This paper presents the Topic-Aspect Model (TAM), a Bayesian mixture model which jointly discovers topics and aspects. We broadly define an aspect of a document as a characteristi...
Identifying the most influential documents in a corpus is an important problem in many fields, from information science and historiography to text summarization and news aggregati...
In this paper, we propose a PLSA-based language model for sports live speech. This model is implemented in unigram rescaling technique that combines a topic model and an n-gram. I...
Statistical language models can learn relationships between topics discussed in a document collection and persons, organizations and places mentioned in each document. We present a...
David Newman, Chaitanya Chemudugunta, Padhraic Smy...
This paper presents an LDA-style topic model that captures not only the low-dimensional structure of data, but also how the structure changes over time. Unlike other recent work t...