How classes of things and properties in general should be represented in conceptual models is a fundamental issue. For example, proponents of object-role modelling argue that no distinction should be made between the two constructs, whereas proponents of entity-relationship modelling argue the distinction is important but provide ambiguous guidelines about how the distinction should be made. In...

In Semantic Role Labeling (SRL), it is reasonable to globally assign semantic roles due to strong dependencies among arguments. Some relations between arguments significantly characterize the structural information of argument structure. In this paper, we concentrate on thematic hierarchy that is a rank relation restricting syntactic realization of arguments. A loglinear model is proposed to ac...

Context-aware applications rely on implicit forms of input, such as sensor-derived data, in order to reduce the need for explicit input from users. They are especially relevant for mobile and pervasive computing environments, in which user attention is at a premium. To support the development of context-aware applications, techniques for modelling context information are required. These must ad...

The predicate and its semantic roles compose a unified entity that conveys the semantics of a given sentence. A standard pipeline of current approaches to semantic role labeling (SRL) is that for a given predicate in a sentence, we can extract features for each candidate argument and then perform the role classification through a classifier. However, this process totally ignores the integrality...

Tweets have become an increasingly popular source of fresh information. We investigate the task of Nominal Semantic Role Labeling (NSRL) for tweets, which aims to identify predicate-argument structures defined by nominals in tweets. Studies of this task can help fine-grained information extraction and retrieval from tweets. There are two main challenges in this task: 1) The lack of information ...

In this paper, we present a Semantic Role Labeling tool for Italian language for the FLaIT competition at Evalita 2011. This tool presents an hybrid approach to resolve the different sub-tasks that composed the SRL task. We apply a discriminative model for the boundary detection task based on lexical and syntactical features. A distributional approach to modeling lexical semantic information, i...

In this paper we derive closed-form solutions for a variety of prices for financial assets in an RBC economy. The equations are based on a log-linear solution of the RBC model and allow a clearer understanding of the determination of risk premia in models with production. We demonstrate not only why the premium of equity over the risk-free rate is small but also why the premium of equity over a...

Using the standard real business cycle model with lump-sum taxes, we analyze the impact of fiscal policy when agents form expectations using adaptive learning rather than rational expectations (RE). The output multipliers for government purchases are significantly higher under learning, and fall within empirical bounds reported in the literature, which is in sharp contrast to the implausibly lo...

This paper introduces and analyzes a battery of inference models for the problem of semantic role labeling: one based on constraint satisfaction, and several strategies that model the inference as a meta-learning problem using discriminative classifiers. These classifiers are developed with a rich set of novel features that encode proposition and sentence-level information. To our knowledge, th...

A fundamental step in sentence comprehension involves assigning semantic roles to sentence constituents. To accomplish this, the listener must parse the sentence, find constituents that are candidate arguments, and assign semantic roles to those constituents. Where do children learning their first languages begin in solving this problem? To experiment with different representations that childre...