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- Peter F. Brown, Stephen Della Pietra, Vincent J. Della Pietra, Robert L. Mercer
- Computational Linguistics
- 1993

We describe a series o,f five statistical models o,f the translation process and give algorithms,for estimating the parameters o,f these models given a set o,f pairs o,f sentences that are translations o,f one another. We define a concept o,f word-byword alignment between such pairs o,f sentences. For any given pair of such sentences each o,f our models… (More)

- Adam L. Berger, Stephen Della Pietra, Vincent J. Della Pietra
- Computational Linguistics
- 1996

The concept of maximum entropy can be traced back along multiple threads to Biblical times. Only recently, however, have computers become powerful enough to permit the widescale application of this concept to real world problems in statistical estimation and pattern recognition. In this paper, we describe a method for statistical modeling based on maximum… (More)

- Peter F. Brown, John Cocke, +5 authors Paul S. Roossin
- Computational Linguistics
- 1990

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)

- Stephen Della Pietra, Vincent J. Della Pietra, John D. Lafferty
- IEEE Trans. Pattern Anal. Mach. Intell.
- 1997

—We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the Kullback-Leibler divergence between the model and the… (More)

- Peter F. Brown, Stephen Della Pietra, Vincent J. Della Pietra, Jennifer C. Lai, Robert L. Mercer
- Computational Linguistics
- 1992

We present an estimate of an upper bound of 1.75 bits for the entropy of characters in printed English, obtained by constructing a word trigram model and then computing the cross-entropy between this model and a balanced sample of English text. We suggest the well-known and widely available Brown Corpus of printed English as a standard against which to… (More)

- Peter F. Brown, John Cocke, +4 authors Paul S. Roossin
- COLING
- 1988

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)

We describe a statistical technique for assigning senses to words. An instance of a word is assigned a sense by asking a question about the context in which the word appears. The question is constructed to have high mutual information with the translation of that instance in another language. When we incorporated this method of assigning senses into our… (More)

- Adam L. Berger, Peter F. Brown, +6 authors Lubos Ures
- HLT
- 1994

We present an overview of Candide, a system for automatic translation of French text to English text. Candide uses methods of information theory and statistics to develop a probability model of the translation process. This model, which is made to accord as closely as possible with a large body of French and English sentence pairs, is then used to generate… (More)

- Peter F. Brown, S. F. Chen, Stephen Della Pietra, Vincent J. Della Pietra, A. S. Kehler, Robert L. Mercer
- Computer Speech & Language
- 1994

We reinterpret the system described by Brown et al. [1] in terms of the analysis-transfer-synthesis paradigm common in machine translation. We describe enhanced analysis and synthesis components that apply a number of simple linguistic transformations so the transfer component operates from a string of French morphemes to a string of English morphemes. We… (More)