ngram.1

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              greater than _M*_m, the _i=_M weights are  used.)   The
              N-gram  counts  themselves  are given in an indexed
              directory structure rooted at _d_i_r, in  an  external
              _f_i_l_e,  or, if _f_i_l_e is the string --, starting on the
              line following the ccoouunnttss keyword.

       --vvooccaabb _f_i_l_e
              Initialize the vocabulary for  the  LM  from  _f_i_l_e.
              This is especially useful if the LM itself does not
              specify a complete vocabulary, e.g., as with --nnuullll.

       --vvooccaabb--aalliiaasseess _f_i_l_e
              Reads  vocabulary alias definitions from _f_i_l_e, con-
              sisting of lines of the form
                   _a_l_i_a_s _w_o_r_d
              This causes all tokens _a_l_i_a_s to be mapped to  _w_o_r_d.

       --nnoonneevveennttss _f_i_l_e
              Read  a list of words from _f_i_l_e that are to be con-
              sidered non-events, i.e., that should only occur in
              LM  contexts,  but  not as predictions.  Such words
              are excluded from sentence  generation  (--ggeenn)  and
              probability summation (--ppppll --ddeebbuugg 33).

       --lliimmiitt--vvooccaabb
              Discard  LM  parameters on reading that do not per-
              tain to the words specified in the vocabulary.  The
              default  is that words used in the LM are automati-
              cally added to the vocabulary.  This option can  be
              used  to  reduce  the memory requirements for large
              LMs that are going to be evaluated only on a  small
              vocabulary subset.

       --uunnkk   Indicates  that  the  LM contains the unknown word,
              i.e., is an open-class LM.

       --mmaapp--uunnkk _w_o_r_d
              Map out-of-vocabulary words to  _w_o_r_d,  rather  than
              the default <<uunnkk>> tag.

       --ttoolloowweerr
              Map  all  vocabulary  to lowercase.  Useful if case
              conventions for text/counts and language model dif-
              fer.

       --mmuullttiiwwoorrddss
              Split  input  words consisting of multiwords joined
              by underscores into their components, before evalu-
              ating LM probabilities.

       --mmiixx--llmm _f_i_l_e
              Read  a  second N-gram model for interpolation pur-
              poses.  The second and any additional  interpolated
              models  can  also  be class N-grams (using the same
              --ccllaasssseess  definitions),  but  are  otherwise   con-
              strained  to be standard N-grams, i.e., the options
              --ddff, --ttaaggggeedd, --sskkiipp, and --hhiiddddeenn--vvooccaabb do not apply
              to them.
              NNOOTTEE:: Unless --bbaayyeess (see below) is specified, --mmiixx--
              llmm triggers a static interpolation of the models in
              memory.   In  most  cases a more efficient, dynamic
              interpolation is sufficient, requested by --bbaayyeess 00.
              Also,  mixing models of different type (e.g., word-
              based and class-based)  will  _o_n_l_y  work  correctly
              with dynamic interpolation.

       --llaammbbddaa _w_e_i_g_h_t
              Set the weight of the main model when interpolating
              with --mmiixx--llmm.  Default value is 0.5.

       --mmiixx--llmm22 _f_i_l_e

       --mmiixx--llmm33 _f_i_l_e

       --mmiixx--llmm44 _f_i_l_e

       --mmiixx--llmm55 _f_i_l_e

       --mmiixx--llmm66 _f_i_l_e

       --mmiixx--llmm77 _f_i_l_e

       --mmiixx--llmm88 _f_i_l_e

       --mmiixx--llmm99 _f_i_l_e
              Up to 9 more N-gram models  can  be  specified  for
              interpolation.

       --mmiixx--llaammbbddaa22 _w_e_i_g_h_t

       --mmiixx--llaammbbddaa33 _w_e_i_g_h_t

       --mmiixx--llaammbbddaa44 _w_e_i_g_h_t

       --mmiixx--llaammbbddaa55 _w_e_i_g_h_t

       --mmiixx--llaammbbddaa66 _w_e_i_g_h_t

       --mmiixx--llaammbbddaa77 _w_e_i_g_h_t

       --mmiixx--llaammbbddaa88 _w_e_i_g_h_t

       --mmiixx--llaammbbddaa99 _w_e_i_g_h_t
              These  are  the  weights for the additional mixture
              components, corresponding to --mmiixx--llmm22 through --mmiixx--
              llmm99.   The  weight for the --mmiixx--llmm model is 1 minus
              the sum of --llaammbbddaa and --mmiixx--llaammbbddaa22  through  --mmiixx--
              llaammbbddaa99.

       --lloogglliinneeaarr--mmiixx
              Implement a log-linear (rather than linear) mixture
              LM, using the parameters above.

