Nocuous ambiguity occurs when a linguistic expression is interpreted differently by different readers in a given context. We present an approach to automatically identify nocuous ambiguity that is likely to lead to misunderstandings among readers. Our model is built on a machine learning architecture. It learns from a set of heuristics each of which predicts a factor that may lead a reader to f...

Modern portfolio theory is based on the relationship between risk and return and in this paper, specific uncertainty conditions are introduced as ambiguity which affects the asset pricing. Also, the relationship between risk, ambiguity and return is examined. First, ambiguity is estimated by the means of three-variable and main component method, trading volume, ask-bid spread, error of earnings...

There has been some ambiguity about the growth of attractors in Kauffman networks with network size. Some recent work has linked this to the role and growth of circuits or loops of boolean variables. Using numerical methods we have investigated the growth of structural circuits in Kauffman networks and suggest that the exponential growth in the number of structural circuits places a lower bound...

A Computational Model of Syntactic Processing Ambiguity Resolution from Interpretation Michael Niv Mark J Steedman Supervisor Syntactic ambiguity abounds in natural language yet humans have no di culty coping with it In fact the process of ambiguity resolution is almost always unconscious But it is not infallible however as example demonstrates The horse raced past the barn fell This sentence i...

Abstract The researchers have been interested to explore the impact of personality traits on second or foreign language learning. The current study is an attempt to investigate whether there exists a statistically significant relationship between introvert and extrovert EFL learners’ willingness to communicate and ambiguity tolerance. To this end, a total of 150 EFL learners completed the Intro...

For distantly related proteins, alignmentsbased on structural information are more reliable than traditional sequence alignments. However, when structural comparison leaves some ambiguity in alignment, sequence information can provide valuable additional information to discriminate between multiple alternatives. In this paper we present a Bayesianmodel that incorporates sequence information int...

The objective of uncertainty quantification is to certify that a given physical, engineering or economic system satisfies multiple safety conditions with high probability. A more ambitious goal is to actively influence the system so as to guarantee and maintain its safety, a scenario which can be modeled through a chance constrained program. In this paper we assume that the parameters of the sy...

A common assumption when collecting network data is that objects can be uniquely identified. However, in many scenarios objects do not have a unique label giving rise to ambiguities since the mapping between observed labels and objects is not known. In this paper we consider the ambiguity problem that emerges when objects appear with more than one label in the context of social networks. We fir...

This paper presents Kenmore, a general framework for knowledge acquisition for natural language processing (NLP) systems. To ease the acquisition of knowledge in new domains, Kenmore exploits an on-line corpus using robust sentence analysis and embedded symbolic machine learning techniques while requiring only minimal human intervention. By treating all problems in ambiguity resolution as class...

Two eye-movement experiments examined the processing of sentences containing reduced relative constructions. In the first experiment, animacy of the sentential subject, structural ambiguity, and parafoveal preview of syntactically disambiguating material were manipulated. Evidence of disruption was found in temporarily ambiguous sentences, regardless of animacy or preview. In the second experim...