OpenGrm NGram Library Quick Tour

This tour is organized around the stages of n-gram model creation, modification and use:

  • corpus I/O (ngramsymbols, farcompilestrings and farprintstrings)
  • n-gram model format
  • n-gram counting (ngramcount)
  • n-gram model parameter estimation (ngrammake)
  • n-gram model merging, pruning and constraining (ngrammerge, ngramshrink and ngrammarginalize)
  • model I/O (ngramread, ngramprint and ngraminfo)
  • n-gram model sampling, application and evaluation (ngramrandgen, ngramapply and ngramperplexity)

For additional details, follow the links to each operation's full documentation found in each section and in tthe summary table of available operations below.

Corpus I/O

Text corpora are represented as binary finite-state archives, with one automaton per sentence. This provides efficient later processing by the NGram Library utilities and allows if desired more general probabilistic input (e.g. weighted DAGs or lattices).

The first step is to generate an OpenFst-style symbol table for the text tokens in input corpus. This can be done with the command-line utility ngramsymbols. For example, the symbols in the text of Oscar Wilde's Importance of Being Earnest, using the suitably normalized copy found here, can be extracted with:

$ ngramsymbols <earnest.txt >earnest.syms

If multiple corpora, e.g. for a separate training set and a test set, are to be processed together, the same symbol table should be used throughout. This can be accomplished by concatenating the corpora when passed to ngramsymbols, eliminating out-of-vocabulary symbols. By default, ngramsymbols creates symbol table entries for <epsilon> and an out-of-vocabulary token <unk>. The identity of these labels can be changed using flags. A flag can then be passed to farcompilestrings to specify the out-of-vocabulary label, so that words not in the symbol table will get mapped to that index.


Given a symbol table, a text corpus can be converted to a binary FAR archive with:

$ farcompilestrings -symbols=earnest.syms -keep_symbols=1 earnest.txt >earnest.far

and can be printed with:

$ farprintstrings earnest.far >earnest.txt

Model Format

All n-gram models produced by the utilities here, including those with unnormalized counts, have a cyclic weighted finite-state transducer (FST) format, encoded using the OpenFst library. For the precise details of the n-gram format, see here.

The model is normally stored in a general-purpose, mutable (VectorFst) format, which is convenient for the various processing steps described below.This can be converted to a more compact (but immutable) format specifically for n-gram models (NGramFst) when the desired final model is generated.

N-gram Counting

ngramcount is a command line utility for counting n-grams from an input corpus, represented in FAR format. It produces an n-gram model in the FST format described above. Transitions and final costs are weighted with the negative log count of the associated n-gram. By using the switch --order the maximum length n-gram to count can be chosen. All n-grams observed in the input corpus of length less than or equal to the specified order will be counted. By default, the order is set to 3 (trigram model).

The 1-gram through 5-gram counts for the earnest.far finite-state archive file created above can be created with:

$ ngramcount -order=5 earnest.far >earnest.cnts

N-gram Model Parameter Estimation

ngrammake is a command line utility for normalizing and smoothing an n-gram model. It takes as input the FST produced by ngramcount (which contains raw, unnormalized counts).

The 5-gram counts in earnest.cnts created above can be converted into a n-gram model with:

$ ngrammake earnest.cnts >earnest.mod

Flags to ngrammake specify the smoothing (e.g. Katz, Knesser-Ney, etc) used with the default being Katz.

Here is a generated sesntence from the language model (using ngramrandgen, which is described below):

$ ngramrandgen earnest.mod | farprintstrings
I <epsilon> WOULD STRONGLY <epsilon> ADVISE YOU MR WORTHING TO TRY <epsilon> AND <epsilon> ACQUIRE <epsilon> SOME RELATIONS AS <epsilon> <epsilon> <epsilon> FAR AS THE PIANO IS CONCERNED <epsilon> SENTIMENT <epsilon> IS MY FORTE <epsilon>  

(An epsilon transition is emitted for each backoff.)

N-gram Model Merging, Pruning and Constraining

ngrammerge is a command line utility for merging two n-gram models into a single model -- either unnormalized counts or smoothed, normalized models. For example, suppose we split our corpus up into two parts, earnest.aa and earnest.ab, and derive 5-gram counts from each independently using ngramcount as shown above. We can then merge the counts to get the same counts as derived above from the full corpus (earnest.cnts):

$ ngrammerge earnest.aa.cnts earnest.ab.cnts >earnest.merged.cnts
$ fstequal earnest.cnts earnest.merged.cnts

Note that, unlike our example merging unnormalized counts above, merging two smoothed models that have been built from half a corpus each will result in a different model than one built from the corpus as a whole, due to the smoothing and mixing. Each of the two model or count FSTs can be weighted, using the --alpha switch for the first input FST, and the --beta switch for the second input FST.


ngramshrink is a command line utility for pruning n-gram models.

