1473
Similarity-Based Models of Word Cooccurrence Probabilities
In many applications of natural language processing (NLP) it is necessary to determine the likelihood of a given word combination. For example, a speech recognizer may need to determine which of the two word combinations “eat a peach” and ”eat a beach” is more likely. Statistical NLP methods determine the likelihood of a word combination from its frequency in a training corpus. However, the nature of language is such that many word combinations are infrequent and do not occur in any given corpus. In this work we propose a method for estimating the probability of such previously unseen word combinations using available information on “most similar” words.We describe probabilistic word association models based on distributional word similarity, and apply them to two tasks, language modeling and pseudo-word disambiguation. In the language modeling task, a similarity-based model is used to improve probability estimates for unseen bigrams in a back-off language model. The similarity-based method yields a 20% perplexity improvement in the prediction of unseen bigrams and statistically significant reductions in speech-recognition error.We also compare four similarity-based estimation methods against back-off and maximum-likelihood estimation methods on a pseudo-word sense disambiguation task in which we controlled for both unigram and bigram frequency to avoid giving too much weight to easy-to-disambiguate high-frequency configurations. The similarity-based methods perform up to 40% better on this particular task.
312731
Mostly-Unsupervised Statistical Segmentation of Japanese: Applications to Kanji
Given the lack of word delimiters in written Japanese, word segmentation is generally considered a crucial first step in processing Japanese texts. Typical Japanese segmentation algorithms rely either on a lexicon and grammar or on pre-segmented data. In contrast, we introduce a novel statistical method utilizing unsegmented training data, with performance on kanji sequences comparable to and sometimes surpassing that of morphological analyzers over a variety of error metrics.
6305097
Measures of Distributional Similarity
We study distributional similarity measures for the purpose of improving probability estimation for unseen cooccurrences. Our contributions are three-fold: an empirical comparison of a broad range of measures; a classification of similarity functions based on the information that they incorporate; and the introduction of a novel function that is superior at evaluating potential proxy distributions.
1243491
Fast context-free grammar parsing requires fast boolean matrix multiplication
In 1975, Valiant showed that Boolean matrix multiplication can be used for parsing context-free grammars (CFGs), yielding the asympotically fastest (although not practical) CFG parsing algorithm known. We prove a dual result: any CFG parser with time complexity <i>O</i>(<i>gn</i><sup>3-∈</sup>), where <i>g</i> is the size of the grammar and <i>n</i> is the length of the input string, can be efficiently converted into an algorithm to multiply <i>m</i> × <i>m</i> Boolean matrices in time <i>O</i>(<i>m</i><sup>3-∈/3</sup>). Given that practical, substantially subcubic Boolean matrix multiplication algorithms have been quite difficult to find, we thus explain why there has been little progress in developing practical, substantially subcubic general CFG parsers. In proving this result, we also develop a formalization of the notion of parsing.
2032603
Similarity-Based Approaches to Natural Language Processing
Statistical methods for automatically extracting information about associations between words or documents from large collections of text have the potential to have considerable impact in a number of areas, such as information retrieval and natural-language-based user interfaces. However, even huge bodies of text yield highly unreliable estimates of the probability of relatively common events, and, in fact, perfectly reasonable events may not occur in the training data at all. This is known as the sparse data problem. Traditional approaches to the sparse data problem use crude approximations. We propose a different solution: if we are able to organize the data into classes of similar events, then, if information about an event is lacking, we can estimate its behavior from information about similar events. This thesis presents two such similarity-based approaches, where, in general, we measure similarity by the Kullback-Leibler divergence, an information-theoretic quantity. 
Our first approach is to build soft, hierarchical clusters: soft, because each event belongs to each cluster with some probability; hierarchical, because cluster centroids are iteratively split to model finer distinctions. Our clustering method, which uses the technique of deterministic annealing, represents (to our knowledge) the first application of soft clustering to problems in natural language processing. We use this method to cluster words drawn from 44 million words of Associated Press Newswire and 10 million words from Grolier's encyclopedia, and find that language models built from the clusters have substantial predictive power. Our algorithm also extends with no modification to other domains, such as document clustering. 
