Results for 'Probabilistic parsing'

957 found
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  1.  37
    (1 other version)Parsing and Hypergraphs.Dan Klein & Christopher D. Manning - unknown
    While symbolic parsers can be viewed as deduction systems, this view is less natural for probabilistic parsers. We present a view of parsing as directed hypergraph analysis which naturally covers both symbolic and probabilistic parsing. We illustrate the approach by showing how a dynamic extension of Dijkstra’s algorithm can be used to construct a probabilistic chart parser with an Ç´Ò¿µ time bound for arbitrary PCFGs, while preserving as much of the flexibility of symbolic chart parsers (...)
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  2.  84
    A Probabilistic Model of Semantic Plausibility in Sentence Processing.Ulrike Padó, Matthew W. Crocker & Frank Keller - 2009 - Cognitive Science 33 (5):794-838.
    Experimental research shows that human sentence processing uses information from different levels of linguistic analysis, for example, lexical and syntactic preferences as well as semantic plausibility. Existing computational models of human sentence processing, however, have focused primarily on lexico‐syntactic factors. Those models that do account for semantic plausibility effects lack a general model of human plausibility intuitions at the sentence level. Within a probabilistic framework, we propose a wide‐coverage model that both assigns thematic roles to verb–argument pairs and determines (...)
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  3.  32
    A Probabilistic Model of Lexical and Syntactic Access and Disambiguation.Daniel Jurafsky - 1996 - Cognitive Science 20 (2):137-194.
    The problems of access—retrieving linguistic structure from some mental grammar —and disambiguation—choosing among these structures to correctly parse ambiguous linguistic input—are fundamental to language understanding. The literature abounds with psychological results on lexical access, the access of idioms, syntactic rule access, parsing preferences, syntactic disambiguation, and the processing of garden‐path sentences. Unfortunately, it has been difficult to combine models which account for these results to build a general, uniform model of access and disambiguation at the lexical, idiomatic, and syntactic (...)
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  4.  21
    Optimizing Local Probability Models for Statistical Parsing.Mark Mitchell, Christopher D. Manning & Kristina Toutanova - unknown
    This paper studies the properties and performance of models for estimating local probability distributions which are used as components of larger probabilistic systems — history-based generative parsing models. We report experimental results showing that memory-based learning outperforms many commonly used methods for this task (Witten-Bell, Jelinek-Mercer with fixed weights, decision trees, and log-linear models). However, we can connect these results with the commonly used general class of deleted interpolation models by showing that certain types of memory-based learning, including (...)
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  5. (1 other version)Learning a Generative Probabilistic Grammar of Experience: A Process‐Level Model of Language Acquisition.Oren Kolodny, Arnon Lotem & Shimon Edelman - 2014 - Cognitive Science 38 (4):227-267.
    We introduce a set of biologically and computationally motivated design choices for modeling the learning of language, or of other types of sequential, hierarchically structured experience and behavior, and describe an implemented system that conforms to these choices and is capable of unsupervised learning from raw natural-language corpora. Given a stream of linguistic input, our model incrementally learns a grammar that captures its statistical patterns, which can then be used to parse or generate new data. The grammar constructed in this (...)
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  6.  54
    From Exemplar to Grammar: A Probabilistic Analogy‐Based Model of Language Learning.Rens Bod - 2009 - Cognitive Science 33 (5):752-793.
    While rules and exemplars are usually viewed as opposites, this paper argues that they form end points of the same distribution. By representing both rules and exemplars as (partial) trees, we can take into account the fluid middle ground between the two extremes. This insight is the starting point for a new theory of language learning that is based on the following idea: If a language learner does not know which phrase‐structure trees should be assigned to initial sentences, s/he allows (...)
