We discuss the relevance of k-best parsing to recent applications in natural language processing, and develop efficient algorithms for k-best trees in the framework of hypergraph parsing. To demonstrate the efficiency, scalability and accuracy of these algorithms, we present experiments on Bikel’s implementation of Collins ’ lexicalized PCFG model, and on Chiang’s CFG-based decoder for hierarchical phrase-based translation. We show in particular how the improved output of our algorithms has the potential to improve results from parse reranking systems and other applications. 1

We define general algebraic frameworks for shortest-distance problems based on the structure of semirings. We give a generic algorithm for finding single-source shortest distances in a weighted directed graph when the weights satisfy the conditions of our general semiring framework. The same algorithm can be used to solve efficiently classical shortest paths problems or to find the k-shortest distances in a directed graph. It can be used to solve single-source shortest-distance problems in weighted directed acyclic graphs over any semiring. We examine several semirings and describe some specific instances of our generic algorithms to illustrate their use and compare them with existing methods and algorithms. The proof of the soundness of all algorithms is given in detail, including their pseudocode and a full analysis of their running time complexity.

A syntax-directed translator first parses the source-language input into a parsetree, and then recursively converts the tree into a string in the target-language. We model this conversion by an extended treeto-string transducer that have multi-level trees on the source-side, which gives our system more expressive power and flexibility. We also define a direct probability model and use a linear-time dynamic programming algorithm to search for the best derivation. The model is then extended to the general log-linear framework in order to rescore with other features like n-gram language models. We devise a simple-yet-effective algorithm to generate non-duplicate k-best translations for n-gram rescoring. Initial experimental results on English-to-Chinese translation are presented. 1

In this paper we present the RWTH FSA toolkit -- an efficient implementation of algorithms for creating and manipulating weighted finite-state automata. The toolkit has been designed using the principle of on-demand computation and offers a large range of widely used algorithms. To prove the superior efficiency of the toolkit, we compare the implementation to that of other publically available toolkits. We also show that on-demand computations help to reduce memory requirements significantly without any loss in speed. To increase its flexibility, the RWTH FSA toolkit supports high-level interfaces to the programming language Python as well as a command-line tool for interactive manipulation of FSAs. Furthermore, we show how to utilize the toolkit to rapidly build a fast and accurate statistical machine translation system. Future extensibility of the toolkit is ensured as it will be publically available as open source software.

Dynamic Programming (DP) is an important class of algorithms widely used in many areas of speech and language processing. Recently there have been a series of work trying to formalize many instances of DP algorithms under algebraic and graph-theoretic frameworks. This tutorial surveys two such frameworks, namely semirings and directed hypergraphs, and draws connections between them. We formalize two particular types of DP algorithms under each of these frameworks: the Viterbi-style topological algorithms and the Dijkstra-style best-first algorithms. Wherever relevant, we also discuss typical applications of these algorithms in Natural Language Processing. 1

Morphological analysis of a wide range of languages can be implemented efficiently using finite-state transducer technologies. Over the last 30 years, a number of attempts have been made to create tools for computational morphologies. The two main competing approaches have been parallel vs. cascaded rule application. The parallel rule application was originally introduced by Koskenniemi [1983] and implemented in tools like TwolC and LexC. Currently many applications of morphologies could use dictionaries encoding the a priori likelihoods of words and expressions as well as the likelihood of relations to other representations or languages. We have made the choice to create open-source tools and language descriptions in order to let as many as possible participate in the effort. The current article presents some of the main tools that we have created such as HFST-LexC, HFST-TwolC and HFST-Compose-Intersect. We evaluate their efficiency in comparison to some similar tools and libraries. In particular, we evaluate them using several full-fledged morphological descriptions. Our tools compare well with similar open source tools, even if we still have some challenges ahead before we can catch up with the commercial tools. We demonstrate that for various reasons a parallel rule approach still seems to be more efficient than a cascaded rule approach when developing finite-state morphologies. 1

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