Home > Articles > Web Services > Cloud Computing

  • Print
  • + Share This
This chapter is from the book



M. Liberman, “Morphology.” Linguistics 001, Lecture 7, University of Pennsylvania, 2009. http://www.ling.upenn.edu/courses/Fall_2009/ling001/morphology.html.


M. Haspelmath, “The indeterminacy of word segmentation and the nature of morphology and syntax,” Folia Linguistica, vol. 45, 2011.


H. Kučera and W. N. Francis, Computational Analysis of Present-Day American English. Providence, RI: Brown University Press, 1967.


S. B. Cohen and N. A. Smith, “Joint morphological and syntactic disambiguation,” in Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp. 208–217, 2007.


T. Nakagawa, “Chinese and Japanese word segmentation using word-level and character-level information,” in Proceedings of 20th International Conference on Computational Linguistics, pp. 466–472, 2004.


H. Shin and H. You, “Hybrid n-gram probability estimation in morphologically rich languages,” in Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, 2009.


D. Z. Hakkani-Tür, K. Oflazer, and G. Tür, “Statistical morphological disambiguation for agglutinative languages,” in Proceedings of the 18th Conference on Computational Linguistics, pp. 285–291, 2000.


G. T. Stump, Inflectional Morphology: A Theory of Paradigm Structure. Cambridge Studies in Linguistics, New York: Cambridge University Press, 2001.


K. R. Beesley and L. Karttunen, Finite State Morphology. CSLI Studies in Computational Linguistics, Stanford, CA: CSLI Publications, 2003.


M. Baerman, D. Brown, and G. G. Corbett, The Syntax-Morphology Interface. A Study of Syncretism. Cambridge Studies in Linguistics, New York: Cambridge University Press, 2006.


B. Roark and R. Sproat, Computational Approaches to Morphology and Syntax. Oxford Surveys in Syntax and Morphology, New York: Oxford University Press, 2007.


O. Smrž, “Functional Arabic morphology. Formal system and implementation,” PhD thesis, Charles University in Prague, 2007.


H. Eifring and R. Theil, Linguistics for Students of Asian and African Languages. Universitetet i Oslo, 2005.


B. Bickel and J. Nichols, “Fusion of selected inflectional formatives & exponence of selected inflectional formatives,” in The World Atlas of Language Structures Online (M. Haspelmath, M. S. Dryer, D. Gil, and B. Comrie, eds.), ch. 20 & 21, Munich: Max Planck Digital Library, 2008.


W. Fischer, A Grammar of Classical Arabic. Trans. Jonathan Rodgers. Yale Language Series, New Haven, CT: Yale University Press, 2002.


K. C. Ryding, A Reference Grammar of Modern Standard Arabic. New York: Cambridge University Press, 2005.


O. Smrž and V. Bielický, “ElixirFM.” Functional Arabic Morphology, SourceForge.net, 2010. http://sourceforge.net/projects/elixer-fm/.


T. Kamei, R. KMno, and E. Chino, eds., The Sanseido Encyclopedia of Linguistics, Volume 6 Terms (in Japanese). Sanseido, 1996.


F. Karlsson, Finnish Grammar. Helsinki: Werner Söderström Osakenyhtiö, 1987.


J. Hajič and B. Hladká, “Tagging inflective languages: Prediction of morphological categories for a rich, structured tagset,” in Proceedings of COLING-ACL 1998, pp. 483– 490, 1998.


J. Hajič, “Morphological tagging: Data vs. dictionaries,” in Proceedings of NAACLANLP 2000, pp. 94–101, 2000.


N. Habash and O. Rambow, “Arabic tokenization, part-of-speech tagging and morphological disambiguation in one fell swoop,” in Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05), pp. 573–580, 2005.


N. A. Smith, D. A. Smith, and R. W. Tromble, “Context-based morphological disambiguation with random fields,” in Proceedings of HLT/EMNLP 2005, pp. 475–482, 2005.


