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We can recover j from g(j) as follows : j, = s,j jk = jk+ 1 + gki mod 2 which gives jn-1 = gnj + gLl jnm2 = g,j + gi-l + gi-2,***. Thus On the right side of p. 505, the fifth and sixth line from the bottom, the lower error exponent E-(R) is valid for the 1 output and the upper error exponent i? (R) for the 0 output. ACKNOWLEDGMENT The authors want to thank(More)
In this paper, we present a statistical approach to machine translation. We describe the application of our approach to translation from French to English and give preliminary results. The field of machine translation is almost as old as the modern digital computer. In 1949 Warren Weaver suggested that the problem be attacked with statistical methods and(More)
Speech recognition is formulated as a problem of maximum likelihood decoding. This formulation requires statistical models of the speech production process. In this paper, we describe a number of statistical models for use in speech recognition. We give special attention to determining the parameters for such models from sparse data. We also describe two(More)
This paper presents an attempt at using the syntactic structure in natural language for improved language models for speech recognition. The structured language model merges techniques in automatic parsing and language modeling using an original probabilistic parameterization of a shift-reduce parser. A maximum likelihood re-estimation procedure belonging(More)
The paper presents a language model that develops syntactic structure and uses it to extract meaningful information from the word history, thus enabling the use of long distance dependencies. The model assigns probability to every joint sequence of words–binary-parse-structure with headword annotation and operates in a left-to-right manner — therefore(More)
An approach to automatic translation is outlined that utilizes technklues of statistical inl'ormatiml extraction from large data bases. The method is based on the availability of pairs of large corresponding texts that are translations of each other. In our case, the iexts are in English and French. Fundamental to the technique is a complex glossary of(More)
Speech recognition language models are based on probabilities P(Wk+I = v [ WlW2~..., Wk) that the next word Wk+l will be any particular word v of the vocabulary, given that the word sequence Wl, w2,..., Wk is hypothesized to have been uttered in the past. If probabilistic context-free grammars are to be used as the basis of the language model, it will be(More)
We describe a generative probabilistic model of natural language , which we call HBG, that takes advantage of detailed linguistic information to resolve ambiguity. HBG incorporates lexical, syntactic, semantic, and structural information from the parse tree into the disambiguation process in a novel way. We use a corpus of bracketed sentences, called a(More)
This group works towards automatic transcription of continuous speech with a vocabulary and syntax as unrestricted as possible. It is a long-term effort; however, an experimental system is operational. The acoustic processor contains a spectrum analyzer based on the Fast Fourier Transform and a phone segmenter/recognizer which makes use of transitional and(More)
The problem of quantitatively comparing tile performance of different broad-coverage grammars of En-glish has to date resisted solution. Prima facie, known English grammars appear to disagree strongly with each other as to the elements of even tile simplest sentences. For instance, the grammars of Steve Abneying), Don tfindle (AT&T), Bob Ingria (BBN), and(More)