Hirarki of Languages

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    Hierarchical Structure in Natural Language

    loss_NP

    with_IN a_DT loss_NN of_INthe_DT

    cents_NP

    of_PP

    with_PPloss_NP

    contract_NN ended_VBD

    contract_NP

    ended_VP

    ended_S

    7_CD cents_NNS

    Words are hierarchically organized in syntactic constituents tree structure

    Part of Speech(POS) and Non-Terminal(NT) tags identify the type of constituent

    Lexicalized annotation of intermediate nodes in the tree

    Identifying the syntactic structure

    Parsing

    Automatic parsing of natural language text is an area of active researchMicrosoft Research

    Speech.Net

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    Exploiting Syntactic Structure for Language ModelingCiprian Chelba, Frederick Jelinek

    Hierarchical Structure in Natural Language

    Speech Recognition: Statistical Approach

    Basic Language Modeling:

    Measures for Language Model Quality

    Current Approaches to Language Modeling

    A Structured Language Model:

    Language Model Requirements

    Word and Structure Generation

    Research Issues

    Model Performance: Perplexity results on UPenn-Treebank

    Model Performance: Perplexity and WER results on WSJ/SWB/BN

    Any Future for the Structured Language Model?

    Richer Syntactic Dependencies

    Syntactic Structure Portability

    Information Extraction from TextMicrosoft Research

    Speech.Net

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    Speech Recognition Statistical Approach

    Producer

    Speakers Speech

    Speech Recognizer

    Acoustic

    Processor

    Linguistic

    DecoderW W

    ^A

    Speaker

    Mind

    Acoustic Channel

    Speech

    acoustic model: channel probability;

    language model: source probability;

    searchfor the most likely word string .

    due to the large vocabulary size tens of thousands of words an exhaustivesearch is intractable.

    Microsoft ResearchSpeech.Net

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    Basic Language Modeling

    Estimate the source probability

    ,

    from a training corpus millions of words of text chosen for its similarity to the expectedutterances.

    Parametric conditional models:

    parameter space

    source alphabet (vocabulary)

    Source Modeling Problem

    Microsoft ResearchSpeech.Net

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    Measures for Language Model QualityWord Error Rate (WER)

    TRN: UP UPSTATE NEW YORK SOMEWHERE UH OVER OVER HUGE AREAS

    HYP: UPSTATE NEW YORK SOMEWHERE UH ALL ALL THE HUGE AREAS

    1 0 0 0 0 0 1 1 1 0 0

    :4 errors per 10 words in transcription; WER = 40%

    Evaluating WER reduction is computationally expensive.

    Perplexity(PPL)

    different than maximum likelihood estimation: the test data is not seen during themodel estimation process;

    good models are smooth:

    Microsoft ResearchSpeech.Net

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    Current Approaches to Language Modeling

    Assume a Markov source of order ; equivalence classification of a given context:

    Data sparseness: 3-gram model

    approx. 70% of the trigrams in the training data have been seen once.

    the rate of new (unseen) trigrams in test data relative to those observed in a training

    corpus of size 38 million words is 32% for a 20,000-words vocabulary;

    Smoothing: recursive linear interpolation among relative frequency estimates of different

    orders

    using a recursive mixing scheme:

    Parameters:

    Microsoft ResearchSpeech.Net

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    Exploiting Syntactic Structure for Language Modeling

    Hierarchical Structure in Natural Language

    Speech Recognition: Statistical Approach

    Basic Language Modeling:

    A Structured Language Model: Language Model Requirements

    Word and Structure Generation

    Research Issues:

    Model Component Parameterization

    Pruning Method

    Word Level Probability Assignment

    Model Statistics Reestimation

    Model Performance: Perplexity results on UPenn-Treebank

    Model Performance: Perplexity and WER results on WSJ/SWB/BN

    Microsoft ResearchSpeech.Net

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    A Structured Language Model

    Generalize trigram modeling (local) by taking advantage of sentence structure (influ-ence by more distant past)

    Use exposed heads h (words and their corresponding non-terminal tags ) for

    prediction:

    is the partial hidden structure, with head assignment, provided to

    ended_VBD cents_NNS after

    cents_NP

    of_PP

    loss_NP

    loss_NP

    ended_VP

    with_PP

    with_IN a_DT loss_NN of_IN 7_CDthe_DT contract_NN

    contract_NP

    Microsoft ResearchSpeech.Net

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    Language Model Requirements

    Model must operate left-to-right:

    In hypothesizing hidden structure, the model can use only word-prefix

    i.e., notthe complete sentence

    as all conventional parsers do!