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  7.  58
    (1 other version)An Ç ´Ò¿ µ Agenda-Based Chart Parser for Arbitrary Probabilistic Context-Free Grammars.Dan Klein & Christopher D. Manning - unknown
    While Ç ´Ò¿ µ methods for parsing probabilistic context-free grammars (PCFGs) are well known, a tabular parsing framework for arbitrary PCFGs which allows for botton-up, topdown, and other parsing strategies, has not yet been provided. This paper presents such an algorithm, and shows its correctness and advantages over prior work. The paper finishes by bringing out the connections between the algorithm and work on hypergraphs, which permits us to extend the presented Viterbi (best parse) algorithm to (...)
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  8.  72
    A note on the expressive power of probabilistic context free grammars.Gabriel Infante-Lopez & Maarten De Rijke - 2006 - Journal of Logic, Language and Information 15 (3):219-231.
    We examine the expressive power of probabilistic context free grammars (PCFGs), with a special focus on the use of probabilities as a mechanism for reducing ambiguity by filtering out unwanted parses. Probabilities in PCFGs induce an ordering relation among the set of trees that yield a given input sentence. PCFG parsers return the trees bearing the maximum probability for a given sentence, discarding all other possible trees. This mechanism is naturally viewed as a way of defining a new class (...)
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  9.  15
    A Note on the Expressive Power of Probabilistic Context Free Grammars.Gabriel Infante-Lopez & Maarten Rijke - 2006 - Journal of Logic, Language and Information 15 (3):219-231.
    We examine the expressive power of probabilistic context free grammars (PCFGs), with a special focus on the use of probabilities as a mechanism for reducing ambiguity by filtering out unwanted parses. Probabilities in PCFGs induce an ordering relation among the set of trees that yield a given input sentence. PCFG parsers return the trees bearing the maximum probability for a given sentence, discarding all other possible trees. This mechanism is naturally viewed as a way of defining a new class (...)
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  10.  36
    ベイジアンネットワーク推定による確率モデル遺伝的プログラミング.伊庭 斉志 長谷川 禎彦 - 2007 - Transactions of the Japanese Society for Artificial Intelligence 22 (1):37-47.
    Genetic Programming is a powerful optimization algorithm, which employs the crossover for genetic operation. Because the crossover operator in GP randomly selects sub-trees, the building blocks may be destroyed by the crossover. Recently, algorithms called PMBGPs based on probabilistic techniques have been proposed in order to improve the problem mentioned above. We propose a new PMBGP employing Bayesian network for generating new individuals with a special chromosome called expanded parse tree, which much reduces a number of possible symbols at (...)
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  11.  79
    A Model of Language Processing as Hierarchic Sequential Prediction.Marten van Schijndel, Andy Exley & William Schuler - 2013 - Topics in Cognitive Science 5 (3):522-540.
    Computational models of memory are often expressed as hierarchic sequence models, but the hierarchies in these models are typically fairly shallow, reflecting the tendency for memories of superordinate sequence states to become increasingly conflated. This article describes a broad-coverage probabilistic sentence processing model that uses a variant of a left-corner parsing strategy to flatten sentence processing operations in parsing into a similarly shallow hierarchy of learned sequences. The main result of this article is that a broad-coverage model (...)
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  12. Two Models of Minimalist, Incremental Syntactic Analysis.Edward P. Stabler - 2013 - Topics in Cognitive Science 5 (3):611-633.
    Minimalist grammars (MGs) and multiple context-free grammars (MCFGs) are weakly equivalent in the sense that they define the same languages, a large mildly context-sensitive class that properly includes context-free languages. But in addition, for each MG, there is an MCFG which is strongly equivalent in the sense that it defines the same language with isomorphic derivations. However, the structure-building rules of MGs but not MCFGs are defined in a way that generalizes across categories. Consequently, MGs can be exponentially more succinct (...)
