Algorithmic complexity

A complex relation is any nary relation in which some of the arguments may be be unspeciﬁed. We present here a simple twostage method for extracting complex relations between named entities in text. The ﬁrst stage creates a graph from pairs of entities that are likely to be related, and the second stage scores maximal cliques in that graph as potential complex relation instances. We evaluate the new method against a standard baseline for extracting genomic variation relations from biomedical text. ing named entities.
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G r a m m a r formalisms based on the encoding of grammatical information in complexvalued feature systems enjoy some currency both in linguistics and naturallanguageprocessing research. Such formalisms can be thought of by analogy to contextfree grammars as generalizing the notion of nonterminal symbol from a finite domain of atomic elements to a possibly infinite domain of directed graph structures nf a certain sort.
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When students at MIT competed against each other in the first realtime graphical computer game Spacewar in 1962 (Graetz 1981), probably none of them could have dreamt how realistic and complex computer games would develop to be in four decades and how large a business would grow around them. Commercial arcade games such as Pong and Space Invaders arrived in the 1970s, and home computers brought computer games within the reach of all enthusiasts in the 1980s.
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Science arises from the very human desire to understand and control the world. Over the course of history, we humans have gradually built up a grand edifice of knowledge that enables us to predict, to varying extents, the weather, the motions of the planets, solar and lunar eclipses, the courses of diseases, the rise and fall of economic growth, the stages of language development in children, and a vast panorama of other natural, social, and cultural phenomena. More recently we have even come to understand some fundamental limits to our abilities to predict.
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This book is inspired by boredom and fascination: boredom with the usual presentation of data structures and algorithms, and fascination with complex systems. The problem with data structures is that they are often taught without a motivating context; the problem with complexity science is that it is usually not taught at all. In 2005 I developed a new class at Olin College where students read about topics in complexity, implement experiments in Python, and learn about algorithms and data structures. ...
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The publication of the CooleyTukey fast Fourier transform (FFT) algorithm in 1965 has opened a new area in digital signal processing by reducing the order of complexity of some crucial computational tasks like Fourier transform and convultion from N 2 to N log 2 , where N is the problem size. The development of the major algorithms (CooleyTukey and splitradix FFT, prime factor algorithm and Winograd fast Fourier transform) is reviewed. Then, an attempt is made to indicate the state of the art on the subject, showin the standing of researh, open problems and implementations....
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We describe several tabular algorithms for Tree Adjoining G r a m m a r parsing, creating a continuum from simple pure bottomup algorithms to complex predictive algorithms and showing what transformations must be applied to each one in order to obtain the next one in the continuum.
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It is by now well established that quantum machines can solve certain computational problems much faster than the best algorithms known in the standard Turing machine model. The complexity question of which problems can be feasibly computed by quantum machines has also been extensively investigated in recent years, both in the context of one machine models (quantum polynomial classes) and various flavors of multimachine models (single and multiple prover quantum interactive proofs).
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In this paper we investigate how much data is required to train an algorithm for attribute selection, a subtask of Referring Expressions Generation (REG). To enable comparison between differentsized training sets, a systematic training method was developed. The results show that depending on the complexity of the domain, training on 10 to 20 items may already lead to a good performance.
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The paper demonstrates that exponential complexities with respect to grammar size and input length have little impact on the performance of three unificationbased parsing algorithms, using a widecoverage grammar. The results imply that the study and optimisation of unificationbased parsing must rely on empirical data until complexity theory can more accurately predict the practical behaviour of such parserQ. 1.
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Drawing appropriate defeasible inferences has been proven to be one of the most pervasive puzzles of natural language processing and a recurrent problem in pragmatics. This paper provides a theoretical framework, called stratified logic, that can accommodate defeasible pragmatic inferences. The framework yields an algorithm that computes the conversational, conventional, scalar, clausal, and normal state implicatures; and the presuppositions that are associated with utterances. The algorithm applies equally to simple and complex utterances and sequences of utterances. ...
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An important goal of computational linguistics has been to use linguistic theory to guide the construction of computationally efficient realworld natural language processing systems. At first glance, generalized phrase structure grammar (GPSG) appears to be a blessing on two counts. First, the precise formalisms of GPSG might be a direct and fransparent guide for parser design and implementation. Second, since GPSG has weak contextfree generative power and contextfree languages can be parsed in O(n ~) by a wide range of algorithms, GPSG parsers would appear to run in polynomial time.
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Referring expressions and other object descriptions should be maximal under the Local Brevity, No Unnecessary Components, and Lexical Preference preference rules; otherwise, they may lead hearers to infer unwanted conversational implicatures. These preference rules can be incorporated into a polynomial time generation algorithm, while some alternative formalizations of conversational impficature make the generation task NPHard. and avoid utterance (lb). Incorrect conversational implicatures may also arise from inappropriate attributive (informational) descriptions. ...
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In this paper we study a set of problems that are of considerable importance to Statistical Machine Translation (SMT) but which have not been addressed satisfactorily by the SMT research community. Over the last decade, a variety of SMT algorithms have been built and empirically tested whereas little is known about the computational complexity of some of the fundamental problems of SMT. Our work aims at providing useful insights into the the computational complexity of those problems.
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Lecture Data Structures & Algorithms: Chapter 3 (Searching Techniques prens) presented linear (sequential) search, binary search, complexity of algorithms.
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In computational complexity theory, PSPACE is the set of all decision problems that can be solved by a Turing machine using a polynomial amount of space. In this chapter will introduce PSPACE with a number of content: PSPACE complexity class, quantified satisfiability, planning problem, PSPACEcomplete.
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The course focuses on strategies and techniques to efficiently store data (Data Structures) and to perform processing on such data in efficient ways (Algorithms), as well as on the analysis and design of such techniques. In this lecture, the following topics will be covered: Mathematical review; asymptotic and algorithm analysis; relationships and data structures; requential storage: Lists, queues, stacks, deques; hash tables; trees; priority queues and heaps; sort algorithms; graphs and graph algorithms; algorithm design techniques; complexity classes and NP completeness.
62p allbymyself_08 22022016 4 1 Download

Lecture Discrete mathematics and its applications (7/e) – Chapter 3: Algorithms. This chapter presents the following content: Algorithms, example algorithms, algorithmic paradigms, growth of functions, bigo and other notation, complexity of algorithms.
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Machine learning techniques have the potential of alleviating the complexity of knowledge acquisition. This book presents today’s state and development tendencies of machine learning. It is a multiauthor book. Taking into account the large amount of knowledge about machine learning and practice presented in the book, it is divided into three major parts: Introduction, Machine Learning Theory and Applications. Part I focuses on the introduction to machine learning.
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For the past several years mathematics majors in the computing track at the University of Pennsylvania have taken a course in continuous algorithms (numerical analysis) in the junior year, and in discrete algorithms in the senior year. This book has grown out of the senior course as I have been teaching it recently. It has also been tried out on a large class of computer science and mathematics majors, including seniors and graduate students, with good results.
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