air India.

    2. b.

      I need to fly between Delhi and Mumbai.

    3. c.

      Please repeat again.

  7. 7.

    Convert the following passive voice to active voice. Construct the necessary trees. Also write the steps. The village was looted by dacoits.

    $$\begin{aligned} S&\rightarrow NP~~VP \\ NP&\rightarrow N\\ NP&\rightarrow Det~~N\\ VP&\rightarrow V~~PP \\ PP&\rightarrow Prep~~NP\\ N&\rightarrow Rajan \mid bench \\ Det&\rightarrow the\\ prep&\rightarrow on \end{aligned}$$
  8. 8.

    Given the parse-tree in Fig. 19.19, construct the grammar for this.

  9. 9.

    Construct the grammars and parse-tree for the following sentences.

    1. a.

      The boy who was sleeping was awakened.

    2. b.

      The boy who was sleeping on the table was awakened.

    3. c.

      Jack slept on the table.

  10. 10.

    Construct the parse-trees and resolve the ambiguities in the following sentences using “selectional constraint”. Also, specify whether the ambiguities are syntactic, semantic, or some other?

    1. a.

      “He saw the man with the horse.”

    2. b.

      “he saw the man with gun.”

    3. c.

      “He saw the man with binocular.”

  11. 11.

    What are the different types of ambiguities in natural language, like English?

Fig. 19.19
figure 19

Parse-tree

Full size image

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature India Private Limited

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Chowdhary, K.R. (2020). Natural Language Processing. In: Fundamentals of Artificial Intelligence. Springer, New Delhi. https://doi.org/10.1007/978-81-322-3972-7_19

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-3972-7_19

  • Published:

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-3970-3

  • Online ISBN: 978-81-322-3972-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation