Author: Joseph Le Roux joseph.le.roux@gmail.com Co-author: Corentin Ribeyre corentin.ribeyre@gmail.com Date: 2011-12-09 12:34:42 CET
1 Introduction 2 Installation 2.1 Dependencies 2.2 Compilation 3 Software 3.1 Generating Grammars From Treebanks: tb2gram 3.2 twostage_lorgparser 3.3 simple_lorgparser 3.3.1 How to get a grammar for the simple lorg parser 3.4 Helper scripts 3.4.1 format_output.sh 4 A note for people without root access and/ or non standard boost installation 5 Format 5.1 Treebanks 5.2 Sentence files 5.3 Grammars 5.3.1 Rules 6 Misc.
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 github https://github.com/CNGLdlab/LORG-Release
We'll do our best to get back to you but we won't make any promises.
lorg_tools consist of 3 programs:
simple_lorgparser: a simple PCFG parser implementing the CKY algorithm.
twostage_lorgparser: a PCFG-LA parser implementing the CKY algorithm and a coarse-to-fine strategy.
tb2gram: a trainer for PCFG-LAs
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]. 1. You need boost -- >= 1.46 -- available at [boost page] or from your favorite package manager.
2. You may use [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.
3. Intel Threading Building Blocks available at [tbb page] or from your favorite package manager.
[boost page]: http://www.boost.org
[tbb page]: http://threadingbuildingblocks.org
[Google malloc]: http://code.google.com/p/google-perftools/ Uncompress the archive lorg_tools-SVN.tar.gz, go into the created directory and type :
./configure && make && make install
NB: You may need administrator privileges to install lorg tools system-wide 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.
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. 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.
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
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 a lot 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 The simple lorg_parser implements CKY PCFG parser. How to use:
simple_lorgparser input -g grammar_file -o output3.3.1 How to get a grammar for the simple lorg parser
The easiest way is to convert a PCFG-LA created with tb2gram
using the script annot2simple.pl:
annot2simple annotated_grammar > simple_grammar
This will create a PCFG from a PCFG-LA.
(deprecated)
3.4 Helper scripts
===================
3.4.1 format_output.sh This script will remove extra comment lines from the parser's
output to conform to evalb format. 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 [ENTER ADDRESS HERE] We present the formats for the different types of files used by our software Treebanks should be in PTB format. Trees can be on several
lines. Encoding must be UTF-8 especially for French unknown mapping. 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. 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 ::= AnnotationInfo+ AnnotationHistory+ InternalRule+ LexicalRule+
AnnotationInfo ::= NT integer
AnnotationHistory ::= Tree[integer]
- 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 string5.3.1 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)
6 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)
7. People
----------
The following people have worked on the LORG parser: Joseph Le Roux, Deirdre Hogan, Jennifer Foster, Corentin Ribeyre, Lamia Tounsi and Wolfgang Seeker.