This note describes the inside-outside algorithm. The inside-outside algorithm has very important applications to statistical models based on context-free grammars. In particular, it is used in EM estimation of probabilistic context-free grammars, and it is used in estimation of discriminative models for context-free parsing. As we will see, the inside-outside algorithm has many similarities to...

We introduce a new approach to named entity classification which we term a Priority Model. We also describe the construction of a semantic database called SemCat consisting of a large number of semantically categorized names relevant to biomedicine. We used SemCat as training data to investigate name classification techniques. We generated a statistical language model and probabilistic contextf...

This paper describes a formal model for the recognition of on-line handwritten mathematical expressions using 2D stochastic context-free grammars and hidden Markov models. Hidden Markov models are used to recognize mathematical symbols, and a stochastic context-free grammar is used to model the relation between these symbols. This formal model makes possible to use classic algorithms for parsin...

This paper presents an automatic harmonization method that, for a given melody (sequence of musical notes), generates a sequence of chord symbols in the style of existing data. A typical way is to use hidden Markov models (HMMs) that represent chord transitions on a regular grid (e.g., bar or beat grid). This approach, however, cannot explicitly describe the rhythms, harmonic functions (e.g., t...

background and objectives: public-private partnership (ppp) is a well-established model to alleviate the risk of investment in health domain. while the model is widely applied in the developed countries, the adoption of the model in many developing countries is hampered partly by the lack of knowledge on dimensions and requirements of its local implementation. the present study, thus, aimed to ...

Language models (LMs) are often constructed by building multiple component LMs that are combined using interpolation weights. By tuning these interpolation weights, using either perplexity or discriminative approaches, it is possible to adapt LMs to a particular task. In this work, improved LM adaptation is achieved by introducing context dependent interpolation weights. An important part of th...

Existing driving behaviour models have a strong emphasis on the driver’s cognitive components including aspects such as motivation, risk assessment, attention, compensation, capability, workload, individual traits and experience. Each existing model was designed specifically for a particular driving situation such as speeding or fatigue. A general and comprehensive model is still unavailable de...