The following command shrinks the 5-gram model created above using entropy pruning to roughly 1/10 the original size:

$ ngramshrink -method=relative_entropy -theta=.00015 earnest.mod >earnest.pru

A random sentence generated through this LM is:

$ ngramrandgen earnest.pru | farprintstrings
I THINK <epsilon> BE ABLE TO <epsilon> DIARY GWENDOLEN WONDERFUL SECRETS MONEY <epsilon> YOU <epsilon>  


ngrammarginalize is a command line utility for re-estimating smoothed n-gram models using marginalization constraints similar to Kneser-Ney smoothing.

The following imposes marginalization constraints on the 5-gram model created above:

$ ngrammarginalize earnest.mod >earnest.marg.mod

This functionality is available in version 1.1.0 and higher. Note that this algorithm may need to be run for several iterations, using the --iterations switch. See full operation documentation for further considerations and references.

N-gram Model Reading, Printing and Info

ngramprint is a command line utility for reading in n-gram models and producing text files. Both raw counts and normalized models are encoded with the same automaton structure, so either can be accessed for this function. There are multiple options for output. For example, using the example 5-gram model created below, the following prints out a portion of it in ARPA format:

$ ngramprint --ARPA earnest.mod >earnest.ARPA
$ head -15 earnest.ARPA
\data\
ngram 1=2306
ngram 2=10319
ngram 3=14796
ngram 4=15218
ngram 5=14170

\1-grams:
-99   <s>   -0.9399067
-1.064551   </s>
-3.337681   MORNING   -0.3590219
-2.990894   ROOM   -0.4771213
-1.857355   IN   -0.6232494
-2.87695   ALGERNON   -0.4771213

ngramread is a command line utility for reading in textual representations of n-gram models and producing FSTs appropriate for use by other functions and utilities. It has several options for input. For example,

$ ngramread --ARPA earnest.ARPA >earnest.mod

generates a n-gram model in FST format from the ARPA n-gram language model specification.

ngraminfo is a command-line utility that prints out various information about an n-gram language model in FST format.

$ ngraminfo earnest.mod
# of states                                       39076
# of ngram arcs                                   51618
# of backoff arcs                                 39075
initial state                                     1
unigram state                                     0
# of final states                                 5190
ngram order                                       5
# of 1-grams                                      2305
# of 2-grams                                      10319
# of 3-grams                                      14796
# of 4-grams                                      15218
# of 5-grams                                      14170
well-formed                                       y
normalized                                        y

N-gram Model Sampling, Application and Evaluation

ngramrandgen is a command line utility for sampling from n-gram models.

$ ngramrandgen --max_sents=1 earnest.mod | farprintstrings
IT IS SIMPLY A VERY INEXCUSABLE MANNER


ngramapply is a command line utility for applying n-gram models. It can be called to apply a model to a concatenated archive of automata:

$ ngramapply earnest.mod earnest.far | farprintstrings -print_weight

The result is a FAR weighted by the n-gram model.


ngramperplexity can be used to evaluate an n-gram model. For example, the following calculates the perplexity of two strings (a hand bag and bag hand a) from the example 5-gram model generated above:

echo -e "A HAND BAG\nBAG HAND A" |\
    farcompilestrings -generate_keys=1 -symbols=earnest.syms --keep_symbols=1 |\
    ngramperplexity --v=1 earnest.mod -
A HAND BAG
                                                ngram  -logprob
        N-gram probability                      found  (base10)
        p( A | <s> )                         = [2gram]  1.87984
        p( HAND | A ...)                     = [2gram]  2.56724
        p( BAG | HAND ...)                   = [3gram]  0.0457417
        p( </s> | BAG ...)                   = [4gram]  0.507622
1 sentences, 3 words, 0 OOVs
logprob(base 10)= -5.00044;  perplexity (base 10)= 17.7873

BAG HAND A
                                                ngram  -logprob
        N-gram probability                      found  (base10)
        p( BAG | <s> )                       = [1gram]  4.02771
        p( HAND | BAG ...)                   = [1gram]  3.35968
        p( A | HAND ...)                     = [1gram]  2.51843
        p( </s> | A ...)                     = [1gram]  1.53325
1 sentences, 3 words, 0 OOVs
logprob(base 10)= -11.4391;  perplexity (base 10)= 724.048

2 sentences, 6 words, 0 OOVs
logprob(base 10)= -16.4395;  perplexity (base 10)= 113.485

Using the C++ Library

The OpenGrm NGram library is a C++ library. Users can call the available operations from that level rather than from the command line if desired. From C++, include <ngram/ngram.h> in the installation include directory and link to libfst.so, libfar.so, and libngram.so in the installation library directory. This assumes you've installed OpenFst (with --enable-far=yes). (You may instead use just those include files for the classes and functions that you will need.) All classes and functions are in the ngram namespace.