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  13.  14
    Feature Selection for a Rich HPSG Grammar Using Decision Trees.Christopher D. Manning & Kristina Toutanova - unknown
    This paper examines feature selection for log linear models over rich constraint-based grammar (HPSG) representations by building decision trees over features in corresponding probabilistic context free grammars (PCFGs). We show that single decision trees do not make optimal use of the available information; constructed ensembles of decision trees based on different feature subspaces show signifi- cant performance gains (14% parse selection error reduction). We compare the performance of the learned PCFG grammars and log linear models over the same features.
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  14.  22
    Memory Versus Expectation: Processing Relative Clauses in a Flexible Word Order Language.Eszter Ronai & Ming Xiang - 2023 - Cognitive Science 47 (1):e13227.
    Memory limitations and probabilistic expectations are two key factors that have been posited to play a role in the incremental processing of natural language. Relative clauses (RCs) have long served as a key proving ground for such theories of language processing. Across three self-paced reading experiments, we test the online comprehension of Hungarian subject- and object-extracted RCs (SRCs and ORCs, respectively). We capitalize on the syntactic properties of Hungarian that allow for a variety of word orders within RCs, which (...)
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  15.  9
    How Likely Is it that I Would Act the Same Way: Modeling Moral Judgment During Uncertainty.Paul C. Bogdan, Sanda Dolcos & Florin Dolcos - 2024 - Cognitive Science 48 (11):e70010.
    Moral rules come with exceptions, and moral judgments come with uncertainty. For instance, stealing is wrong and generally punished. Yet, it could be the case that the thief is stealing food for their family. Such information about the thief's context could flip admonishment to praise. To varying degrees, this type of uncertainty regarding the context of another person's behavior is ever-present in moral judgment. Hence, we propose a model of how people evaluate others’ behavior: We argue that individuals principally judge (...)
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  16.  35
    Situated Language Understanding as Filtering Perceived Affordances.Peter Gorniak & Deb Roy - 2007 - Cognitive Science 31 (2):197-231.
    We introduce a computational theory of situated language understanding in which the meaning of words and utterances depends on the physical environment and the goals and plans of communication partners. According to the theory, concepts that ground linguistic meaning are neither internal nor external to language users, but instead span the objective‐subjective boundary. To model the possible interactions between subject and object, the theory relies on the notion of perceived affordances: structured units of interaction that can be used for prediction (...)
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  17.  81
    Introduction.Jon Williamson - 2006 - Journal of Logic, Language and Information 15 (1-2):1-3.
    The need for a coherent answer to this question has become increasingly urgent in the past few years, particularly in the field of artificial intelligence. There, both logical and probabilistic techniques are routinely applied in an attempt to solve complex problems such as parsing natural language and determining the way proteins fold. The hope is that some combination of logic and probability will produce better solutions. After all, both natural language and protein molecules have some structure that admits (...)
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  18.  53
    A Bayesian‐Network Approach to Lexical Disambiguation.Leila M. R. Eizirik, Valmir C. Barbosa & Sueli B. T. Mendes - 1993 - Cognitive Science 17 (2):257-283.
    Lexical ambiguity can be syntactic if it involves more than one grammatical category for a single word, or semantic if more than one meaning can be associated with a word. In this article we discuss the application of a Bayesian‐network model in the resolution of lexical ambiguities of both types. The network we propose comprises a parsing subnetwork, which can be constructed automatically for any context‐free grammar, and a subnetwork for semantic analysis, which, in the spirit of Fillmore's (1968) (...)
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  19.  19
    Modelling threshold phenomena in OWL: Metabolite concentrations as evidence for disorders.J. Hastings, L. Jansen, C. Steinbeck & S. Schulz - 2011 - In Michel Dumontier & Melanie Courtot, Proceedings of the 8th International Workshop on OWL: Experiences and Directions.
    While genomic and proteomic information describe the overall cellular machinery available to an organism, the metabolic profile of an individual at a given time provides a canvas as to the current physiological state. Concentration levels of relevant metabolites vary under different conditions, in particular, in the presence or absence of different disorders. Metabolite concentrations thus mediate an important link between chemistry and biology, contributing to a systems-wide understanding of biological processes and pathways. However, there are a number of challenges in (...)
