this paper, we focus on the translation modeling problem. Before we turn to this problem, however, we should address an issue that may be a concern to some readers: Why do we estimate Pr(e) and Pr(fle) rather than estimate Pr(elf ) directly? We are really interested in this latter probability. Wouldn't we reduce our problems from three to two by this direct approach? If we can estimate Pr(fle) adequately, why can't we just turn the whole process around to estimate Pr(elf)? To understand this, imagine that we divide French and English strings into those that are well-formed and those that are ill-formed. This is not a precise notion. We have in mind that strings like Il va la bibliothque, or I live in a house, or even Colorless green ideas sleep furiously are well-formed, but that strings like lava I1 bibliothque or a I in live house are not. When we translate a French string into English, we can think of ourselves as springing from a well-formed French string into the sea of well-formed English strings with the hope of landing on a good one. It is important, therefore, that our model for Pr(elf ) concentrate its probability as much as possible on wellformed English strings. But it is not important that our model for Pr(fle ) concentrate its probability on well-formed French strings. If we were to reduce the probability of all well-formed French strings by the same factor, spreading the probability thus 265 liberated over ill-formed French strings, there would be no effect on our translations: the argument that maximizes some function f(x) also maximizes cf(x) for any positive constant c. As we shall see below, our translation models are prodigal, spraying probability all over the place, most of it on ill-formed French strings. In fact, as we discuss in Section 4.5, two...

this paper, we will describe a simple rule-based approach to automated learning of linguistic knowledge. This approach has been shown for a number of tasks to capture information in a clearer and more direct fashion without a compromise in performance. We present a detailed case study of this learning method applied to part of speech tagging

This paper presents an unsupervised learning algorithm for sense disambiguation that, when trained on unannotated English text, rivals the performance of supervised techniques that require time-consuming hand annotations. The algorithm is based on two powerful constraints -- that words tend to have one sense per discourse and one sense per collocation -- exploited in an iterative bootstrapping procedure. Tested accuracy exceeds 96%.

This paper describes a program that disambiguates English word senses in unrestricted text using statistical models of the major Roget's Thesaurus categories. Roget's categories serve as approximations of conceptual classes. The categories listed for a word in Roget's index tend to correspond to sense distinctions; thus selecting the most likely category provides a useful level of sense disambiguation. The selection of categories is accomplished by identifying and weighting words that are indicative of each category when seen in context, using a Bayesian theoretical framework. Other

We present a syntax-based statistical translation model. Our model transforms a source-language parse tree into a target-language string by applying stochastic operations at each node. These operations capture linguistic differences such as word order and case marking. Model parameters are estimated in polynomial time using an EM algorithm. The model produces word alignments that are better than those produced by IBM Model 5. 1

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This paper presents a new approach for resolving lexical ambiguities in one language using statistical data from a monolingual corpus of another language. This approach exploits the differences between mappings of words to senses in different languages. The paper concentrates on the problem of target word selection in machine translation, for which the approach is directly applicable. The presented algorithm identifies syntactic relationships between words, using a source language parser, and maps the alternative interpretations of these relationships to the target language, using a bilingual lexicon. The preferred senses are then selected according to statistics on lexical relations in the target language. The selection is based on a statistical model and on a constraint propagation algorithm, which handles simultaneously all ambiguities in the sentence. The method was evaluated using three sets of Hebrew and German examples and was found to be very useful for disambiguation. The paper includes a detailed comparative analysis of statistical sense disambiguation methods. 1. Introduction The resolution of lexical ambiguities in non-restricted text is one of the most difficult tasks of natural language processing. A related task in machine translation, on which we focus in this paper, is target word selection. This is the task of deciding which target language word is the most appropriate equivalent of a source language word in context. In addition to the alternatives introduced by the different word senses of the source language word, the target language may specify additional alternatives that differ mainly in their usage. Traditionally several linguistic levels were used to deal with this problem: syntactic, semantic and pragmatic. Computationally the syntactic methods...

This paper presents a statistical decision procedure for lexical ambiguity resolution. The algorithm exploits both local syntactic patterns and more distant collocational evidence, generating an efficient, effective, and highly perspicuous recipe for resolving a given ambiguity. By identifying and utilizing only the single best disambiguating evidence in a target context, the algorithm avoids the problematic complex modeling of statistical dependencies. Although directly applicable to a wide class of ambiguities, the algorithm is described and evaluated in a realistic case study, the problem of restoring missing accents in Spanish and French text. Current accuracy exceeds 99% on the full task, and typically is over 90% for even the most difficult ambiguities.

The representation of documents and queries as vectors in a high-dimensional space is well-established in information retrieval [1]. This paper proposes to represent the semantics of words and contexts in a text as vectors. The dimensions of the space are words and the initial vectors are determined by the words occurring close to the entity to be represented which implies that the space has several thousand dimensions (words). This makes the vector representations (which are dense) too cumbersome to use directly. Therefore, dimensionality reduction by means of a singular value decomposition is employed. The paper analyzes the structure of the vector representations and applies them to word sense disambiguation and thesaurus induction.