Quantitative Content
Analysis Area

QCA       ISTL PARC

The academic and professional skills most strongly represented within QCA include:

We draw from each of these areas and integrate the techniques and knowledge from these fields in our research. For example, from pattern recognition and machine learning, we have exploited and tailored a number of techniques, including PLSA (probabilistic latent semantic analysis), HMMs, kNN, Naive Bayes, and SVMs. From computational linguistics we draw on knowledge about language and superficial linguistic and textual cues. Statistical analysis of the web allows us to integrate linkage structure with lingustic content.

Principal Interests of Research Members

Francine Chen Topic analysis, text summarization, pattern recognition, multi-modal information access
Ayman Farahat Web analysis, statistical language modeling, information retrieval, personalization and recommendation systems.
Annie Zaenen Shallow linguistic analysis, grammars