* LORG Readme

  Author: Joseph Le Roux <joseph.le.roux@gmail.com>
  Co-author: Corentin Ribeyre <corentin.ribeyre@gmail.com>
  Date: Tue Dec 23 11:05:56 2014


** Introduction

   This is the README for lorg tools written at [[http://www.nclt.dcu.ie]]
   Questions, bug reports, feedback on the software can be logged via
   [[https://github.com/CNGLdlab/LORG-Release][github]] We'll do our best to get back to you but we won't make any
   promises.

   lorg_tools consist of 4 programs:
   1. twostage_lorgparser: a PCFG-LA parser implementing the CKY
      algorithm and a coarse-to-fine strategy.
   2. tb2gram: a trainer for PCFG-LAs
   3. simple_lorgparser: a simple PCFG parser implementing the CKY
      algorithm (deprecated)
   4. feature_extractor: a feature extractor from parse trees (experimental)

   This software is free to use in non-commercial projects.

   If you use these tools in support of an academic publication,
   please cite the paper [1].


** Installation

*** Dependencies

    1. A C++11-aware compiler tested with (like gcc 4.7.0 and clang 3.4)
    2. You need boost -- >= 1.56 -- available at [[http://www.boost.org][boost page]] or from your favorite package manager.
    3. You may use [[http://code.google.com/p/google-perftools/][Google malloc]] instead of your system implementation. It might speed up memory
       allocation. Autotools will automatically try to detect the availability of this library and use it,
       provided it is usable (deprecated)
    4. Intel Threading Building Blocks available at [[http://threadingbuildingblocks.org][tbb page]] or from your favorite package manager

*** Compilation

      autoreconf --install && ./configure && make && make install

      NB: You may need administrator privileges to install lorg tools system-wide

** Software

*** Generating Grammars From Treebanks: tb2gram

    This program reads a treebank (in PTB format) and create a PCFG-LA,
    in a text format readable by the two-stage parser (cf. infra).
    It implements split-merge EM learning algorithm for PCFG_LAs. See
    [2] for more details on the algorithm.

    1. For the impatient :

       tb2gram treebankfile_1 ... treebankfile_n -o output_prefix

    2. More information : tb2gram -h

       Various parameters can be changed and will give very different results.
       In particular:
       - random-seed :: change the random seed for EM initialisation.
                        It will help getting different grammars with
                        the same parameters (useful if you want to
                        test the relevance of your new parameters, for
                        example a new tag set).


       - unknown-word-mapping :: how rare words are classified during
            training to handle unknown words at parsing. See [1] for
            details. This parameter can be set to:
            + generic : is the simplest, and will work for all languages
            + BerkeleyEnglish : use the signatures from the Berkeley
              Parser (English)
            + BaselineFrench : use the signatures from the Berkeley
              Parser (French)
            + EnglishIG : use the signatures collected on PTB ranked by
              information gain (English). See [1] for more information.
            + FrenchIG :use the signatures collected on FTB ranked by
              information gain (French)
            + Arabic : use morphotactic signatures (Arabic)
            + ArabicIG : use morphotactics and information gain
              (Arabic)
            + ItalianIG : signatures collected from Italian corpora (experimental)

       - unknown-word-cutoff :: cut-off for classifying rare words.
            Default is 5. Again see [1] for details

       - nbthreads :: Number of threads. Default is 1 (seems to be
                      useless with recent tbb implementations)
       - remove-functions :: Remove grammatical functions from node
            names in the treebank before learning the grammar. Default
            is 1 (set).

       - hm and vm :: set the horizontal (hm) an vertical (vm)
                      markovization for rule extraction. Defaults are
                      1 for vm (node names are not modified) and 0 for
                      hm (1 intermediate symbol by original symbol)

     3. Examples

        a. Generating a grammar for English

           tb2gram $WSJ/0[2-9] $WSJ/1* $WSJ/2[01] -v --nbthreads 8 -w EnglishIG -u 1 -o english_grammar

           This will recursively read sections 02-21 for training
           data, be verbose, use (by default) 6 split/merge/smooth
           steps, 8 threads, the automatically acquired signatures for
           English, replace tokens occurring once in the training data
           with their signatures. Please note that it will also use the
           default markovization settings.



** twostage_lorgparser


   This program takes as input a grammar and text and outputs parse
    trees for this text. It implements a coarse-to-fine CKY PCFG-LA parser.

    1. for the impatient :

       twostage_lorgparser input -g grammar_file -o output

       where the grammar file was created by the trainer, and input is a file of sentences (one per line).

       If input is not set, the parser will read from standard input and, accordingly, if output
       is not set the parser will write on standard output.

    2. more information :

       twostage_lorgparser -h

    3. You should use the same signatures (option --unknown-word-mapping)
       as in training. The parser will output a warning if this is not
       the case.

