Type inference

Xem 1-20 trên 24 kết quả Type inference
  • We propose a novel approach to constraint-based type inference based on coinductive logic. Constraint generation corresponds to translation into a conjunction of Horn clauses P, and constraint satisfaction is defined in terms of the coinductive Herbrand model of P. We illustrate the approach by formally defining this translation for a small object-oriented language similar to Featherweight Java, where type annotations in field and method declarations can be omitted.

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  • In the context of object-oriented programming, many solutions have been proposed to the problem of type inference [17,16,1,21,6,20,12], but the increasing interest in dynamic object-oriented languages is asking for ever more precise and efficient type inference algorithms [3,12]. Two important features which should be supported by type inference are parametric and data polymorphism [1]; the former allows invocation of a method on arguments of unrelated types, the latter allows assignment of values of unrelated types to a field.

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  • Extensive knowledge bases of entailment rules between predicates are crucial for applied semantic inference. In this paper we propose an algorithm that utilizes transitivity constraints to learn a globally-optimal set of entailment rules for typed predicates. We model the task as a graph learning problem and suggest methods that scale the algorithm to larger graphs. We apply the algorithm over a large data set of extracted predicate instances, from which a resource of typed entailment rules has been recently released (Schoenmackers et al., 2010). ...

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  • It is claimed that a variety of facts concerning ellipsis, event reference, and interclausal coherence can be explained by two features of the linguistic form in question: (1) whether the form leaves behind an empty constituent in the syntax, and (2) whether the form is anaphoric in the semantics. It is proposed that these features interact with one of two types of discourse inference, namely Common Topic inference and Coherent Situation inference.

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  • We predict entity type distributions in Web search queries via probabilistic inference in graphical models that capture how entitybearing queries are generated. We jointly model the interplay between latent user intents that govern queries and unobserved entity types, leveraging observed signals from query formulations and document clicks. We apply the models to resolve entity types in new queries and to assign prior type distributions over an existing knowledge base.

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  • Event extraction is a particularly challenging type of information extraction (IE). Most current event extraction systems rely on local information at the phrase or sentence level. However, this local context may be insufficient to resolve ambiguities in identifying particular types of events; information from a wider scope can serve to resolve some of these ambiguities. In this paper, we use document level information to improve the performance of ACE event extraction.

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  • Event extraction is the task of detecting certain specified types of events that are mentioned in the source language data. The state-of-the-art research on the task is transductive inference (e.g. cross-event inference). In this paper, we propose a new method of event extraction by well using cross-entity inference. In contrast to previous inference methods, we regard entitytype consistency as key feature to predict event mentions. We adopt this inference method to improve the traditional sentence-level event extraction system. ...

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  • This paper presents an original approach to semi-supervised learning of personal name ethnicity from typed graphs of morphophonemic features and first/last-name co-occurrence statistics. We frame this as a general solution to an inference problem over typed graphs where the edges represent labeled relations between features that are parameterized by the edge types. We propose a framework for parameter estimation on different constructions of typed graphs for this problem using a gradient-free optimization method based on grid search.

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  • Current approaches to generating multi-sentence text fail to consider what the user may infer from the different statements in a description. This paper presents a system which contains an explicit model of the inferences that people may make from different statement types, and uses this model, together with assumptions about the user's prior knowledge, to pick the most appropriate sequence of utterances for achieving a given communicative goal.

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  • Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: a knowledge base, a computerized model, and a user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions.

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  • In recent years many successful machine learning applications have been developed, ranging from data mining programs that learn to detect fraudulent credit card transactions, to information filtering systems that learn user’s reading preferences, to autonomous vehicles that learn to drive on public highways. At the same time, machine learning techniques such as rule induction, neural networks, genetic learning, case-based reasoning, and analytic learning have been widely applied to real-world problems.

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  • Lecture "Advanced Econometrics (Part II) - Chapter 3: Discrete choice analysis - Binary outcome models" presentation of content: Discrete choice model, basic types of discrete values, the probability models, estimation and inference in binary choice model, binary choice models for panel data.

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  • A lot has happened in the last decade on rationalising the congeries of rules of evidence applied in English courts. Scientific evidence is gradually replacing evidence based on the principle of orality or spontaneity. And yet, judges are not scientifically trained.

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  • Abstract The work presented in this paper deals with the problem of the navigation of a mobile robot either in unknown indoor environment or in a partially known one. A navigation method in an unknown environment based on the combination of elementary behaviors has been developed. Most of these behaviors are achieved by means of fuzzy inference systems. The proposed navigator combines two types of obstacle avoidance behaviors, one for the convex obstacles and one for the concave ones.

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  • The 6-step plan Assumption questions Additional evidence questions Inference questions Strategy questions Hypothesis questions Necessary inference questions Parallel argument questions Summing it up In this chapter, you’ll: • • • • Briefly review the basic terminology you need to know for GMAT Critical Reasoning Learn a step-by-step approach to handling any Critical Reasoning question Learn how to recognize and handle each of the three basic, and most common, types of Critical Reasoning questions Learn success keys for tackling Critical Reasoning questions ...

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  • We develop an abstract component language and a static type system that can tells us the maximum resources a program may use. We prove that the upper resource bound is sharp and we point out a polynomial algorithm that can infer the sharp bound. Knowing the maximal resources a program may request allows us to adjust resource usage of the program and to prevent it from raising exceptions or behaving unexpectedly on systems that do not have enough resources.

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  • Resolving polysemy and synonymy is required for high-quality information extraction. We present ConceptResolver, a component for the Never-Ending Language Learner (NELL) (Carlson et al., 2010) that handles both phenomena by identifying the latent concepts that noun phrases refer to. ConceptResolver performs both word sense induction and synonym resolution on relations extracted from text using an ontology and a small amount of labeled data. Domain knowledge (the ontology) guides concept creation by defining a set of possible semantic types for concepts.

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  • In our review of the literature, we focus first on the value graduates derive from holding the MBA degree. Identification of these benefits should lead to inferences on the reasons for choosing the degree. But since our premise is that online programs encourage the participation of more experienced students, we believe that understanding the motives for choosing a specific program is also important. Our review of the literature is organized to highlight these two points: the value of the MBA, and the motives for choosing a particular type of MBA program. ...

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  • This paper introduces B IU T EE1 , an opensource system for recognizing textual entailment. Its main advantages are its ability to utilize various types of knowledge resources, and its extensibility by which new knowledge resources and inference components can be easily integrated.

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  • Name tagging is a critical early stage in many natural language processing pipelines. In this paper we analyze the types of errors produced by a tagger, distinguishing name classification and various types of name identification errors. We present a joint inference model to improve Chinese name tagging by incorporating feedback from subsequent stages in an information extraction pipeline: name structure parsing, cross-document coreference, semantic relation extraction and event extraction.

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