Semantic N-Gram Topic Modeling

Latent Maximum Entropy Approach for Semantic N-gram Language Modeling

Discriminative n-gram language modeling

This paper describes discriminative language modeling for a large vocabulary speech recognition task. We contrast two parameter estimation methods: the perceptron algorithm, and a method based on maximizing the regularized conditional log-likelihood. The models are encoded as deterministic weighted finite state automata, and are applied by intersecting the automata with word-lattices that are t...

n-Gram Geo-trace Modeling

As location-sensing smart phones and location-based services gain mainstream popularity, there is increased interest in developing techniques that can detect anomalous activities. Anomaly detection capabilities can be used in theft detection, remote elder-care monitoring systems, and many other applications. In this paper we present an ngram based model for modeling a user’s mobility patterns. ...

Ordering-Sensitive and Semantic-Aware Topic Modeling

Topic modeling of textual corpora is an important and challenging problem. In most previous work, the “bag-of-words” assumption is usually made which ignores the ordering of words. This assumption simplifies the computation, but it unrealistically loses the ordering information and the semantic of words in the context. In this paper, we present a Gaussian Mixture Neural Topic Model (GMNTM) whic...

A Nonparametric N-Gram Topic Model with Interpretable Latent Topics

Most nonparametric topic models such as Hierarchical Dirichlet Processes, when viewed as an infinite-dimensional extension to the Latent Dirichlet Allocation, rely on the bag-of-words assumption. They thus lose the semantic ordering of the words inherent in the text which can give an extra leverage to the computational model. We present a new nonparametric topic model that not only maintains th...