437 lines
15 KiB
HTML
437 lines
15 KiB
HTML
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<! $Id: hidden-ngram.1,v 1.34 2019/09/09 22:35:36 stolcke Exp $>
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<HTML>
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<HEADER>
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<TITLE>hidden-ngram</TITLE>
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<BODY>
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<H1>hidden-ngram</H1>
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<H2> NAME </H2>
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hidden-ngram - tag hidden events between words
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<H2> SYNOPSIS </H2>
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<PRE>
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<B>hidden-ngram</B> [ <B>-help</B> ] <I>option</I> ...
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</PRE>
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<H2> DESCRIPTION </H2>
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<B> hidden-ngram </B>
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tags a stream of word tokens with hidden events occurring between words.
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For example, an unsegmented text could be tagged for sentence boundaries
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(the hidden events in this case being `boundary' and `no-boundary').
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The most likely hidden tag sequence consistent with the given word
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sequence is found according to an N-gram language model over both
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words and hidden tags.
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<P>
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<B> hidden-ngram </B>
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is a generalization of
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<A HREF="segment.1.html">segment(1)</A>.
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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 word sequences to be tagged
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(one sentence per line).
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Start- and end-of-sentence tags are
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<I> not </I>
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added by the program, but should be included in the input if the
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language model uses them.
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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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Read the input words from a map file contain both the words and
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additional likelihoods of events following each word.
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Each line contains one input word, plus optional hidden-event/likelihood
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pairs in the format
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<PRE>
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<I>w</I> <I>e1</I> [<I>p1</I>] <I>e2</I> [<I>p2</I>] ...
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</PRE>
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If a <I>p</I> value is omitted a likelihood of 1 is assumed.
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All events not explicitly listed are given likelihood 0, and are
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hence excluded for that word.
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In particular, the label
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<B> *noevent* </B>
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must be listed to allow absence of a hidden event.
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Input word strings are assembled from multiple lines of
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<B> -text-map </B>
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input until either an end-of-sentence token </s> is found, or an escaped
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line (see
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<B>-escape</B>)<B></B><B></B><B></B>
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is encountered.
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<DT><B> -logmap </B>
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<DD>
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Interpret numeric values in the
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<B> -text-map </B>
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file as log probabilities, rather
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than 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 word/tag language model as a standard ARPA N-gram backoff model
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file in
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<A HREF="ngram-format.5.html">ngram-format(5)</A>.
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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 can results 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 3 (use a trigram model).
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<DT><B>-classes</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Interpret the LM as an N-gram over word classes.
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The expansions of the classes are given in
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<I>file</I><I></I><I></I><I></I>
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in
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<A HREF="classes-format.5.html">classes-format(5)</A>.
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Tokens in the LM that are not defined as classes in
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<I> file </I>
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are assumed to be plain words, so that the LM can contain mixed N-grams over
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both words and word classes.
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<DT><B>-simple-classes</B><B></B><B></B><B></B>
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<DD>
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Assume a "simple" class model: each word is member of at most one word class,
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and class expansions are exactly one word long.
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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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The second and any additional interpolated models can also be class N-grams
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(using the same
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<B> -classes </B>
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definitions).
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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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<B>-mix-lm</B>,<B></B><B></B><B></B>
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etc. as factored N-gram model specifications.
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See
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<A HREF="ngram.1.html">ngram(1)</A>
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for more 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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<DT><B> -tolower </B>
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<DD>
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Map vocabulary to lowercase, removing case distinctions.
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<DT><B>-vocab</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Initialize the vocabulary for the LM from
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<I>file</I>.<I></I><I></I><I></I>
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This is useful in conjunction with
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<B>-limit-vocab</B>.<B></B><B></B><B></B>
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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>
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This causes all tokens
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<I> alias </I>
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to be mapped to
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<I>word</I>.<I></I><I></I><I></I>
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<DT><B>-hidden-vocab</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
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<DD>
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Read the list of hidden tags from
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<I>file</I>.<I></I><I></I><I></I>
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Note: This is a subset of the vocabulary contained in the language model.
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<DT><B> -limit-vocab </B>
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<DD>
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Discard LM parameters on reading that do not pertain to the words
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specified in the vocabulary, either by
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<B> -vocab </B>
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or
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<B>-hidden-vocab</B>.<B></B><B></B><B></B>
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The default is that words used in the LM are automatically added to the
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vocabulary.
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This option can be used to reduce the memory requirements for large LMs
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that are going to be evaluated only on a small vocabulary subset.
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<DT><B> -force-event </B>
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<DD>
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Forces a non-default event after every word.