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  20.  39
    Statistical models of syntax learning and use.Mark Johnson & Stefan Riezler - 2002 - Cognitive Science 26 (3):239-253.
    This paper shows how to define probability distributions over linguistically realistic syntactic structures in a way that permits us to define language learning and language comprehension as statistical problems. We demonstrate our approach using lexical‐functional grammar (LFG), but our approach generalizes to virtually any linguistic theory. Our probabilistic models are maximum entropy models. In this paper we concentrate on statistical inference procedures for learning the parameters that define these probability distributions. We point out some of the practical problems that (...)
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  21. ¿Puede el arte ser indefinido?: controversias sobre la definición del arte en la estética contemporánea y la propuesta de Arthur C. Danto.Verónica Parselis - 2008 - Sapientia 63 (223):143-158.
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  22.  11
    Honestidad y Otras Características Deseables Para El Desarrollo Tecnológico.Martín Parselis - 2018 - SCIO Revista de Filosofía 15:177-212.
    La literatura sobre las relaciones entre las personas y la técnica se refiere a análisis heredados de la economía, la sociología, la política, etc. Estas miradas abarcan a la técnica de forma global dificultando el estudio de algunos procesos que ocurren entre cada persona y la técnica. En este sentido, buscaremos explicitar estas relaciones a través de los productos de la técnica como mediadores sociales. Con esta base tienen sentido los criterios de las tecnologías entrañables para que esta mediación se (...)
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  23.  22
    Hector freytes, Antonio ledda, Giuseppe sergioli and.Roberto Giuntini & Probabilistic Logics in Quantum Computation - 2013 - In Hanne Andersen, Dennis Dieks, Wenceslao J. Gonzalez, Thomas Uebel & Gregory Wheeler, New Challenges to Philosophy of Science. Springer Verlag. pp. 49.
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  24. James H. Fetzer.Probabilistic Metaphysics - 1988 - In J. H. Fetzer, Probability and Causality: Essays in Honor of Wesley C. Salmon. D. Reidel. pp. 192--109.
  25.  57
    Mercenaries in Hellenistic Times G. T. Griffith : The Mercenaries of the Hellenistic World. Pp. x + 340. Cambridge: University Press, 1935. Cloth, 16s. [REVIEW]H. W. Parse - 1935 - The Classical Review 49 (04):136-.
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  26. Anne M. Fagot.Some Shortcomings of A. Probabilistic - 1984 - In Lennart Nordenfelt & B. Ingemar B. Lindahl, Health, Disease, and Causal Explanations in Medicine. Reidel. pp. 101.
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  27. Deductive parsing in Haskell.Jan van Eijck - unknown
    This paper contains the full code of an implementation in Haskell [2], in ‘literate programming’ style [3], of an approach to deductive parsing based on [4]. We focus on the case of the Earley [1] parsing algorithm for CF languages.
     
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  28.  29
    Parsing, Grammar, and the Challenge of Raising Children at LF.Julien Musolino & Andrea Gualmini - 2011 - In Edward Gibson & Neal J. Pearlmutter, The Processing and Acquisition of Reference. MIT Press. pp. 109.
    This chapter explores parsing and grammar in children, with an emphasis on how children resolve sentences with ambiguous scope. It focuses on an ambiguity involving the universal quantifier every in subject position along with a negated main predicate, as in the sentence “Every horse didn’t jump over the fence.” One interpretation of this sentence is the “surface-scope” interpretation, which views the expression every horse as a reference to all the horses in the set; thus, each horse in the set (...)
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  29. Deductive parsing with sequentially indexed grammars.Jan van Eijck - unknown
    This paper extends the Earley parsing algorithm for context free languages [3] to the case of sequentially indexed languages. Sequentially indexed languages are related to indexed languages [1, 2]. The difference is that parallel processing of index stacks is replaced by sequential processing [4].
