This paper studies the problem of sentencelevel semantic coherence by answering SATstyle sentence completion questions. These questions test the ability of algorithms to distingui...
Geoffrey Zweig, John C. Platt, Christopher Meek, C...
Latent semantic analysis (LSA), as one of the most popular unsupervised dimension reduction tools, has a wide range of applications in text mining and information retrieval. The k...
Xi Chen, Yanjun Qi, Bing Bai, Qihang Lin, Jaime G....
By mapping messages into a large context, we can compute the distances between them, and then classify them. We test this conjecture on Twitter messages: Messages are mapped onto t...
Yegin Genc, Yasuaki Sakamoto, Jeffrey V. Nickerson
Psychological measures of concreteness of words are generally estimated by having humans provide ratings of words on a concreteness scale. Due to the limits of this technique, con...
Shi Feng, Zhiqiang Cai, Scott A. Crossley, Daniell...
Probabilistic Latent Semantic Analysis (PLSA) has become a popular topic model for image clustering. However, the traditional PLSA method considers each image (document) independen...
Text summarization solves the problem of extracting important information from huge amount of text data. There are various methods in the literature that aim to find out well-form...
■ This study examined neural activity associated with establishing causal relationships across sentences during on-line comprehension. ERPs were measured while participants read...
We explore the near-synonym lexical choice problem using a novel representation of near-synonyms and their contexts in the latent semantic space. In contrast to traditional latent...
Probabilistic latent topic models have recently enjoyed much success in extracting and analyzing latent topics in text in an unsupervised way. One common deficiency of existing to...
Latent Semantic Analysis (LSA) is a relatively new research tool with a wide range of applications in different fields ranging from discourse analysis to cognitive science, from i...