TREC-2 Document Retrieval Experiments using PIRCS

We performed the full experiments, using our network implementation of component probabilistic indexing and retrieval model. Documents were enhanced with a list of semi-automatically generated two-word phrases, and queries with automatic Boolean expressions. An item self-learning procedure was used to initiate network edge weights for retrieval. Initial results submitted were above median for ad hoc, and below median for routing. They were not up to expectation because of a bad choice of high-frequency cutoff for terms, and no query expansion for routing. Later experiments showed that our system does return very good results after correcting the earlier problems and adjusting some parameters. We also re-design our system to handle virtually any number of large files in an incremental fashion, and to do retrieval and learning by initiating our network on demand, without first creating a full inverted file.