J. Hajič, O. Smrž, T. Buckwalter, and H. Jin, “Feature-based tagger of approximations of functional Arabic morphology,” in Proceedings of the 4th Workshop on Treebanks and Linguistic Theories (TLT 2005), pp. 53–64, 2005.


T. Buckwalter, “Issues in Arabic orthography and morphology analysis,” in COLING 2004 Computational Approaches to Arabic Script-based Languages, pp. 31–34, 2004.


R. Nelken and S. M. Shieber, “Arabic diacritization using finite-state transducers,” in Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages, pp. 79–86, 2005.


I. Zitouni, J. S. Sorensen, and R. Sarikaya, “Maximum entropy based restoration of Arabic diacritics,” in Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, pp. 577–584, 2006.


N. Habash and O. Rambow, “Arabic diacritization through full morphological tagging,” in Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers, pp. 53–56, 2007.


G. Huet, “Lexicon-directed segmentation and tagging of Sanskrit,” in Proceedings of the XIIth World Sanskrit Conference, pp. 307–325, 2003.


G. Huet, “Formal structure of Sanskrit text: Requirements analysis for a mechanical Sanskrit processor,” in Sanskrit Computational Linguistics: First and Second International Symposia (G. Huet, A. Kulkarni, and P. Scharf, eds.), vol. 5402 of LNAI, pp. 162–199, Berlin: Springer, 2009.


F. Katamba and J. Stonham, Morphology. Basingstoke: Palgrave Macmillan, 2006.


L. Bauer, Morphological Productivity, Cambridge Studies in Linguistics. New York: Cambridge University Press, 2001.


R. H. Baayen, Word Frequency Distributions, Text, Speech and Language Technology. Boston: Kluwer Academic Publishers, 2001.


A. Kilgarriff, “Googleology is bad science,” Computational Linguistics, vol. 33, no. 1, pp. 147–151, 2007.


H.-C. Kwon and Y.-S. Chae, “A dictionary-based morphological analysis,” in Proceedings of Natural Language Processing Pacific Rim Symposium, pp. 178–185, 1991.


D.-B. Kim, K.-S. Choi, and K.-H. Lee, “A computational model of Korean morphological analysis: A prediction-based approach,” Journal of East Asian Linguistics, vol. 5, no. 2, pp. 183–215, 1996.


A. Halevy, P. Norvig, and F. Pereira, “The unreasonable effectiveness of data,” IEEE Intelligent Systems, vol. 24, no. 2, pp. 8–12, 2009.


R. M. Kaplan and M. Kay, “Regular models of phonological rule systems,” Computational Linguistics, vol. 20, no. 3, pp. 331–378, 1994.


K. Koskenniemi, “Two-level morphology: A general computational model for word form recognition and production,” PhD thesis, University of Helsinki, 1983.


R. Sproat, Morphology and Computation. ACL–MIT Press Series in Natural Language Processing. Cambridge, MA: MIT Press, 1992.


D.-B. Kim, S.-J. Lee, K.-S. Choi, and G.-C. Kim, “A two-level morphological analysis of Korean,” in Proceedings of the 15th International Conference on Computational Linguistics, pp. 535–539, 1994.


S.-Z. Lee and H.-C. Rim, “Korean morphology with elementary two-level rules and rule features,” in Proceedings of the Pacific Association for Computational Linguistics, pp. 182–187, 1997.


N.-R. Han, “Klex: A finite-state trancducer lexicon of Korean,” in Finite-state Methods and Natural Language Processing: 5th International Workshop, FSMNLP 2005, pp. 67–77, Springer, 2006.


M. Kay, “Nonconcatenative finite-state morphology,” in Proceedings of the Third Conference of the European Chapter of the ACL (EACL-87), pp. 2–10, ACL, 1987.


K. R. Beesley, “Arabic morphology using only finite-state operations,” in COLINGACL’98 Proceedings of the Workshop on Computational Approaches to Semitic languages, pp. 50–57, 1998.