     
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  30. Parsing and Presupposition in the Calculation of Local Contexts.Matthew Mandelkern & Jacopo Romoli - forthcoming - Semantics and Pragmatics.
    In this paper, we use antecedent-final conditionals to formulate two problems for parsing-based theories of presupposition projection and triviality of the kind given in Schlenker 2009. We show that, when it comes to antecedent-final conditionals, parsing-based theories predict filtering of presuppositions where there is in fact projection, and triviality judgments for sentences which are in fact felicitous. More concretely, these theories predict that presuppositions triggered in the antecedent of antecedent-final conditionals will be filtered (i.e. will not project) if (...)
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  31.  27
    (1 other version)Probabilistic Reasoning in Expert Systems Reconstructed in Probability Semantics.Roger M. Cooke - 1986 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1986:409 - 421.
    Los's probability semantics are used to identify the appropriate probability conditional for use in probabilistic explanations. This conditional is shown to have applications to probabilistic reasoning in expert systems. The reasoning scheme of the system MYCIN is shown to be probabilistically invalid; however, it is shown to be "close" to a probabilistically valid inference scheme.
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  32. Are Probabilism and Special Relativity Compatible?Nicholas Maxwell - 1988 - Philosophy of Science 55 (4):640-645.
    Are probabilism and special relativity compatible? Dieks argues that they are. But the possible universe he specifies, designed to exemplify both probabilism and special relativity, either incorporates a universal “now”, or amounts to a many world universe, or fails to have any one definite overall Minkowskian-type space-time structure. Probabilism and special relativity appear to be incompatible after all. What is at issue is not whether “the flow of time” can be reconciled with special relativity, but rather whether explicitly probabilistic (...)
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  33. Probabilistic measures of coherence: from adequacy constraints towards pluralism.Michael Schippers - 2014 - Synthese 191 (16):3821-3845.
    The debate on probabilistic measures of coherence flourishes for about 15 years now. Initiated by papers that have been published around the turn of the millennium, many different proposals have since then been put forward. This contribution is partly devoted to a reassessment of extant coherence measures. Focusing on a small number of reasonable adequacy constraints I show that (i) there can be no coherence measure that satisfies all constraints, and that (ii) subsets of these adequacy constraints motivate two (...)
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  34.  74
    Probabilistic algorithmic randomness.Sam Buss & Mia Minnes - 2013 - Journal of Symbolic Logic 78 (2):579-601.
    We introduce martingales defined by probabilistic strategies, in which randomness is used to decide whether to bet. We show that different criteria for the success of computable probabilistic strategies can be used to characterize ML-randomness, computable randomness, and partial computable randomness. Our characterization of ML-randomness partially addresses a critique of Schnorr by formulating ML randomness in terms of a computable process rather than a computably enumerable function.
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  35. Probabilistic Confirmation Theory and the Existence of God.Kelly James Clark - 1985 - Dissertation, University of Notre Dame
    A recent development in the philosophy of religion has been the attempt to justify belief in God using Bayesian confirmation theory. My dissertation critically discusses two prominent spokesmen for this approach--Richard Swinburne and J. L. Mackie. Using probabilistic confirmation theory, these philosophers come to wildly divergent conclusions with respect to the hypothesis of theism; Swinburne contends that the evidence raises the overall probability of the hypothesis of theism, whereas Mackie argues that the evidence disconfirms the existence of God. After (...)
     
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  36. Probabilistic Opinion Pooling Generalized. Part One: General Agendas.Franz Dietrich & Christian List - 2017 - Social Choice and Welfare 48 (4):747–786.