    Model complexity must be limited; even trigram model faces critical data sparsenessproblems

    Model will assign joint probability to sequences of words and hidden parse structure:

    Microsoft ResearchSpeech.Net

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    ended_VBD

    loss_NP

    with_IN a_DT loss_NN of_INthe_DT contract_NN

    contract_NP

    7_CD cents_NNS

    cents_NP

    of_PP

    loss_NP

    with_PP

    ended_VP

    ; null; predict cents; POStag cents; adjoin-right-NP; adjoin-left-PP; ; adjoin-

    left-VP; null;

    ;

    Microsoft ResearchSpeech.Net

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    ended_VBD

    loss_NP

    with_IN a_DT loss_NN of_INthe_DT contract_NN

    contract_NP

    7_CD cents

    cents_NP

    of_PP

    loss_NP

    with_PP

    ended_VP

    _NNS

    ; null; predict cents; POStag cents; adjoin-right-NP; adjoin-left-PP; ; adjoin-

    left-VP; null;

    ;

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    ended_VBD

    loss_NP

    with_IN a_DT loss_NN of_INthe_DT contract_NN

    contract_NP

    7_CD cents

    of_PP

    loss_NP

    with_PP

    ended_VP

    _NNS

    cents_NP

    ; null; predict cents; POStag cents; adjoin-right-NP; adjoin-left-PP; ; adjoin-

    left-VP; null;

    ;

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    ended_VBD

    loss_NP

    with_IN a_DT loss_NN of_INthe_DT contract_NN

    contract_NP

    7_CD cents

    loss_NP

    with_PP

    ended_VP

    _NNS

    cents_NP

    of_PP

    ; null; predict cents; POStag cents; adjoin-right-NP; adjoin-left-PP; ; adjoin-

    left-VP; null;

    ;

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    ended_VBD

    loss_NP

    with_IN a_DT loss_NN of_INthe_DT contract_NN

    contract_NP

    7_CD cents_NNS

    cents_NP

    of_PP

    with_PP

    ended_VP

    loss_NP

    PREDICTOR TAGGER

    PARSER

    predict word

    tag word

    adjoin_{left,right}

    null

    ; null; predict cents; POStag cents; adjoin-right-NP; adjoin-left-PP;

    ; adjoin-

    left-VP; null;

    ;

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    Word and Structure Generation

    predictor

    tagger

    parser

    The predictor generates the next word

    with probability

    The tagger attaches tag

    to the most recently generated word

    with probability

    The parser builds the partial parse

    from

    , and

    in a series of movesending with null, where a parser move is made with probability

    ;

    (adjoin-left, NTtag), (adjoin-right, NTtag), null

    Microsoft ResearchSpeech.Net

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    Research Issues

    Model component parameterization equivalence classifications for model compo-nents:

    Huge number of hidden parses need to prune it by discarding the unlikely ones

    Word level probability assignment calculate

    Model statistics estimation unsupervised algorithm for maximizing

    (mini-mizing perplexity)

    Microsoft ResearchSpeech.Net

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    Pruning Method

    Number of parses

    for a given word prefix

    is

    ;

    Prune most parses without discarding the most likely ones for a given sentence

    Synchronous Multi-Stack Pruning Algorithm

    the hypotheses are ranked according to

    each stack contains partial parses constructed by the same number of parser oper-ations

    The width of the pruning is controlled by:

    maximum number of stack entries

    log-probability threshold

    Microsoft ResearchSpeech.Net

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    Pruning Method

    (k) (k ) (k+1)

    0 parser 0 parser 0 parser

    p parser op

    op

    p parser op p parser op

    p+1 parser p+1 parser p+1 parser

    P_k parser P_k parser P_k parser

    k+1 predict. k+1 predict.

    k+1 predict.

    k+1 predict.

    k+1 predict.

    k+1 predict.

    k+1 predict.

    k+1 predict.

    k+1 predict.

    k+1 predict.P_k+1parserP_k+1parser

    word predictor

    and tagger

    parser adjoin/unary transitions

    null parser transitions

    op

    k predict.

    k predict.

    k predict.

    k predict.

    op

    Microsoft ResearchSpeech.Net

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