G. A. Kiraz, Computational Nonlinear Morphology with Emphasis on Semitic Languages. Studies in Natural Language Processing, Cambridge: Cambridge University Press, 2001.


N. Habash, O. Rambow, and G. Kiraz, “Morphological analysis and generation for Arabic dialects,” in Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages, pp. 17–24, 2005.


H. Skoumalová, “A Czech morphological lexicon,” in Proceedings of the Third Meeting of the ACL Special Interest Group in Computational Phonology, pp. 41–47, 1997.


R. Sedláček and P. Smrž, “A new Czech morphological analyser ajka,” in Text, Speech and Dialogue, vol. 2166, pp. 100–107, Berlin: Springer, 2001.


K. Oflazer, “Computational morphology.” ESSLLI 2006 European Summer School in Logic, Language, and Information, 2006.


C. Pollard and I. A. Sag, Head-Driven Phrase Structure Grammar. Chicago: University of Chicago Press, 1994.


R. Evans and G. Gazdar, “DATR: A language for lexical knowledge representation,” Computational Linguistics, vol. 22, no. 2, pp. 167–216, 1996.


T. Erjavec, “Unification, inheritance, and paradigms in the morphology of natural languages,” PhD thesis, University of Ljubljana, 1996.


B. Carpenter, The Logic of Typed Feature Structures. Cambridge Tracts in Theoretical Computer Science 32, New York: Cambridge University Press, 1992.


S. M. Shieber, Constraint-Based Grammar Formalisms: Parsing and Type Inference for Natural and Computer Languages. Cambridge, MA: MIT Press, 1992.


S. Bird and T. M. Ellison, “One-level phonology: Autosegmental representations and rules as finite automata,” Computational Linguistics, vol. 20, no. 1, pp. 55–90, 1994.


S. Bird and P. Blackburn, “A logical approach to Arabic phonology,” in Proceedings of the 5th Conference of the European Chapter of the Association for Computational Linguistics, pp. 89–94, 1991.


G. G. Corbett and N. M. Fraser, “Network morphology: A DATR account of Russian nominal inflection,” Journal of Linguistics, vol. 29, pp. 113–142, 1993.


J. Hajič, “Unification morphology grammar. Software system for multilanguage morphological analysis,” PhD thesis, Charles University in Prague, 1994.


K. Megerdoomian, “Unification-based Persian morphology,” in Proceedings of CICLing 2000, 2000.


R. Finkel and G. Stump, “Generating Hebrew verb morphology by default inheritance hierarchies,” in Proceedings of the Workshop on Computational Approaches to Semitic Languages, pp. 9–18, 2002.


S. R. Al-Najem, “Inheritance-based approach to Arabic verbal root-and-pattern morphology,” in Arabic Computational Morphology. Knowledge-based and Empirical Methods (A. Soudi, A. van den Bosch, and G. Neumann, eds.), vol. 38, pp. 67–88, Berlin: Springer, 2007.


S. Köprü and J. Miller, “A unification based approach to the morphological analysis and generation of Arabic,” in CAASL-3: Third Workshop on Computational Approaches to Arabic Script-based Languages, 2009.


M. Forsberg and A. Ranta, “Functional morphology,” in Proceedings of the 9th ACM SIGPLAN International Conference on Functional Programming, ICFP 2004, pp. 213–223, 2004.


A. Ranta, “Grammatical Framework: A type-theoretical grammar formalism,” Journal of Functional Programming, vol. 14, no. 2, pp. 145–189, 2004.


P. Ljunglöf, “Pure functional parsing. An advanced tutorial,” Licenciate thesis, Göteborg University & Chalmers University of Technology, 2002.


G. Huet, “The Zen computational linguistics toolkit,” ESSLLI 2002 European Summer School in Logic, Language, and Information, 2002.


