the research problem investigated in this paper is modeling volatility and analyzing risk and return’s relationship in tehran stock exchange using garch-family models including garch(1,1), garch(2,2), egarch(1,1), pgarch(1,1), tgarch(1,1), garch(1,1)-m and cgarch(1,1). using the daily returns of tehran stock exchange companies, we focused on two portfolios of all the companies during a 10-year-...

chaos theory discusses unstable and non-periodic behavior of non-linear fluctuating dynamicsystems. since evaporation level variations of lakes have a dynamic identity, chaos theory can have a uniquerole in collecting data of these variations. therefore it is an important and challenging problem in the field ofplanning and qualitative and quantitative management of urmia lake to verify whether ...

In distributional semantics studies, there is a growing attention in compositionally determining the distributional meaning of word sequences. Yet, compositional distributional models depend on a large set of parameters that have not been explored. In this paper we propose a novel approach to estimate parameters for a class of compositional distributional models: the additive models. Our approa...

Natural language semantics has recently sought to combine the complementary strengths of formal and distributional approaches meaning. However, given fundamentally different ‘representational currency’ underlying these approaches—models world versus linguistic co-occurrence—their unification proven extremely difficult. Here, we define Distributional Formal Semantics, which integrates distributi...

Automatic event location extraction from text plays a crucial role in many applications such as infectious disease surveillance and natural disaster monitoring. The fundamental limitation of previous work such as SpaceEval is the limited scope of extraction, targeting only at locations that are explicitly stated in a syntactic structure. This leads to missing a lot of implicit information infer...

Context-predicting models (more commonly known as embeddings or neural language models) are the new kids on the distributional semantics block. Despite the buzz surrounding these models, the literature is still lacking a systematic comparison of the predictive models with classic, count-vector-based distributional semantic approaches. In this paper, we perform such an extensive evaluation, on a...

We study a nonlinear equation with an elliptic operator having degenerate coercivity. We prove the existence of a W 1,1 0 (Ω) solution which is distributional or entropic, according to the growth assumptions on a lower order term in divergence form. To Ildefonso: But of all these friends and lovers There is no one compares with you [9]

This paper investigates the role of Distributional Semantic Models (DSMs) into a Question Answering (QA) system. Our purpose is to exploit DSMs for answer re-ranking in QuestionCube, a framework for building QA systems. DSMs model words as points in a geometric space, also known as semantic space. Words are similar if they are close in that space. Our idea is that DSMs approaches can help to co...

We investigate how supervision (in the form of explicit instruction) interacts with distributional learning in the acquisition of the perception of a novel vowel contrast in a second language. An experiment with non-Dutch-speaking Bulgarians reveals that listeners who receive bimodal distributional training without explicit instruction can acquire new Dutch vowel contrasts, and that listeners w...