       --bbaayyeess _l_e_n_g_t_h
              Interpolate the second and  the  main  model  using
              posterior  probabilities  for local N-gram-contexts
              of length _l_e_n_g_t_h.  The --llaammbbddaa value is used  as  a
              prior mixture weight in this case.

       --bbaayyeess--ssccaallee _s_c_a_l_e
              Set  the  exponential  scale  factor on the context
              likelihood in conjunction with the --bbaayyeess function.
              Default value is 1.0.

       --ccaacchhee _l_e_n_g_t_h
              Interpolate  the main LM (or the one resulting from
              operations above) with  a  unigram  cache  language
              model based on a history of _l_e_n_g_t_h words.

       --ccaacchhee--llaammbbddaa _w_e_i_g_h_t
              Set interpolation weight for the cache LM.  Default
              value is 0.05.

       --ddyynnaammiicc
              Interpolate the main LM (or the one resulting  from
              operations  above)  with a dynamically changing LM.
              LM changes are indicated by the  tag  ``<LMstate>''
              starting  a  line in the input to --ppppll, --ccoouunnttss, or
              --rreessccoorree, followed by a filename containing the new
              LM.

       --ddyynnaammiicc--llaammbbddaa _w_e_i_g_h_t
              Set   interpolation  weight  for  the  dynamic  LM.
              Default value is 0.05.

       --aaddaapptt--mmaarrggiinnaallss _L_M
              Use  an  LM  obtained  by  adapting   the   unigram
              marginals  to  the  values  specified  in the _L_M in
              nnggrraamm--ffoorrmmaatt(5),  using  the  method  described  in
              Kneser et al. (1997).  The LM to be adapted is that
              constructed according to the other options.

       --bbaassee--mmaarrggiinnaallss _L_M
              Specify the baseline unigram marginals in  a  sepa-
              rate  file  _L_M, which must be in nnggrraamm--ffoorrmmaatt(5) as
              well.  If not specified, the baseline marginals are
              taken  from the model to be adapted, but this might
              not be desirable, e.g., when  Kneser-Ney  smoothing
              was used.

       --aaddaapptt--mmaarrggiinnaallss--bbeettaa _B
              The  exponential  weight given to the ratio between
              adapted and baseline  marginals.   The  default  is
              0.5.

       --aaddaapptt--mmaarrggiinnaallss--rraattiiooss
              Compute  and  output only the log ratio between the
              adapted and the baseline LM  probabilities.   These
              can  be useful as a separate knowledge source in N-
              best rescoring.

       The following options  specify  the  operations  performed
       on/with the LM constructed as per the options above.

       --rreennoorrmm
              Renormalize  the  main model by recomputing backoff
              weights for the given probabilities.

       --pprruunnee _t_h_r_e_s_h_o_l_d
              Prune N-gram probabilities if their removal  causes
              (training  set) perplexity of the model to increase
              by less than _t_h_r_e_s_h_o_l_d relative.

       --pprruunnee--lloowwpprroobbss
              Prune N-gram probabilities that are lower than  the
              corresponding backed-off estimates.  This generates
              N-gram models that can be correctly converted  into
              probabilistic finite-state networks.

       --mmiinnpprruunnee _n
              Only  prune  N-grams  of  length  at  least _n.  The
              default (and minimum allowed  value)  is  2,  i.e.,
              only  unigrams  are  excluded  from  pruning.  This
              option applies to both --pprruunnee and  --pprruunnee--lloowwpprroobbss.

       --rreessccoorree--nnggrraamm _f_i_l_e
              Read  an  N-gram  LM from _f_i_l_e and recompute its N-
              gram probabilities using the LM  specified  by  the
              other  options;  then  renormalize and evaluate the
              resulting new N-gram LM.

       --wwrriittee--llmm _f_i_l_e
              Write a model back to _f_i_l_e.  The output will be  in
              the  same  format  as read by --llmm, except if opera-
              tions  such  as  --mmiixx--llmm  or  --eexxppaanndd--ccllaasssseess  were
              applied,  in which case the output will contain the
              generated  single  N-gram  backoff  model  in  ARPA
              nnggrraamm--ffoorrmmaatt(5).

       --wwrriittee--bbiinn--llmm _f_i_l_e
              Write  a  model to _f_i_l_e using a binary data format.
              This is only  supported  by  certain  model  types,
              specifically,  N-gram backoff models.  Binary model
              files should be  recognized  automatically  by  the
              --rreeaadd function.

       --wwrriittee--vvooccaabb _f_i_l_e
              Write the LM's vocabulary to _f_i_l_e.

       --ggeenn _n_u_m_b_e_r
              Generate _n_u_m_b_e_r random sentences from the LM.

       --sseeeedd _v_a_l_u_e
              Initialize  the  random  number  generator used for
              sentence generation using seed _v_a_l_u_e.  The  default
              is to use a seed that should be close to unique for
              each invocation of the program.