Latent Maximum Entropy Approach for Semantic N-gram Language Modeling

Semantic n-gram language modeling with the latent maximum entropy principle

A Maximum Entropy Approach for Semantic Language Modeling

The conventional n-gram language model exploits only the immediate context of historical words without exploring long-distance semantic information. In this paper, we present a new information source extracted from latent semantic analysis (LSA) and adopt the maximum entropy (ME) principle to integrate it into an n-gram language model. With the ME approach, each information source serves as a s...

Semantic structured language models

In this study, we propose two novel semantic language modeling techniques for spoken dialog systems. These methods are called semantic concept based language modeling and semantic structured language modeling. In the concept based language modeling, we propose to use long span semantic units to model meaning sequences in spoken utterances. In the latter technique, we use statistical semantic pa...

Semantic Structured La

In this study, we propose two novel semantic language modeling techniques for spoken dialog systems. These methods are called semantic concept based language modeling and semantic structured language modeling. In the concept based language modeling, we propose to use long span semantic units to model meaning sequences in spoken utterances. In the latter technique, we use statistical semantic pa...

Statistical Language Modeling with Performance Benchmarks using Various Levels of Syntactic-Semantic Information

Statistical language models using n-gram approach have been under the criticism of neglecting large-span syntactic-semantic information that influences the choice of the next word in a language. One of the approaches that helped recently is the use of latent semantic analysis to capture the semantic fabric of the document and enhance the n-gram model. Similarly there have been some approaches t...