We describe ParsCit, a freely available, open-source implementation of a reference string parsing package. At the core of ParsCit is a trained conditional random field (CRF) model used to label the token sequences in the reference string. A heuristic model wraps this core with added functionality to identify reference strings from a plain text file, and to retrieve the citation contexts. The pa...

We examine how nonveridical markers and rhetorical relations contribute to the expression of Appraisal (evaluation) in discourse. First, we define nonveridicality and nonveridical contexts, following Giannakidou (Polarity Sensitivity as (Non)Veridical Dependency. John Benjamins, Amsterdam, 1998). We then relate nonveridicality to modality, Appraisal and rhetorical relations, and present a map o...

Many applications of Natural Language Generation employ object-oriented knowledge representation formalisms such as loom. Each entity in the domain is represented by an object belonging to a speciied class and possessing attributes suitable for that class. From a logical viewpoint, domain entities correspond to existentially quantiied variables, but since loom and kindred formalisms provide no ...

This paper is a commentary on Beltrama and Bochnak (2015), who propose a uniform semantics for different uses of cross-categorial intensifiers -issimo in Italian and šému in Washo as universal quantifiers over contexts. Two concerns of their uniform semantics are raised, (i) unexpected restrictions on intensification, and (ii) non-truth-conditional intensification. It is argued that a uniform s...

Recent years have seen a sharp increase in the number of related yet distinct advances in semantic segmentation. Here, we tackle this problem by leveraging the respective strengths of these advances. That is, we formulate a conditional random field over a four-connected graph as end-to-end trainable convolutional and recurrent networks, and estimate them via an adversarial process. Importantly,...

This paper investigates the conditional correlations and volatility spillovers between the dollar exchange rate return, gold coin return and crude oil return to stock index return. Monthly returns in the 144 observations (2005 - 2017) are analyzed by constant conditional correlation, dynamic conditional correlation, VARMA-GARCH and VARMA-AGARCH models. So this paper presents interdependences in...

the stochastic outcomes and phenomenon of climate are the reason to use probability sciences in climatology. interactions of most of climatic phenomenon as well as justify them are very acceptable based on probability technique. accordingly it is useful in environmental management and planning. a conditional probability is the probability of an event given that another event has occurred e.g. l...

Variational approximation methods have become a mainstay of contemporary Machine Learning methodology, but currently have little presence in Statistics. We devise an effective variational approximation strategy for fitting generalized linear mixed models (GLMM) appropriate for grouped data. It involves Gaussian approximation to the distributions of random effects vectors, conditional on the res...

We propose a new way to model conditional independence in Markov models. The central feature of our nonuniform Markov model is that it makes predictions of varying lengths using contexts of varying lengths. Experiments on the Wall Street Journal reveal that the nonuniform model performs slightly better than the classic interpolated Markov model of Jelinek and Mercer (1980). This result is somew...

We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features...