G. Huet, “A functional toolkit for morphological and phonological processing, application to a Sanskrit tagger,” Journal of Functional Programming, vol. 15, no. 4, pp. 573–614, 2005.


M. Humayoun, H. Hammarström, and A. Ranta, “Urdu morphology, orthography and lexicon extraction,” in CAASL-2: Second Workshop on Computational Approaches to Arabic Script-based Languages, pp. 59–66, 2007.


A. Dada and A. Ranta, “Implementing an open source Arabic resource grammar in GF,” in Perspectives on Arabic Linguistics (M. A. Mughazy, ed.), vol. XX, pp. 209– 231, John Benjamins, 2007.


A. Ranta, “Grammatical Framework.” Programming Language for Multilingual Grammar Applications, http://www.grammaticalframework.org/, 2010.


J. Baldridge, S. Chatterjee, A. Palmer, and B. Wing, “DotCCG and VisCCG: Wiki and programming paradigms for improved grammar engineering with OpenCCG,” in Proceedings of the Workshop on Grammar Engineering Across Frameworks, 2007.


H. Hammarström, “Unsupervised learning of morphology and the languages of the world,” PhD thesis, Chalmers University of Technology and University of Gothenburg, 2009.


J. A. Goldsmith, “Segmentation and morphology,” in Computational Linguistics and Natural Language Processing Handbook (A. Clark, C. Fox, and S. Lappin, eds.), pp. 364–393, Chichester: Wiley-Blackwell, 2010.


D. Yarowsky and R. Wicentowski, “Minimally supervised morphological analysis by multimodal alignment,” in Proceedings of the 38th Meeting of the Association for Computational Linguistics, pp. 207–216, 2000.


P. Schone and D. Jurafsky, “Knowledge-free induction of inflectional morphologies,” in Proceedings of the North American Chapter of the Association for Computational Linguistics, pp. 183–191, 2001.


S. Neuvel and S. A. Fulop, “Unsupervised learning of morphology without morphemes,” in Proceedings of the ACL-02 Workshop on Morphological and Phonological Learning, pp. 31–40, 2002.


N. Hathout, “Acquistion of the morphological structure of the lexicon based on lexical similarity and formal analogy,” in Coling 2008: Proceedings of the 3rd Textgraphs Workshop on Graph-based Algorithms for Natural Language Processing, pp. 1–8, 2008.


J. Goldsmith, “Unsupervised learning of the morphology of a natural language,” Computational Linguistics, vol. 27, no. 2, pp. 153–198, 2001.


H. Johnson and J. Martin, “Unsupervised learning of morphology for English and Inuktikut,” in Companion Volume of the Proceedings of the Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics 2003: Short Papers, pp. 43–45, 2003.


M. Creutz and K. Lagus, “Induction of a simple morphology for highly-inflecting languages,” in Proceedings of the 7th Meeting of the ACL Special Interest Group in Computational Phonology, pp. 43–51, 2004.


M. Creutz and K. Lagus, “Unsupervised models for morpheme segmentation and morphology learning,” ACM Transactions on Speech and Language Processing, vol. 4, no. 1, pp. 1–34, 2007.


C. Monson, J. Carbonell, A. Lavie, and L. Levin, “ParaMor: Minimally supervised induction of paradigm structure and morphological analysis,” in Proceedings of Ninth Meeting of the ACL Special Interest Group in Computational Morphology and Phonology, pp. 117–125, 2007.


F. M. Liang, “Word Hy-phen-a-tion by Com-put-er,” PhD thesis, Stanford University, 1983.


V. Demberg, “A language-independent unsupervised model for morphological segmentation,” in Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pp. 920–927, 2007.


A. Clark, “Supervised and unsupervised learning of Arabic morphology,” in Arabic Computational Morphology. Knowledge-based and Empirical Methods (A. Soudi, A. van den Bosch, and G. Neumann, eds.), vol. 38, pp. 181–200, Berlin: Springer, 2007.


