428 lines
14 KiB
HTML
428 lines
14 KiB
HTML
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<! $Id: disambig.1,v 1.33 2019/09/09 22:35:36 stolcke Exp $>
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<HTML>
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<HEADER>
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<TITLE>disambig</TITLE>
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<BODY>
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<H1>disambig</H1>
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<H2> NAME </H2>
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disambig - disambiguate text tokens using an N-gram model
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<H2> SYNOPSIS </H2>
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<B> disambig </B>
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[<B>-help</B>]<B></B><B></B><B></B>
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<I> option </I>
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...
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<H2> DESCRIPTION </H2>
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<B> disambig </B>
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translates a stream of tokens from a vocabulary V1 to a corresponding stream
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of tokens from a vocabulary V2,
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according to a probabilistic, 1-to-many mapping.
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Ambiguities in the mapping are resolved by finding the V2 sequence with
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the highest posterior probability given the V1 sequence.
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This probability is computed from pairwise conditional probabilities P(V1|V2),
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as well as a language model for sequences over V2.
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<H2> OPTIONS </H2>
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<P>
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Each filename argument can be an ASCII file, or a
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compressed file (name ending in .Z or .gz), or ``-'' to indicate
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stdin/stdout.
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<DL>
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<DT><B> -help </B>
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<DD>
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Print option summary.
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<DT><B> -version </B>
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<DD>
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Print version information.
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<DT><B>-text</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Specifies the file containing the V1 sentences.
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<DT><B>-map</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Specifies the file containing the V1-to-V2 mapping information.
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Each line of
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<I> file </I>
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contains the mapping for a single word in V1:
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<PRE>
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<I>w1</I> <I>w21</I> [<I>p21</I>] <I>w22</I> [<I>p22</I>] ...
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</PRE>
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where
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<I> w1 </I>
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is a word from V1, which has possible mappings
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<I>w21</I>,<I></I><I></I><I></I>
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<I>w22</I>,<I></I><I></I><I></I>
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... from V2.
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Optionally, each of these can be followed by a numeric string for the
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probability
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<I>p21</I>,<I></I><I></I><I></I>
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which defaults to 1.
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The number is used as the conditional probability P(<I>w1</I>|<I>w21</I>),
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but the program does not depend on these numbers being properly normalized.
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<DT><B>-escape</B><I> string</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Set an ``escape string.''
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Input lines starting with
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<I> string </I>
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are not processed and passed unchanged to stdout instead.
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This allows associated information to be passed to scoring scripts etc.
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<DT><B>-text-map</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Processes a combined text/map file.
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The format of
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<I> file </I>
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is the same as for
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<B>-map</B>,<B></B><B></B><B></B>
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except that the
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<I> w1 </I>
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field on each line is interpreted as a word
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<I> token </I>
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rather than a word
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<I>type</I>.<I></I><I></I><I></I>
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Hence, the V1 text input consists of all words in column 1 of
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<I> file </I>
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in order of appearance.
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This is convenient if different instances of a word have different mappings.
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There is no implicit insertion of begin/end sentence tokens in this
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mode. Sentence boundaries should be indicated explicitly by
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lines of the form
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<PRE>
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</s> </s>
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<s> <s>
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</PRE>
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An escaped line (see
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<B>-escape</B>)<B></B><B></B><B></B>
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also implicitly marks a sentence boundary.
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<DT><B>-classes</B> file<B></B><B></B><B></B>
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<DD>
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Specifies the V1-to-V2 mapping information in
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<A HREF="classes-format.5.html">classes-format(5)</A>.
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Class labels are interpreted as V2 words, and expansions as V1 words.
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Multi-word expansions are not allowed.
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<DT><B> -scale </B>
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<DD>
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Interpret the numbers in the mapping as P(<I>w21</I>|<I>w1</I>).
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This is done by dividing probabilities by the unigram probabilities of
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<I>w21</I>,<I></I><I></I><I></I>
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obtained from the V2 language model.
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<DT><B> -logmap </B>
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<DD>
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Interpret numeric values in map file as log probabilities, not probabilities.
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<DT><B>-lm</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Specifies the V2 language model as a standard ARPA N-gram backoff model file
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<A HREF="ngram-format.5.html">ngram-format(5)</A>.
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The default is not to use a language model, i.e., choose V2 tokens
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based only on the probabilities in the map file.
