2.0 Conclusions

We presented in some detail our natural language information retrieval system consisting of an advanced NLP module and a `pure' statistical core engine. While many problems remain to be resolved, including the question of adequacy of term-based representation of document content, we attempted to demonstrate that the architecture described here is nonetheless viable. In particular , we demonstrated that natural language processing can now be done on a fairly large scale and that its speed and robustness has improved to the point where it can be applied to real IR problems. The main observation to make is that natural language processing is not as effective as we once hoped in to obtain better indexing and better term representations of queries. Using linguistic terms, such as phrases, head-modifier pairs, names, or even simple concepts does help to improve retrieval precision, but the gains remained quite modest. On the other hand, full text query expansion works remarkably well. Our main effort in the immediate future will be to explore ways to achieve at least partial automation of this process. An initial experiment in this direction has been performed as part of NLP Track (genlp3 run), and the results are encouraging. ACKNOWLEDGEMENTS. We would like to thank Donna Harman of NIST for making her PRISE system available to us since the beginning of TREC.. Will Rogers provided valuable assistance in installing updated versions of PRISE at NYU and Rutgers. We would also like to thank Ralph Weischedel for providing and assisting in the use of the BBN's part of speech tagger at NYU.