Context-driven Bengali Text Generation using Conditional Language Model

On Application of Conditional Random Field in Stemming of Bengali Natural Language Text

While stochastic route has been explored in solving the stemming problem, Conditional Random Field (CRF), a conditional probability based statistical model, has not been applied yet. We applied CRF to train a set of stemmers for Bengali natural language text. Care had been taken to design it language neutral so that same approach can be applied for other languages. The experiments yielded more ...

Process Model Generation from Natural Language Text

Business process modeling has become an important tool for managing organizational change and for capturing requirements of software. A central problem in this area is the fact that the acquisition of as-is models consumes up to 60% of the time spent on process management projects. This is paradox as there are often extensive documentations available in companies, but not in a ready-to-use form...

Bengali Text Summarization Using Singular Value Decomposition

Natural Language Generation within Model-Driven Architecture

There is often a gap between the requirements and the code of a system, both as in specification versus implementation [9, 3] but also in the knowledge needed to understand them [2]. One attempt to bridge the gap is given by Model-Driven Architecture [8, 6] where software models are used as an intermediate step. The idea is that the models should both formalise the requirements and explain the ...

Using Language and Context in the Analysis of Text

We describe a theory of natural language understanding within which we identify two separate components, a language centered one and a context centered one. xn5 rdrme* 1 component uses a knowledge Dase consisting of pairings of phrases with the concepts associated with them to determine the meaning of utterances. The latter component clarifies the meaning found by the first one and makes it mor...