A. Xanthos, Apprentissage automatique de la morphologie: le cas des structures racineschème. Sciences pour la communication, Bern: Peter Lang, 2008.


B. Snyder and R. Barzilay, “Unsupervised multilingual learning for morphological segmentation,” in Proceedings of ACL-08: HLT, pp. 737–745, 2008.


H. Poon, C. Cherry, and K. Toutanova, “Unsupervised morphological segmentation with log-linear models,” in Proceedings of Human Language Technologies: Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 209–217, 2009.


S. Della Pietra, V. Della Pietra, and J. Lafferty, “Inducing features of random fields,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 4, pp. 380–393, 1997.

  • + Share This
  • 🔖 Save To Your Account

InformIT Promotional Mailings & Special Offers

I would like to receive exclusive offers and hear about products from InformIT and its family of brands. I can unsubscribe at any time.


Pearson Education, Inc., 221 River Street, Hoboken, New Jersey 07030, (Pearson) presents this site to provide information about products and services that can be purchased through this site.

This privacy notice provides an overview of our commitment to privacy and describes how we collect, protect, use and share personal information collected through this site. Please note that other Pearson websites and online products and services have their own separate privacy policies.

Collection and Use of Information

To conduct business and deliver products and services, Pearson collects and uses personal information in several ways in connection with this site, including:

Questions and Inquiries

For inquiries and questions, we collect the inquiry or question, together with name, contact details (email address, phone number and mailing address) and any other additional information voluntarily submitted to us through a Contact Us form or an email. We use this information to address the inquiry and respond to the question.

Online Store

For orders and purchases placed through our online store on this site, we collect order details, name, institution name and address (if applicable), email address, phone number, shipping and billing addresses, credit/debit card information, shipping options and any instructions. We use this information to complete transactions, fulfill orders, communicate with individuals placing orders or visiting the online store, and for related purposes.


Pearson may offer opportunities to provide feedback or participate in surveys, including surveys evaluating Pearson products, services or sites. Participation is voluntary. Pearson collects information requested in the survey questions and uses the information to evaluate, support, maintain and improve products, services or sites, develop new products and services, conduct educational research and for other purposes specified in the survey.

Contests and Drawings

Occasionally, we may sponsor a contest or drawing. Participation is optional. Pearson collects name, contact information and other information specified on the entry form for the contest or drawing to conduct the contest or drawing. Pearson may collect additional personal information from the winners of a contest or drawing in order to award the prize and for tax reporting purposes, as required by law.


If you have elected to receive email newsletters or promotional mailings and special offers but want to unsubscribe, simply email information@informit.com.

Service Announcements

On rare occasions it is necessary to send out a strictly service related announcement. For instance, if our service is temporarily suspended for maintenance we might send users an email. Generally, users may not opt-out of these communications, though they can deactivate their account information. However, these communications are not promotional in nature.

Customer Service

We communicate with users on a regular basis to provide requested services and in regard to issues relating to their account we reply via email or phone in accordance with the users' wishes when a user submits their information through our Contact Us form.

Other Collection and Use of Information

Application and System Logs

Pearson automatically collects log data to help ensure the delivery, availability and security of this site. Log data may include technical information about how a user or visitor connected to this site, such as browser type, type of computer/device, operating system, internet service provider and IP address. We use this information for support purposes and to monitor the health of the site, identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents and appropriately scale computing resources.

Web Analytics

Pearson may use third party web trend analytical services, including Google Analytics, to collect visitor information, such as IP addresses, browser types, referring pages, pages visited and time spent on a particular site. While these analytical services collect and report information on an anonymous basis, they may use cookies to gather web trend information. The information gathered may enable Pearson (but not the third party web trend services) to link information with application and system log data. Pearson uses this information for system administration and to identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents, appropriately scale computing resources and otherwise support and deliver this site and its services.

