The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

We address the problem of embedding enti ties into Euclidean space over time based on co-occurrence data. We extend the CODE model of Globerson et al. (2004) to a dynamic setting. This leads to a non-standard factored state space model with real-valued hidden parent nodes and discrete observation nodes. We investigate the use of variational approximations applied to the observation model that allow us to formulate the entire dynamic model as a Kalman Flter. Applying this model to temporal co-occurrence data yields posterior distributions of entity coordinates in Euclidean space that are updated over time. Initial results on per-year co-occurrences of authors and words in the NIPS corpus and on synthetic data, including videos of dynamic embeddings, seem to indicate that the model results in embeddings of co-occurrence data that are meaningful both temporally and contextually.

Notes

Text Reference

Purnamrita Sarkar, Sajid Siddiqi, and Geoffrey Gordon, "Approximate Kalman Filters for Embedding Author-Word Co-occurrence Data over Time," Workshop on Statistical Network Analysis at the Twenty-third International Conference on Machine Learning (ICML), 2006.

BibTeX Reference

@inproceedings{Siddiqi_2006_5689,
   author = "Purnamrita Sarkar and Sajid Siddiqi and Geoffrey Gordon",
   title = "Approximate Kalman Filters for Embedding Author-Word Co-occurrence Data over Time",
   booktitle = "Workshop on Statistical Network Analysis at the Twenty-third International Conference on Machine Learning (ICML)",
   year = "2006",
}

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