In a real environment, acoustic and language features often vary depending on the speakers, speaking styles and topic changes. To accommodate these changes, speech recognition approaches that include the incremental tracking of changing environments have attracted attention. This paper proposes a topic tracking language model that can adaptively track changes in topics based on current text inf...

Topic features are useful in improving text summarization. However, independency among topics is a strong restriction on most topic models, and alleviating this restriction can deeply capture text structure. This paper proposes a hybrid topic model to generate multi-document summaries using a combination of the Hidden Topic Markov Model (HTMM), the surface texture model and the topic transition...

This paper proposes a novel topic model, Citation-Author-Topic (CAT) model that addresses a semantic search task we define as expert search – given a research area as a query, it returns names of experts in this area. For example, Michael Collins would be one of the top names retrieved given the query Syntactic Parsing. Our contribution in this paper is two-fold. First, we model the cited autho...

Query expansion is one of most important technique in information retrieval where provides a series of methods to reform the query applying to retrieval in order to extract result more precisely. In the past, there are many kind of trails on this technique in different perspective. In this project, I’ll try to implement query expansion with topic models, where using topic modeling technique to ...

Compared to the vast amount of work that has been dedicated to developing and reening proof search methods (not only) for classical rst-order logic, surprisingly little is known about algorithmic methods for Model Building. On the other hand nding and investigating models of abstract structures is at the very heart of mathematical activity. Consequently the value of models in Automated Deductio...

In this paper we propose the sparse supervised topic model (SSTM), a graphical model that learns topic structures of a given document collection and also a sparse linear prediction model for response vairables associated with documents. Our model jointly learns the topics and the classifier and encourages a sparse classifier by concentrating all the relevant information for prediction into a sm...

In this paper, we consider the problem of modeling hierarchical labeled data – such as Web pages and their placement in hierarchical directories. The state-of-the-art model, hierarchical Labeled LDA (hLLDA), assumes that each child of a non-leaf label has equal importance, and that a document in the corpus cannot locate in a non-leaf node. However, in most cases, these assumptions do not meet t...

Tra c congestion is quite common in urban settings, and is not always caused by tra c incidents. In this paper, we propose a simple method for detecting tra c incidents by using probe-car data to compare usual and current tra c states, thereby distinguishing incidents from spontaneous congestion. First, we introduce a tra c state model based on a probabilistic topic model to describe tra c stat...

This supplement includes brief elaborations on the main paper that may be of interest to some readers. In Section 1, we explain the minorization procedure underlying MAP inference. In Section 2, we lay out the details of our stochastic subgradient approximation procedure for online MAP inference. In Section 3, we lay out a useful interpretation of MAP prediction. In Section 4, we summarize the ...

We address the issue of 'topic analysis,' by which is determined a text's topic structure, which indicates what topics are included in a text, and how topics change within the text. We propose a novel approach to this issue, one based on statistical modeling and learning. We represent topics by means of word clusters, and employ a finite mixture model to represent a word distribution within a t...