This set of pages (book) is being developed as a 2 hours tutorial to be delivered at PARSEME-COST workshop (Dubrovnik, 26-27 September 2016).

It will also serve as an English introduction to FRMG and this wiki. Comments are welcome !

It should cover the following points:

• a brief description of FRMG and a few words about FRMG wiki
• a brief description of Tree Adjoining Grammars (TAGs) : notion of trees, tree operations, pro and cons of large TAGs
• a presentation of meta-grammars as a solution to design large coverage grammatical descriptions
  • modularity and elementary constraints to ease grammatical descriptions
  • inheritance hierarchy
  • elementary constraints
    • nodes
    • the class itself (desc)
    • equality
    • precedence
    • dominance (father and ancestor)
    • node and class features
    • equations
    • anonymous nodes
    • macros and other short notations
  • resource producers/consumers
  • guards as complex constraints
  • browsing the classes
• getting more compact grammars through tree factorization
  • disjunction
  • guards
  • interleaving or free node ordering
  • repetition (Kleene star)
• browsing the grammar
  • in frmgwiki
  • some statistics about the trees
  • hypertags to link trees and anchors
• Playing with the resulting parser
  • trying a few sentences
  • playing with disambiguation
  • highlighting edges
  • the preprocessing steps
    • Tokenizing with SxPipe
    • lexicon Lefff and FRMG lexer
  • installing the Alpage processing chain
• disambiguation and beyond
  • shared forest
  • derivations vs dependencies
  • hand-crafted disambiguation rules
  • injecting some knowledge
• the hard life : how to conciliate coverage, accuracy, and efficiency !
  • efficiency
    • a few sources of inefficiency (parser & disambiguation)
    • using TIGs
    • factorization
    • lexicalization
    • left-corner (lctag)
    • restrictions
    • guiding (by self training)
      • tagging
      • hypertagging
      • leftcorner restrictions
    • a few stats
  • coverage
    • using test suite and regression
    • using error mining
    • using robust partial parsing
    • using correction rules
  • accuracy
    • learning from the French TreeBank
    • combining with DyALog-SR, a transition-based statistical parser
    • domain adaptation with unsupervised learning (self-training)
    • feature engineering
• FRMG and MWEs
  • at the level of SxPipe (named entities and some frozen expressions such as complex csu)
  • at the level of the parser (+ metagrammar) : predicative nouns and light verbs
  • at the level of the metagrammar : idiomatic expressions
  • at disambiguation level (terms)
  • the conversions issues for output schema with different notions and lists of MWEs
• Discussion(s):
  • developing and maintaining a large coverage meta-grammar
  • starting a meta-grammar for a new language
  • re-using meta-grammar components (hierarchy, classes)
  • exploring new target formalisms