    4. These are the parameters that you may want to change:
       unknown-word-mapping: should have the same value for training and parsing.
       beam-threshold: the probability threshold used for chart construction.
       - stubbornness :: number of parse attempts with increasing lower beam-thresholds.
                         The last attempt is performed without beam (potentially leading
                         to huge forests). A negative value disables this feature.
       - accurate :: a finer set of thresholds for the coarse-to-fine solution extraction (experimental)
       - parser-type :: the algorithm used for solution extraction
                        - vit ::  Viterbi (PCFG approximation)
                        - kmax :: k-best MaxRule output a list of solutions of length k. Use the --k to
                                  set the length of the list and --verbose to display solution scores.
                        - max ::  MaxRule. This is equivalent to kmax with k set to 1 but note that
                                 it is more efficient. This is the default setting.
                        - maxn :: MaxRule with several grammars. If
                                  you use this parsing method, the
                                  command line would be something
                                  like:

                                  twostage_lorgparser input -g grammar_file -o output -F othergram_1 ... -F othergram_n
        - input-type :: input format (see section on [sentence files] )

     5. Example

        twostage_lorgparser wsj23.tagged -g english_grammar_smoothed6 -o wsj3.parsed -w EnglishIG --input-type tag --parser-type kmax --k 20

        This will parse the file wsj23.tagged, assuming that it is in
        the "tag" format (see below), using the grammar
        english_grammar_smoothed6, the maxrule algorithm, returning the
        20 best parses for each sentence.

        [sentence files]: sec-5-2

*** simple_lorgparser

   (deprecated)



*** Helper scripts
===================

**** format_output.sh
     This script will remove extra comment lines from the parser's
     output to conform to evalb format.

** A note for people without root access and/ or non standard boost installation

   If your version of boost is not installed system-wide or more
   generally if it is installed in a non-standard directory ~dir~,
   be sure to add ~dir~ in your ~LD_LIBRARY_PATH~. For
   example, add in your .bashrc: ~export LD_LIBRARY_PATH=dir:$LD_LIBRARY_PATH~


   If you add signatures for a new language, and you feel like new
   versions of lorg tools should have these signatures, please
   contact us via github.

** Format
   We present the formats for the different types of files used by our software

*** Treebanks
    Treebanks should be in PTB format. Trees can be on several lines.
    Encoding must be UTF-8.

*** Sentence files
    These files must contain one sentence by line. There are 3
    different types of input :

    - raw :: The parser does its own tokenizing. This feature is
             only implememented for English and is highly experimental.
    - tok :: The input is tokenized as in the corresponding treebank.
    - tag :: The input is tokenized and each token has a list of
             predicted pos tags. Input looks like:

             tok_1 ( TAG_11 ... TAG_1n ) ... tok_m ( TAG_m1 ... TAG_mp )

             Caution: Spaces before and after parentheses are mandatory.

    For the last 2 types of input, strings "[", "[[" , "]" and "]]"
    have a special meaning. There are treated like chunk delimiters
    with the following semantics. "[" or "[" mark the beginning of a
    chunk, while "]" or "]" mark the end of a chunk. Double symbols
    indicate strong frontiers while simple ones refer to weak
    frontiers.

    IMPORTANT:
    --> You should escape the input parentheses if they have no special meaning
    --> You should escape the squared parentheses in the input and in the training set, if not

    - lat :: The input is a lattice (or a dag, or an acyclic automaton).
             Each line is an edge of the form:

             begin_postion end_position word [ optional list of postags ]

             Sentences are separated by an empty line. This returns the
             best tree over the lattice, so it chooses one path. This might be
             useful to parse the output of a speech recognition system but
             can also be used for other purposes (for example, see [3])
             This is still an experimental feature.


*** Grammars
     We have chosen text format over binary format, so grammars can
     be more easily amended using scripts. On the other hand, this
     makes our grammars quite large on disk.  An annotated grammar is
     made of annotation information followed by annotation histories,
     followed by grammar rules, followed by lexical rules.

     Grammar ::= Comment* AnnotationHistory+ InternalRule+ LexicalRule+
     AnnotationHistory ::= NT Tree[integer]

     - Comments starts with //
     - An AnnotationInfo gives the number of annotations for a
       non-terminal symbol in the grammar.
     - An AnnotationHistory gives the history of splits for a
       non-terminal symbol.
     - In the future Annotation Histories and Annotation Information
       will be unified.
     - NT is a string


*** Rules
    There are 2 kinds of rules, lexical rules and internal rules, the
    latter being divided in binary and unary internal rules. See the
    following table for a BNF desciption of the format, where
    annotation is of integer type and probability of floating point
    type.

    InternalRule ::= BinaryRule \/  UnaryRule
    BinaryRule ::= "int" NT NT NT binary_probability+
    UnaryRule ::= "int" NT NT unary_probability+
    binary_probability ::= (annotation,annotation,annotation,probability)
    unary_probability ::= (annotation,annotation,probability)
    LexicalRule ::= "lex" NT word lexical_probability+
    lexical_probability ::= (annotation,probability)


** References

  [1] "Handling Unknown Words in Statistical Latent-Variable Parsing
  Models for Arabic, English and French", Mohammed Attia, Jennifer
  Foster, Deirdre Hogan, Joseph Le Roux, Lamia Tounsi and Josef van
  Genabith, Proceedings of SPMRL 2010.

  [2] "Improved Inference for Unlexicalized Parsing", Slav Petrov and
  Dan Klein, HLT-NAACL 2007

  [3] "Language-Independent Parsing with Empty Elements", Shu Cai,
  David Chiang and Yoav Goldberg, ACL-2011 (Short Paper)


** People
   The following people have worked on the LORG parser: Joseph Le
   Roux, Deirdre Hogan, Jennifer Foster, Corentin Ribeyre, Lamia
   Tounsi and Wolfgang Seeker, Antoine Rozenknop.