Historically, the utilization of context, the range and scope of context-aware systems, and the levels of computational intelligence in such systems have been very limited. While the inherent complexity is a significant factor, a principal reason for these limitations lies in the failure to incorporate the emotional component. Affective computing technologies are designed to implement innate em...

Context-free grammars (CFGs) are a class of formal grammars that have found numerous applications in modeling computer languages. A probabilistic form of CFG, the probabilistic CFG (PCFG), has also been successfully applied to model natural languages. In this paper, we discuss the use of PCFGs in natural language modeling. We develop PCFGs as a natural extension of the CFGs and explain one prob...

Several mathematical distances between probabilistic languages have been investigated in the literature, motivated by applications in language modeling, computational biology, syntactic pattern matching and machine learning. In most cases, only pairs of probabilistic regular languages were considered. In this paper we extend previous results to pairs of languages generated by a probabilistic co...

In this paper, it is argued that Critical Discourse Studies (CDS) should be extended to also include what may be called Critical Context Studies (CCS). After a summary of the new theory of context, defined in terms of special mental models in episodic memory, subjectively representing the ‘definition of the communicative situation’ by the participants, it is argued how a critical analysis of te...

This paper describes a Viterbi search algorithm for continuous speech recognition using context-dependent phone models under the constraint defined by a context-free grammar (CFG). It is based on a frame synchronous LR parser which dynamically generates a finite state network (FSN) from the CFG with an efficient path merging mechanism. Full context-dependency (intraand interword context) is tak...

In its early development, machine translation adopted rule-based approaches, which can include the use of language syntax. The late 1980s and early 1990s saw the inception of the statistical machine translation (SMT) approach, where translation models can be learned automatically from a parallel corpus rather than created manually by humans. Initial SMT models were word-based and phrase-based, ...

A B S T R A C T The notion of stochastic lexicalized tree-adjoining grammar (SLTAG) is defined and basic algorithms for SLTAG are designed. The parameters of a SLTAG correspond to the probability of combining two structures each one associated with a word. The characteristics of SLTAG are unique and novel since it is lexically sensitive (as N-gram models or Hidden Markov Models) and yet hierarc...

Primitive Optimality Theory (OTP) (Eisner, 1997a; Albro, 1998), a computational model of Optimality Theory (Prince and Smolensky, 1993), employs a finite state machine to represent the set of active candidates at each stage of an Optimality Theoretic derivation, as well as weighted finite state machines to represent the constraints themselves. For some purposes, however, it would be convenient ...

We present a language model in which the probability of a sentence is the sum of the individual parse probabilities, and these are calculated using a probabilistic context-free grammar (PCFG) plus statistics on individual words and how they t into parses. We have used the model to improve syntactic disambiguation. After training on Wall Street Journal (WSJ) text we tested on about 200 WSJ sente...