    How can different individuals' probability assignments to some events be aggregated into a collective probability assignment? Classic results on this problem assume that the set of relevant events -- the agenda -- is a sigma-algebra and is thus closed under disjunction (union) and conjunction (intersection). We drop this demanding assumption and explore probabilistic opinion pooling on general agendas. One might be interested in the probability of rain and that of an interest-rate increase, but not in the probability of rain (...)
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  37.  28
    Comparing decoding mechanisms for parsing argumentative structures.Stergos Afantenos, Andreas Peldszus & Manfred Stede - 2018 - Argument and Computation 9 (3):177-192.
    Parsing of argumentative structures has become a very active line of research in recent years. Like discourse parsing or any other natural language task that requires prediction of linguistic struc...
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  38.  20
    Parsing as a Cue-Based Retrieval Model.Jakub Dotlačil - 2021 - Cognitive Science 45 (8):e13020.
    This paper develops a novel psycholinguistic parser and tests it against experimental and corpus reading data. The parser builds on the recent research into memory structures, which argues that memory retrieval is content‐addressable and cue‐based. It is shown that the theory of cue‐based memory systems can be combined with transition‐based parsing to produce a parser that, when combined with the cognitive architecture ACT‐R, can model reading and predict online behavioral measures (reading times and regressions). The parser's modeling capacities are (...)
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  39. Probabilistic Knowledge.Sarah Moss - 2016 - Oxford, United Kingdom: Oxford University Press.
    Traditional philosophical discussions of knowledge have focused on the epistemic status of full beliefs. In this book, Moss argues that in addition to full beliefs, credences can constitute knowledge. For instance, your .4 credence that it is raining outside can constitute knowledge, in just the same way that your full beliefs can. In addition, you can know that it might be raining, and that if it is raining then it is probably cloudy, where this knowledge is not knowledge of propositions, (...)
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  40. Are probabilism and special relativity incompatible?Nicholas Maxwell - 1985 - Philosophy of Science 52 (1):23-43.
    In this paper I expound an argument which seems to establish that probabilism and special relativity are incompatible. I examine the argument critically, and consider its implications for interpretative problems of quantum theory, and for theoretical physics as a whole.
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  41.  79
    A parsing method for Montague grammars.Joyce Friedman & David S. Warren - 1978 - Linguistics and Philosophy 2 (3):347 - 372.
    The main result in this paper is a method for obtaining derivation trees from sentences of certain formal grammars. No parsing algorithm was previously known to exist for these grammars.Applied to Montague's PTQ the method produces all parses that could correspond to different meanings. The technique directly addresses scope and reference and provides a framework for examining these phenomena. The solution for PTQ is implemented in an efficient and useful computer program.
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  42. Probabilistic Causation.John-Michael Kuczynski - 2015 - Madison: Freud Institute.
    In this paper, it is shown that an event E can be the cause of an event E* even if there is a less than 100% likelihood that, given an arbitrary E-similar event, an E*-similar event will ensue.
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  43. Probabilistic Arguments in the Epistemological Approach to Argumentation.Christoph Lumer - 2011 - In Frans H. van Eemeren, Bart Garssen, David Godden & Gordon Mitchell, Proceedings of the Seventh International Conference of the International Society for the Study of Argumentation. Rozenberg / Sic Sat. pp. 1141-1154.
    The aim of the paper is to develop general criteria of argumentative validity and adequacy for probabilistic arguments on the basis of the epistemological approach to argumentation. In this approach, as in most other approaches to argumentation, proabilistic arguments have been neglected somewhat. Nonetheless, criteria for several special types of probabilistic arguments have been developed, in particular by Richard Feldman and Christoph Lumer. In the first part (sects. 2-5) the epistemological basis of probabilistic arguments is discussed. With (...)
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  44.  27
    Probabilism, just war and sovereing supremacy in the work of Gabriel Vazquez.Daniel Schwartz - 2013 - History of Political Thought 34 (2):177-194.