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<DT><B>-use-server</B><I> S</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Use a network LM server (typically implemented by
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<A HREF="ngram.1.html">ngram(1)</A>
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with the
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<B> -server-port </B>
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option) as the main model.
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The server specification
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<I> S </I>
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can be an unsigned integer port number (referring to a server port running on
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the local host),
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a hostname (referring to default port 2525 on the named host),
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or a string of the form
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<I>port</I>@<I>host</I>,<I></I><I></I>
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where
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<I> port </I>
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is a portnumber and
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<I> host </I>
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is either a hostname ("dukas.speech.sri.com")
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or IP number in dotted-quad format ("140.44.1.15").
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<BR>
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For server-based LMs, the
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<B> -order </B>
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option limits the context length of N-grams queried by the client
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(with 0 denoting unlimited length).
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Hence, the effective LM order is the mimimum of the client-specified value
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and any limit implemented in the server.
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<BR>
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When
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<B> -use-server </B>
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is specified, the arguments to the options
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<B>-mix-lm</B>,<B></B><B></B><B></B>
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<B>-mix-lm2</B>,<B></B><B></B><B></B>
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etc. are also interpreted as network LM server specifications provided
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they contain a '@' character and do not contain a '/' character.
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This allows the creation of mixtures of several file- and/or
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network-based LMs.
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<DT><B> -cache-served-ngrams </B>
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<DD>
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Enables client-side caching of N-gram probabilities to eliminated duplicate
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network queries, in conjunction with
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<B>-use-server</B>.<B></B><B></B><B></B>
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This may result in a substantial speedup
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but requires memory in the client that may grow linearly with the
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amount of data processed.
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<DT><B>-order</B><I> n</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Set the effective N-gram order used by the language model to
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<I>n</I>.<I></I><I></I><I></I>
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Default is 2 (use a bigram model).
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<DT><B>-mix-lm</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Read a second N-gram model for interpolation purposes.
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<DT><B> -factored </B>
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<DD>
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Interpret the files specified by
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<B>-lm</B><B></B><B></B><B></B>
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and
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<B> -mix-lm </B>
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as a factored N-gram model specification.
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See
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<A HREF="ngram.1.html">ngram(1)</A>
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for details.
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<DT><B>-count-lm</B><B></B><B></B><B></B>
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<DD>
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Interpret the model specified by
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<B>-lm</B><B></B><B></B><B></B>
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(but not
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<B>-mix-lm</B>)<B></B><B></B><B></B>
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as a count-based LM.
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See
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<A HREF="ngram.1.html">ngram(1)</A>
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for details.
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<DT><B>-lambda</B><I> weight</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Set the weight of the main model when interpolating with
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<B>-mix-lm</B>.<B></B><B></B><B></B>
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Default value is 0.5.
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<DT><B>-mix-lm2</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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<DT><B>-mix-lm3</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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<DT><B>-mix-lm4</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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<DT><B>-mix-lm5</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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<DT><B>-mix-lm6</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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<DT><B>-mix-lm7</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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<DT><B>-mix-lm8</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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<DT><B>-mix-lm9</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Up to 9 more N-gram models can be specified for interpolation.
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<DT><B>-mix-lambda2</B><I> weight</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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<DT><B>-mix-lambda3</B><I> weight</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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<DT><B>-mix-lambda4</B><I> weight</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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<DT><B>-mix-lambda5</B><I> weight</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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<DT><B>-mix-lambda6</B><I> weight</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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<DT><B>-mix-lambda7</B><I> weight</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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<DT><B>-mix-lambda8</B><I> weight</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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<DT><B>-mix-lambda9</B><I> weight</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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These are the weights for the additional mixture components, corresponding
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to
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<B> -mix-lm2 </B>
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through
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<B>-mix-lm9</B>.<B></B><B></B><B></B>
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The weight for the
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<B> -mix-lm </B>
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model is 1 minus the sum of
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<B> -lambda </B>
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and
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<B> -mix-lambda2 </B>
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through
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<B>-mix-lambda9</B>.<B></B><B></B><B></B>
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<DT><B>-context-priors</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Read context-dependent mixture weight priors from
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<I>file</I>.<I></I><I></I><I></I>
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Each line in
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<I> file </I>
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should contain a context N-gram (most recent word first) followed by a vector
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of mixture weights whose length matches the number of LMs being interpolated.
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<DT><B>-bayes</B><I> length</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Interpolate models using posterior probabilities
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based on the likelihoods of local N-gram contexts of length
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<I>length</I>.<I></I><I></I><I></I>
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The
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<B> -lambda </B>
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values are used as prior mixture weights in this case.