Cookies and Related Technologies

This site uses cookies and similar technologies to personalize content, measure traffic patterns, control security, track use and access of information on this site, and provide interest-based messages and advertising. Users can manage and block the use of cookies through their browser. Disabling or blocking certain cookies may limit the functionality of this site.

Do Not Track

This site currently does not respond to Do Not Track signals.


Pearson uses appropriate physical, administrative and technical security measures to protect personal information from unauthorized access, use and disclosure.


This site is not directed to children under the age of 13.


Pearson may send or direct marketing communications to users, provided that

  • Pearson will not use personal information collected or processed as a K-12 school service provider for the purpose of directed or targeted advertising.
  • Such marketing is consistent with applicable law and Pearson's legal obligations.
  • Pearson will not knowingly direct or send marketing communications to an individual who has expressed a preference not to receive marketing.
  • Where required by applicable law, express or implied consent to marketing exists and has not been withdrawn.

Pearson may provide personal information to a third party service provider on a restricted basis to provide marketing solely on behalf of Pearson or an affiliate or customer for whom Pearson is a service provider. Marketing preferences may be changed at any time.

Correcting/Updating Personal Information

If a user's personally identifiable information changes (such as your postal address or email address), we provide a way to correct or update that user's personal data provided to us. This can be done on the Account page. If a user no longer desires our service and desires to delete his or her account, please contact us at customer-service@informit.com and we will process the deletion of a user's account.


Users can always make an informed choice as to whether they should proceed with certain services offered by InformIT. If you choose to remove yourself from our mailing list(s) simply visit the following page and uncheck any communication you no longer want to receive: www.informit.com/u.aspx.

Sale of Personal Information

Pearson does not rent or sell personal information in exchange for any payment of money.

While Pearson does not sell personal information, as defined in Nevada law, Nevada residents may email a request for no sale of their personal information to NevadaDesignatedRequest@pearson.com.

Supplemental Privacy Statement for California Residents

California residents should read our Supplemental privacy statement for California residents in conjunction with this Privacy Notice. The Supplemental privacy statement for California residents explains Pearson's commitment to comply with California law and applies to personal information of California residents collected in connection with this site and the Services.

Sharing and Disclosure

Pearson may disclose personal information, as follows:

  • As required by law.
  • With the consent of the individual (or their parent, if the individual is a minor)
  • In response to a subpoena, court order or legal process, to the extent permitted or required by law
  • To protect the security and safety of individuals, data, assets and systems, consistent with applicable law
  • In connection the sale, joint venture or other transfer of some or all of its company or assets, subject to the provisions of this Privacy Notice
  • To investigate or address actual or suspected fraud or other illegal activities
  • To exercise its legal rights, including enforcement of the Terms of Use for this site or another contract
  • To affiliated Pearson companies and other companies and organizations who perform work for Pearson and are obligated to protect the privacy of personal information consistent with this Privacy Notice
  • To a school, organization, company or government agency, where Pearson collects or processes the personal information in a school setting or on behalf of such organization, company or government agency.


This web site contains links to other sites. Please be aware that we are not responsible for the privacy practices of such other sites. We encourage our users to be aware when they leave our site and to read the privacy statements of each and every web site that collects Personal Information. This privacy statement applies solely to information collected by this web site.

Requests and Contact

Please contact us about this Privacy Notice or if you have any requests or questions relating to the privacy of your personal information.

Changes to this Privacy Notice

We may revise this Privacy Notice through an updated posting. We will identify the effective date of the revision in the posting. Often, updates are made to provide greater clarity or to comply with changes in regulatory requirements. If the updates involve material changes to the collection, protection, use or disclosure of Personal Information, Pearson will provide notice of the change through a conspicuous notice on this site or other appropriate way. Continued use of the site after the effective date of a posted revision evidences acceptance. Please contact us if you have questions or concerns about the Privacy Notice or any objection to any revisions.

Last Update: November 17, 2020