    Proponents of probabilism argued that 'when an opinion is probable it may be followed even when the contrary opinion is more probable'. Gabriel Vazquez (1549-1604) was the first Jesuit theologian to defend and expand this doctrine. The prevalent theory of sovereignty at the time held that: (1) when sovereigns are victims of wrongs, they take on the role of international judges (thus just wars are just punishments); and (2) the sovereign need not stand before the judgment of any other human (...)
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  45.  21
    Underspecification, Parsing Mismatches and Routinisation: The Historical Development of the Clitic Systems of Greek Dialects.Stergios Chatzikyriakidis - 2020 - Journal of Logic, Language and Information 30 (2):277-304.
    In this paper, the historical development of the clitic systems of Standard Modern, Cypriot and Pontic Greek is discussed. These three varieties not only present the whole range of variation one can find across clitic systems in Greek but, furthermore, derive from a common linguistic ancestor, i.e. Koine Greek. This paper argues that the transition from Koine Greek to the Medieval varieties and from the Medieval varieties to the respective modern ones can be explained by making the assumption that routinisation (...)
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  46. The Probabilistic Mind: Prospects for Bayesian Cognitive Science.Nick Chater & Mike Oaksford (eds.) - 2008 - Oxford University Press.
    'The Probabilistic Mind' is a follow-up to the influential and highly cited 'Rational Models of Cognition'. It brings together developments in understanding how, and how far, high-level cognitive processes can be understood in rational terms, and particularly using probabilistic Bayesian methods.
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  47. Radical probabilism and bayesian conditioning.Richard Bradley - 2005 - Philosophy of Science 72 (2):342-364.
    Richard Jeffrey espoused an antifoundationalist variant of Bayesian thinking that he termed ‘Radical Probabilism’. Radical Probabilism denies both the existence of an ideal, unbiased starting point for our attempts to learn about the world and the dogma of classical Bayesianism that the only justified change of belief is one based on the learning of certainties. Probabilistic judgment is basic and irreducible. Bayesian conditioning is appropriate when interaction with the environment yields new certainty of belief in some proposition but leaves (...)
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  48. Probabilistic Opinion Pooling.Franz Dietrich & Christian List - 2016 - In Alan Hájek & Christopher Hitchcock, The Oxford Handbook of Probability and Philosophy. Oxford: Oxford University Press.
    Suppose several individuals (e.g., experts on a panel) each assign probabilities to some events. How can these individual probability assignments be aggregated into a single collective probability assignment? This article reviews several proposed solutions to this problem. We focus on three salient proposals: linear pooling (the weighted or unweighted linear averaging of probabilities), geometric pooling (the weighted or unweighted geometric averaging of probabilities), and multiplicative pooling (where probabilities are multiplied rather than averaged). We present axiomatic characterisations of each class of (...)
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  49.  34
    Parsing with Treebank Grammars: Empirical Bounds, Theoretical Models, and the Structure of the Penn Treebank.Dan Klein & Christopher D. Manning - unknown
    This paper presents empirical studies and closely corresponding theoretical models of the performance of a chart parser exhaustively parsing the Penn Treebank with the Treebank’s own CFG grammar. We show how performance is dramatically affected by rule representation and tree transformations, but little by top-down vs. bottom-up strategies. We discuss grammatical saturation, including analysis of the strongly connected components of the phrasal nonterminals in the Treebank, and model how, as sentence length increases, the effective grammar rule size increases as (...)
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  50. Probabilistic opinion pooling generalised. Part two: The premise-based approach.Franz Dietrich & Christian List - 2017 - Social Choice and Welfare 48 (4):787–814.
    How can different individuals' probability functions on a given sigma-algebra of events be aggregated into a collective probability function? Classic approaches to this problem often require 'event-wise independence': the collective probability for each event should depend only on the individuals' probabilities for that event. In practice, however, some events may be 'basic' and others 'derivative', so that it makes sense first to aggregate the probabilities for the former and then to let these constrain the probabilities for the latter. We formalize (...)
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