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This is useful for language models that represent the default event
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explicitly with a tag, rather than implicitly by the absence of a tag
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between words (which is the default).
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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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Also, with this option the LM is assumed to be open-vocabulary
|
||
|
|
(the default is close-vocabulary).
|
||
|
|
<DT><B> -fb </B>
|
||
|
|
<DD>
|
||
|
|
Perform forward-backward decoding of the input token sequence.
|
||
|
|
Outputs the tags that have the highest posterior probability,
|
||
|
|
for each position.
|
||
|
|
The default is to use Viterbi decoding, i.e., the output is the
|
||
|
|
tag sequence with the highest 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> -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.
|
||
|
|
Input tokens are output one-per-line, followed by event tokens.
|
||
|
|
<DT><B> -posteriors </B>
|
||
|
|
<DD>
|
||
|
|
Output the table of posterior probabilities for each
|
||
|
|
tag position.
|
||
|
|
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 tag at given position.
|
||
|
|
<DT><B> -totals </B>
|
||
|
|
<DD>
|
||
|
|
Output the total string probability for each input sentence.
|
||
|
|
If
|
||
|
|
<B> -fb </B>
|
||
|
|
is also specified this probability is obtained by summing over all
|
||
|
|
hidden event sequences; otherwise it is calculated (i.e., underestimated)
|
||
|
|
using the most probably hidden event sequence.
|
||
|
|
<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>
|
||
|
|
Write the posterior weighted counts of n-grams, including those
|
||
|
|
with hidden tags, summed over the entire input data, to
|
||
|
|
<I>file</I>.<I></I><I></I><I></I>
|
||
|
|
The posterior probabilities should normally be computed with the
|
||
|
|
forward-backward algorithm (instead of Viterbi), so the
|
||
|
|
<B> -fb </B>
|
||
|
|
option is usually also specified.
|
||
|
|
Only n-grams whose contexts occur in the language model are output.
|
||
|
|
<DT><B>-unk-prob</B><I> L</I><B></B><I></I><B></B><I></I><B></B>
|
||
|
|
<DD>
|
||
|
|
Specifies that unknown words and other words having zero probability in
|
||
|
|
the language model be assigned a log probability of
|
||
|
|
<I>L</I>.<I></I><I></I><I></I>
|
||
|
|
This is -100 by default but might be set to 0, e.g., to compute
|
||
|
|
perplexities excluding unknown words.
|
||
|
|
<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.
|
||
|
|
<BR>
|
||
|
|
The dynamic programming for event decoding is not efficiently interleaved
|
||
|
|
with that required to evaluate class N-gram models;
|
||
|
|
therefore, the state space generated
|
||
|
|
in decoding with
|
||
|
|
<B>-classes</B><B></B><B></B><B></B>
|
||
|
|
quickly becomes infeasibly large unless
|
||
|
|
<B>-simple-classes</B><B></B><B></B><B></B>
|
||
|
|
is also specified.
|
||
|
|
<P>
|
||
|
|
The file given by
|
||
|
|
<B> -classes </B>
|
||
|
|
is read multiple times if
|
||
|
|
<B> -limit-vocab </B>
|
||
|
|
is in effect or if a mixture of LMs is specified.
|
||
|
|
This will lead to incorrect behavior if the argument of
|
||
|
|
<B> -classes </B>
|
||
|
|
is stdin (``-'').
|
||
|
|
<H2> SEE ALSO </H2>
|
||
|
|
<A HREF="ngram.1.html">ngram(1)</A>, <A HREF="ngram-count.1.html">ngram-count(1)</A>, <A HREF="disambig.1.html">disambig(1)</A>, <A HREF="segment.1.html">segment(1)</A>,
|
||
|
|
<A HREF="ngram-format.5.html">ngram-format(5)</A>, <A HREF="classes-format.5.html">classes-format(5)</A>.
|
||
|
|
<BR>
|
||
|
|
A. Stolcke et al., ``Automatic Detection of Sentence Boundaries and
|
||
|
|
Disfluencies based on Recognized Words,''
|
||
|
|
<I>Proc. ICSLP</I>, 2247-2250, Sydney.
|
||
|
|
<H2> AUTHORS </H2>
|
||
|
|
Andreas Stolcke <stolcke@icsi.berkeley.edu>,
|
||
|
|
Anand Venkataraman <anand@speech.sri.com>
|
||
|
|
<BR>
|
||
|
|
Copyright (c) 1998-2006 SRI International
|
||
|
|
</BODY>
|
||
|
|
</HTML>
|