Our second approach is a nearest-neighbor approach: instead of calculating a centroid for each class, we in essence build a cluster around each word. We compare several such nearest-neighbor approaches on a word sense disambiguation task and find that as a whole, their performance is far superior to that of standard methods. In another set of experiments, we show that using estimation techniques based on the nearest-neighbor model enables us to achieve perplexity reductions of more than 20 percent over standard techniques in the prediction of low-frequency events, and statistically significant speech recognition error-rate reduction.
7521453
Bootstrapping Lexical Choice via Multiple-Sequence Alignment
An important component of any generation system is the mapping dictionary, a lexicon of elementary semantic expressions and corresponding natural language realizations. Typically, labor-intensive knowledge-based methods are used to construct the dictionary. We instead propose to acquire it automatically via a novel multiple-pass algorithm employing multiple-sequence alignment, a technique commonly used in bioinformatics. Crucially, our method lever-ages latent information contained in multi-parallel corpora --- datasets that supply several verbalizations of the corresponding semantics rather than just one.We used our techniques to generate natural language versions of computer-generated mathematical proofs, with good results on both a per-component and overall-output basis. For example, in evaluations involving a dozen human judges, our system produced output whose readability and faithfulness to the semantic input rivaled that of a traditional generation system.
6016075
Concept-based component libraries and optimizing compilers
This paper discusses concept lattices and some of their applications in component library development and compiler optimizations. Ongoing work on concept-based user-extensible simplification (type-based optimizer generators) and library transformations is first reviewed, then a new application--categorization of algorithms via precise descriptions of their performance characteristics--is outlined, with Standard Template Library and Boost Graph Library algorithm concept lattices as example.
44673545
Generic programming for high-performance scientific applications
We present case studies that apply generic programming to the development of high-performance parallel code for solving two archetypal partial differential equations (PDEs). We examine the overall structure of the example scientific codes and consider their generic implementation. With a generic approach it is a straightforward matter to reuse software components from different sources; implementations with components from the Iterative Template Library (ITL), the Matrix Template Library (MTL), Blitz++, A++/P++, and Fortran BLAS are presented. Our newly developed Generic Message Passing library is used for communication. We compare the generic implementations with equivalent implementations developed with alternative libraries and languages and discuss performance as well as software engineering issues. Copyright © 2005 John Wiley & Sons, Ltd.
1094026
Generic Graph Algorithms for Sparse Matrix Ordering
Fill-reducing sparse matrix orderings have been a topic of active research for many years. Although most such algorithms are developed and analyzed within a graph-theoretical framework, for reasons of performance the corresponding implementations are typically realized with programming languages devoid of language features necessary to explicitly represent graph abstractions. Recently, generic programming has emerged as a programming paradigm capable of providing high levels of performance in the presence of programming abstractions. In this paper we present an implementation of the Minimum Degree ordering algorithm using the newly-developed Generic Graph Component Library. Experimental comparisons show that, despite our heavy use of abstractions, our implementation has performance indistinguishable from that of a widely used Fortran implementation.
52861235
On bounding the delay of a critical path
35337480
On generating tests to cover diverse worst-case timing corners
With process variations, timing behavior may vary from chip to chip. This paper investigates the problem of generating test patterns to cover potentially diverse worst-case timing corners. We focus the work on a specific problem formulation where the delay of a path can be affected by k aggressors. We demonstrate that the search space for such a problem can be quite complex. We study various methods to guide the test generation. We show that with different chips having different worst-case corners, it may not be affordable to search for the tests to expose all these corners. Experimental results are presented to explain the problem formulation, the test generation methods, and the limitation on what we can achieve for solving the problem.