Language Modeling Using Dynamic Bayesian Networks

Topic-based language modeling with dynamic Bayesian networks

Although n-gram models are still the de facto standard in language modeling for speech recognition, more sophisticated models achieve better accuracy by taking additional information, such as syntactic rules, semantic relations or domain knowledge into account. Unfortunately, most of the effort in developing such models goes into the implementation of handcrafted inference routines. A generic m...

An Introduction to Inference and Learning in Bayesian Networks

Bayesian networks (BNs) are modern tools for modeling phenomena in dynamic and static systems and are used in different subjects such as disease diagnosis, weather forecasting, decision making and clustering. A BN is a graphical-probabilistic model which represents causal relations among random variables and consists of a directed acyclic graph and a set of conditional probabilities. Structure...

Language Modeling With Dynamic Bayesian Networks Using Conversation Types and Part of Speech Information

In this paper we investigate whether more accurate modeling of differences in language in different types of conversations, e.g. formal presentations vs. spontaneous conversations can improve the quality of a language model. We also investigate whether the modeling of sentence lengths can improve a language model. A language model is an important component of statistical natural language proces...

Dynamic Bayesian Networks for Language Modeling

The modeling of body's immune system using Bayesian Networks

In this paper, the urinary infection, that is a common symptom of the decline of the immune system, is discussed based on the well-known algorithms in machine learning, such as Bayesian networks in both Markov and tree structures. A large scale sampling has been executed to evaluate the performance of Bayesian network algorithm. A number of 4052 samples wereobtained from the database of the Tak...

Robust Opponent Modeling in Real-Time Strategy Games using Bayesian Networks

Opponent modeling is a key challenge in Real-Time Strategy (RTS) games as the environment is adversarial in these games, and the player cannot predict the future actions of her opponent. Additionally, the environment is partially observable due to the fog of war. In this paper, we propose an opponent model which is robust to the observation noise existing due to the fog of war. In order to cope...