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This option can also be combined with
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<B>-context-priors</B>,<B></B><B></B><B></B>
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in which case the
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<I> length </I>
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parameter also controls how many words of context are maximally used to look up
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mixture weights.
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If
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<B>-context-priors</B><B></B><B></B><B></B>
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is used without
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<B>-bayes</B>,<B></B><B></B><B></B>
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the context length used is set by the
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<B> -order </B>
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option and Bayesian interpolation is disabled, as when
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<I> scale </I>
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(see next) is zero.
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<DT><B>-bayes-scale</B><I> scale</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Set the exponential scale factor on the context likelihood in conjunction
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with the
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<B> -bayes </B>
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function.
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Default value is 1.0.
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<DT><B>-lmw</B><I> W</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Scales the language model probabilities by a factor
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<I>W</I>.<I></I><I></I><I></I>
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Default language model weight is 1.
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<DT><B>-mapw</B><I> W</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Scales the likelihood map probability by a factor
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<I>W</I>.<I></I><I></I><I></I>
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Default map weight is 1.
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Note: For Viterbi decoding (the default) it is equivalent to use
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<B>-lmw</B><I> W</I><B></B><I></I><B></B><I></I><B></B>
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or
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<B>-mapw</B><I> 1/W",</I><B></B><I></I><B></B><I></I><B></B>
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but not for forward-backward computation.
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<DT><B> -tolower1 </B>
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<DD>
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Map input vocabulary (V1) to lowercase, removing case distinctions.
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<DT><B> -tolower2 </B>
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<DD>
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Map output vocabulary (V2) to lowercase, removing case distinctions.
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<DT><B> -keep-unk </B>
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<DD>
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Do not map unknown input words to the <unk> token.
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Instead, output the input word unchanged.
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This is like having an implicit default mapping for unknown words to
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themselves, except that the word will still be treated as <unk> by the language
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model.
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Also, with this option the LM is assumed to be open-vocabulary
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(the default is close-vocabulary).
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<DT><B>-vocab-aliases</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Reads vocabulary alias definitions from
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<I>file</I>,<I></I><I></I><I></I>
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consisting of lines of the form
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||
|
|
<PRE>
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||
|
|
<I>alias</I> <I>word</I>
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||
|
|
</PRE>
|
||
|
|
This causes all V2 tokens
|
||
|
|
<I> alias </I>
|
||
|
|
to be mapped to
|
||
|
|
<I>word</I>,<I></I><I></I><I></I>
|
||
|
|
and is useful for adapting mismatched language models.
|
||
|
|
<DT><B> -no-eos </B>
|
||
|
|
<DD>
|
||
|
|
Do no assume that each input line contains a complete sentence.
|
||
|
|
This prevents end-of-sentence tokens </s> from being appended automatically.
|
||
|
|
<DT><B> -continuous </B>
|
||
|
|
<DD>
|
||
|
|
Process all words in the input as one sequence of words, irrespective of
|
||
|
|
line breaks.
|
||
|
|
Normally each line is processed separately as a sentence.
|
||
|
|
V2 tokens are output one-per-line.
|
||
|
|
This option also prevents sentence start/end tokens (<s> and </s>)
|
||
|
|
from being added to the input.
|
||
|
|
<DT><B> -fb </B>
|
||
|
|
<DD>
|
||
|
|
Perform forward-backward decoding of the input (V1) token sequence.
|
||
|
|
Outputs the V2 tokens that have the highest posterior probability,
|
||
|
|
for each position.
|
||
|
|
The default is to use Viterbi decoding, i.e., the output is the
|
||
|
|
V2 sequence with the higher joint posterior probability.
|
||
|
|
<DT><B> -fw-only </B>
|
||
|
|
<DD>
|
||
|
|
Similar to
|
||
|
|
<B>-fb</B>,<B></B><B></B><B></B>
|
||
|
|
but uses only the forward probabilities for computing posteriors.
|
||
|
|
This may be used to simulate on-line prediction of tags, without the
|
||
|
|
benefit of future context.
|
||
|
|
<DT><B> -totals </B>
|
||
|
|
<DD>
|
||
|
|
Output the total string probability for each input sentence.
