320 research outputs found
An Efficient Probabilistic Context-Free Parsing Algorithm that Computes Prefix Probabilities
We describe an extension of Earley's parser for stochastic context-free
grammars that computes the following quantities given a stochastic context-free
grammar and an input string: a) probabilities of successive prefixes being
generated by the grammar; b) probabilities of substrings being generated by the
nonterminals, including the entire string being generated by the grammar; c)
most likely (Viterbi) parse of the string; d) posterior expected number of
applications of each grammar production, as required for reestimating rule
probabilities. (a) and (b) are computed incrementally in a single left-to-right
pass over the input. Our algorithm compares favorably to standard bottom-up
parsing methods for SCFGs in that it works efficiently on sparse grammars by
making use of Earley's top-down control structure. It can process any
context-free rule format without conversion to some normal form, and combines
computations for (a) through (d) in a single algorithm. Finally, the algorithm
has simple extensions for processing partially bracketed inputs, and for
finding partial parses and their likelihoods on ungrammatical inputs.Comment: 45 pages. Slightly shortened version to appear in Computational
Linguistics 2
Precise n-gram Probabilities from Stochastic Context-free Grammars
We present an algorithm for computing n-gram probabilities from stochastic
context-free grammars, a procedure that can alleviate some of the standard
problems associated with n-grams (estimation from sparse data, lack of
linguistic structure, among others). The method operates via the computation of
substring expectations, which in turn is accomplished by solving systems of
linear equations derived from the grammar. We discuss efficient implementation
of the algorithm and report our practical experience with it.Comment: 12 pages, to appear in ACL-9
Comparing Human and Machine Errors in Conversational Speech Transcription
Recent work in automatic recognition of conversational telephone speech (CTS)
has achieved accuracy levels comparable to human transcribers, although there
is some debate how to precisely quantify human performance on this task, using
the NIST 2000 CTS evaluation set. This raises the question what systematic
differences, if any, may be found differentiating human from machine
transcription errors. In this paper we approach this question by comparing the
output of our most accurate CTS recognition system to that of a standard speech
transcription vendor pipeline. We find that the most frequent substitution,
deletion and insertion error types of both outputs show a high degree of
overlap. The only notable exception is that the automatic recognizer tends to
confuse filled pauses ("uh") and backchannel acknowledgments ("uhhuh"). Humans
tend not to make this error, presumably due to the distinctive and opposing
pragmatic functions attached to these words. Furthermore, we quantify the
correlation between human and machine errors at the speaker level, and
investigate the effect of speaker overlap between training and test data.
Finally, we report on an informal "Turing test" asking humans to discriminate
between automatic and human transcription error cases
Processing Unification-based Grammars in a Connectionist Framework
We present an approach to the processing of unification-based grammars in the connectionist paradigm. The method involves two basic steps: (1) Translation of a grammar's rules into a set of structure fragments, and (2) encoding these fragments in a connectionist network such that unification and rule application can take place by spreading activation. Feature structures are used to constrain sentence generation by semantic and/or grammatical properties. The method incorporates a general model of unification in connectionist networks
Processing Unification-based Grammars in a Connectionist Framework
We present an approach to the processing of unification-based grammars in the connectionist paradigm. The method involves two basic steps: (1) Translation of a grammar's rules into a set of structure fragments, and (2) encoding these fragments in a connectionist network such that unification and rule application can take place by spreading activation. Feature structures are used to constrain sentence generation by semantic and/or grammatical properties. The method incorporates a general model of unification in connectionist networks. INTRODUCTION In recent years connectionist models have achieved notable results in modeling various aspect of perception and cognition. Although natural language processing has not been among the most prominent of its applications, there are a fair number of connectionist models of both language analysis and generation (Charniak & Santos, 1987; Cottrell, 1985; Dell, 1985; Fanty, 1985; Gasser, 1988; Kalita & Shastri, 1987; McClelland & Kawamoto, 1986). Ho..
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