Description of the course

Slides

• Basics of Phonetics, Phonology & Morphology: slides, exercise
• Tagset Design and Corpora Annotation: slides
• Classical approaches to morphological analysis: slides
• Classical approaches to tagging: slides
• An example: our approach: slides
• Unsupervised and Resource-light Approaches to Computational Morphology: slides

Old Czech MA

• Old Czech MA V 0.1 (Wed) - guesser needs implementing, and the results somehow combined with the map based xanalyzer (see readme.txt for basic instructions)
• Old Czech MA V 0.2 (Thu) - adds a cascade of analyzer and a guesser (the analysis part, the part reading the guesser specification is missing)
• Old Czech MA V 0.3 (Fri) - added a simple guesser and stem change rules

Recommended Reading

Overview papers written by us:

• J. Hana and Feldman A.(2012): Resource-Light Approaches to Computational Morphology Part 1: Monolingual Approaches In Language and Linguistics Compass Journal (Computational Linguistics Section). Vol.6, Issue 10, pp. 622-634, Blackwell.
• J. Hana and Feldman A.(2013): ESSLLI 2013 reader

• M.F.Porter (1980): An algorithm for suffix stripping (What is now called Porter stemmer)
• L. Karttunen & K. Wittenburg (1983): A two-level morphological analysis of English

• A. Feldman & J. Hana (2010): A resource-light approach to morpho-syntactic tagging (Chapter 6, 7)
• J. Goldsmith (2001): Unsupervised Learning of the Morphology of a Natural Language.
• D. Yarowsky & R. Wicentowski (2000): Minimally Supervised Morphological Analysis by Multimodal Alignment.
• * R. Wicentowski (2004): Multilingual noise-robust supervised morphological analysis using the WordFrame model.
• S.Cucerzan & D. Yarowsky (2002): Bootstrapping a Multilingual Part-of-speech Tagger in One Person-day
  *_ (2003): Minimally Supervised Induction of Grammatical Gender
• P. Schone & D. Jurafsky (2001): Knowledge-free induction of inflectional morphologies
• *P. J. Schone (2001): Toward knowledge-free induction of machine-readable dictionaries.
• *M. G. Snover & M. R. Brent (2001): A Bayesian model for morpheme and paradigm identification.
• C. Monson et al (2007): ParaMor: Minimally Supervised Induction of Paradigm Structure and Morphological Analysis.
• *C. Monson (2009): ParaMor: From Paradigm Structure to Natural Language Morphology Induction. (thesis)
  
• *C. Monson et al. (2009): ParaMor and Morpho Challenge 2008;
  
• *T. Tchoukalov, C. Monson & B. Roark (2010): Morphological Analysis by Multiple Sequence Alignment.
• Morfessor: M. Creutz and K. Lagus (2007): Unsupervised models for morpheme segmentation and morphology learning.
• Kohonen et al 2010,
• Tepper and Xia 2008 *Creutz, M (2006): Induction of the Morphology of Natural Language (thesis)

Links

• ACL Special Interest Group on Computational Morphology and Phonology.
• SFST -- Stuttgart Finite State Transducer Tools SFST is a toolbox for the implementation of morphological analysers and other tools which are based on finite state transducer technology.
• OpenFst Library A library for constructing, combining, optimizing, and searching weighted finite-state transducers (FSTs).
• Foma: a finite-state compiler and library
• FSA6.2xx: FSA Utilities toolbox: a collection of utilities to manipulate regular expressions, finite-state automata and finite-state transducers. (SICStus Prolog, SWI-Prolog or YAP)
• HFST : Helsinki Finite State Transducer Technology
• TnT tagger -- a very efficient statistical part-of-speech tagger
• Jitar HMM part of speech tagger : An open source java implementation of a simple Trigram HMM part-of-speech tagger, based on TnT

Support

• U.S. NSF (award #: 0916280).
• Grant Agency Czech Republic (project ID: P406/10/P328) and