As mentioned earlier, each n-gram model, including those with unnormalized counts, is represented as a weighted FST. Each of the n-gram operation classes holds the FST in the common base class NGramModel. A partial description of this class follows:

template <class Arc>
class NGramModel {
public:
   typedef typename Arc::StateId StateId; 
 
   // Construct an NGramModel object, consisting of the FST and some
   // information about the states under the assumption that the FST is
   // a model.  
   explicit NGramModel(const Fst<Arc> &infst);  

   // Returns highest n-gram order. 
   int HiOrder() const;    
   // Returns order of a given state.  
   int StateOrder(StateId state) const;   
   // Returns the unigram state. 
   StateId UnigramState() const;  
  
   // Validates model has a well-formed n-gram topology 
   bool CheckTopology() const;   
   // Validates that states are fully normalized (probabilities sum to 1.0) 
   bool CheckNormalization() const;   

   // Gets a const reference to the internal (expanded) FST. 
   const Fst<Arc> &GetFst() const;
     
private:
   const Fst<Arc> &fst_;
};

From this class is derived NGramCount for counting, NGramMake for parameter estimation/smoothing, NGramShrink for model pruning, NGramMerge for model interpolation/merging (among others). NGramMake and NGramShrink are further sub-classed for each specific smoothing and pruning method. For example, NGramMake has methods (some abstract) common to most/all parameter estimation/smoothing techniques while NGramKatz has the specific implementations for that method.

Available Operations

Click on operation name for additional information.

Operation Usage Description
NGramApply ngramapply [--bo_arc_type] ngram.fst [in.far [out.far]] Intersect n-gram model with fst archive
NGramCount ngramcount [--order] [in.far [out.fst]] count n-grams from fst archive
  NGramCounter(order); --- n-gram counter
NGramInfo ngraminfo [in.mod] print various information about an n-gram model
NGramMake ngrammake [--method] [--backoff] [--bins] [--witten_bell_k] [--discount_D] [in.fst [out.fst]] n-gram model smoothing and normalization
  NGramAbsolute(&CountFst); --- Absolute Discount smoothing
  NGramKatz(&CountFst); --- Katz smoothing
  NGramKneserNey(&CountFst); --- Kneser Ney smoothing
  NGramUnsmoothed(&CountFst); --- no smoothing
  NGramWittenBell(&CountFst); --- Witten-Bell smoothing
NGramMarginal ngrammarginalize [--iterations] [--max_bo_updates] [--output_each_iteration] [--steady_state_file] [in.mod [out.mod]] impose marginalization constraints on input model
  NGramMarginal(&M); --- n-gram marginalization constraint class
NGramMerge ngrammerge [--alpha] [--beta] [--use_smoothing] [--normalize] in1.fst in2.fst [out.fst] merge two count or model FSTs
  NGramMerge(&M1, &M2, alpha, beta); --- n-gram merge class
NGramPerplexity ngramperplexity [--OOV_symbol] [--OOV_class_size] [--OOV_probability] ngram.fst [in.far [out.txt]] calculate perplexity of input corpus from model
NGramPrint ngramprint [--ARPA] [--backoff] [--integers] [--negativelogs] [in.fst [out.txt]] print n-gram model to text file
NGramRandgen ngramrandgen [--max_sents] [--max_length] [--seed] [in.mod [out.far]] randomly sample sentences from an n-gram model
NGramRead ngramread [--ARPA] [--epsilon_symbol] [--OOV_symbol] [in.txt [out.fst]] read n-gram counts or model from file
NGramShrink ngramshrink [--method=count,relative_entropy,seymore] [-count_pattern] [-theta] [in.mod [out.mod]] n-gram model pruning
  NGramCountPrune(&M, count_pattern); --- count-based model pruning
  NGramRelativeEntropy(&M, theta); --- relative-entropy-based model pruning
  NGramSeymoreShrink(&M, theta); --- Seymore/Rosenfeld-based model pruning
NGramSymbols ngramsymbols [--epsilon_symbol] [--OOV_symbol] [in.txt [out.txt]] create symbol table from corpus

Convenience Script Work in progress, under construction

The shell script ngram.sh is provided to run some common OpenGrm NGram pipelines of commands and to provide some rudimentary distributed computation support. For example:

$ ngram.sh --itype=text_sents --otype=pruned_lm --ifile=in.txt --ofile=lm.fst --symbols=in.syms --order=5 --smooth_method=katz --shrink_method=relative_entropy --theta=.00015

will read a text corpus in the format accepted by farcompilestrings and output a backoff 5-gram LM pruned with a relative entropy threshold of .00015. See ngram.sh --help for available options and values and see here for a discussion of the distributed computation support.

Topic attachments
I Attachment History Action Size Date Who Comment
Texttxt earnest.txt r1 manage 89.0 K 2010-11-05 - 02:14 MichaelRiley  
Topic revision: r32 - 2017-06-19 - BrianRoark
 
This site is powered by the TWiki collaboration platform Powered by PerlCopyright © 2008-2017 by the contributing authors. All material on this collaboration platform is the property of the contributing authors.
Ideas, requests, problems regarding TWiki? Send feedback