Manhattan GMAT Guide 3 Word Problems covers rates & work, statistics, overlapping sets, & more teaches problem solving & data sufficiency strategies includes practice problems with detailed explanations updated for the official guide for GMAT.
Are you mystified by math word problems? This easy-to-understand guide shows you how to conquer these tricky questions with a step-by-step plan for finding the right solution each and every time, no matter the kind or level of problem. From learning math lingo and performing operations to calculating formulas and writing equations, you'll get all the skills you need to succeed!
Even those who ﬁnd algebra easy are stumped by word problems (also
called ‘‘applications’’). In this book, word problems are treated very care-
fully. Two important skills needed to solve word problems are discussed
earlier than the word problems themselves. First, you will learn how to
ﬁnd quantitative relationships in word problems and how to represent
them using variables. Second, you will learn how to represent multiple quan-
tities using only one variable.
Includes the latest information on security problems in Word and how to prevent them.
Offers tips for formatting for black and white versus color printers
Explains when to use Word for publishing to the Web and when to depend on FrontPage.
Large vocabulary speech recognition systems fail to recognize words beyond their vocabulary, many of which are information rich terms, like named entities or foreign words. Hybrid word/sub-word systems solve this problem by adding sub-word units to large vocabulary word based systems; new words can then be represented by combinations of subword units. Previous work heuristically created the sub-word lexicon from phonetic representations of text using simple statistics to select common phone sequences. ...
We cast the word alignment problem as maximizing a submodular function under matroid constraints. Our framework is able to express complex interactions between alignment components while remaining computationally efﬁcient, thanks to the power and generality of submodular functions. We show that submodularity naturally arises when modeling word fertility. Experiments on the English-French Hansards alignment task show that our approach achieves lower alignment error rates compared to conventional matching based approaches. ...
Word alignment has an exponentially large search space, which often makes exact inference infeasible. Recent studies have shown that inversion transduction grammars are reasonable constraints for word alignment, and that the constrained space could be efﬁciently searched using synchronous parsing algorithms. However, spurious ambiguity may occur in synchronous parsing and cause problems in both search efﬁciency and accuracy. In this paper, we conduct a detailed study of the causes of spurious ambiguity and how it effects parsing and discriminative learning. ...
Hierarchical phrase-based models are attractive because they provide a consistent framework within which to characterize both local and long-distance reorderings, but they also make it dif cult to distinguish many implausible reorderings from those that are linguistically plausible. Rather than appealing to annotationdriven syntactic modeling, we address this problem by observing the in uential role of function words in determining syntactic structure, and introducing soft constraints on function word relationships as part of a standard log-linear hierarchical phrase-based model. ...
An efﬁcient decoding algorithm is a crucial element of any statistical machine translation system. Some researchers have noted certain similarities between SMT decoding and the famous Traveling Salesman Problem; in particular (Knight, 1999) has shown that any TSP instance can be mapped to a sub-case of a word-based SMT model, demonstrating NP-hardness of the decoding task. In this paper, we focus on the reverse mapping, showing that any phrase-based SMT decoding problem can be directly reformulated as a TSP.