|
||
|
|
<DT><B> -posteriors </B>
|
||
|
|
<DD>
|
||
|
|
Output the table of posterior probabilities for each
|
||
|
|
input (V1) token and each V2 token, in the same format as
|
||
|
|
required for the
|
||
|
|
<B> -map </B>
|
||
|
|
file.
|
||
|
|
If
|
||
|
|
<B> -fb </B>
|
||
|
|
is also specified the posterior probabilities will be computed using
|
||
|
|
forward-backward probabilities; otherwise an approximation will be used
|
||
|
|
that is based on the probability of the most likely path containing
|
||
|
|
a given V2 token at given position.
|
||
|
|
<DT><B>-nbest</B><I> N</I><B></B><I></I><B></B><I></I><B></B>
|
||
|
|
<DD>
|
||
|
|
Output the
|
||
|
|
<I> N </I>
|
||
|
|
best hypotheses instead of just the first best when
|
||
|
|
doing Viterbi search.
|
||
|
|
If
|
||
|
|
<I>N</I>>1,<I></I><I></I><I></I>
|
||
|
|
then each hypothesis is prefixed by the tag
|
||
|
|
<B>NBEST_</B><I>n</I><B> </B><I>x</I><B>,</B><I></I><B></B>
|
||
|
|
where
|
||
|
|
<I> n </I>
|
||
|
|
is the rank of the hypothesis in the N-best list and
|
||
|
|
<I> x </I>
|
||
|
|
its score, the negative log of the combined probability of transitions
|
||
|
|
and observations of the corresponding HMM path.
|
||
|
|
<DT><B>-write-counts</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
|
||
|
|
<DD>
|
||
|
|
Outputs the V2-V1 bigram counts corresponding to the tagging performed on
|
||
|
|
the input data.
|
||
|
|
If
|
||
|
|
<B> -fb </B>
|
||
|
|
was specified these are expected counts, and otherwise they reflect the 1-best
|
||
|
|
tagging decisions.
|
||
|
|
<DT><B>-write-vocab1</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
|
||
|
|
<DD>
|
||
|
|
Writes the input vocabulary from the map (V1) to
|
||
|
|
<I>file</I>.<I></I><I></I><I></I>
|
||
|
|
<DT><B>-write-vocab2</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
|
||
|
|
<DD>
|
||
|
|
Writes the output vocabulary from the map (V2) to
|
||
|
|
<I>file</I>.<I></I><I></I><I></I>
|
||
|
|
The vocabulary will also include the words specified in the language model.
|
||
|
|
<DT><B>-write-map</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
|
||
|
|
<DD>
|
||
|
|
Writes the map back to a file for validation purposes.
|
||
|
|
<DT><B> -debug </B>
|
||
|
|
<DD>
|
||
|
|
Sets debugging output level.
|
||
|
|
</DD>
|
||
|
|
</DL>
|
||
|
|
<P>
|
||
|
|
Each filename argument can be an ASCII file, or a compressed
|
||
|
|
file (name ending in .Z or .gz), or ``-'' to indicate
|
||
|
|
stdin/stdout.
|
||
|
|
<H2> BUGS </H2>
|
||
|
|
The
|
||
|
|
<B> -continuous </B>
|
||
|
|
and
|
||
|
|
<B> -text-map </B>
|
||
|
|
options effectively disable
|
||
|
|
<B>-keep-unk</B>,<B></B><B></B><B></B>
|
||
|
|
i.e., unknown input words are always mapped to <unk>.
|
||
|
|
Also,
|
||
|
|
<B> -continuous </B>
|
||
|
|
doesn't preserve the positions of escaped input lines relative to
|
||
|
|
the input.
|
||
|
|
<H2> SEE ALSO </H2>
|
||
|
|
<A HREF="ngram-count.1.html">ngram-count(1)</A>, <A HREF="ngram.1.html">ngram(1)</A>, <A HREF="hidden-ngram.1.html">hidden-ngram(1)</A>, <A HREF="training-scripts.1.html">training-scripts(1)</A>,
|
||
|
|
<A HREF="ngram-format.5.html">ngram-format(5)</A>, <A HREF="classes-format.5.html">classes-format(5)</A>.
|
||
|
|
<H2> AUTHOR </H2>
|
||
|
|
Andreas Stolcke <stolcke@icsi.berkeley.edu>,
|
||
|
|
Anand Venkataraman <anand@speech.sri.com>
|
||
|
|
<BR>
|
||
|
|
Copyright (c) 1995-2007 SRI International
|
||
|
|
</BODY>
|
||
|
|
</HTML>
|