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While being successful in providing keyword based access to web pages, commercial search portals still lack the ability to answer questions expressed in a natural language. We present a probabilistic approach to automated question answering on the Web, based on trainable patterns, answer triangulation and semantic filtering. In contrast to the other "shallow" approaches, our approach is entirely self-learning. It does not require any manually created scoring and filtering rules while still performing comparably. It also performs better than other fully trainable approaches.
@inproceedings{1009090, author = {Dmitri Roussinov and Jose Robles}, title = {Learning patterns to answer open domain questions on the web}, booktitle = {SIGIR '04: Proceedings of the 27th annual international conference on Research and development in information retrieval}, year = {2004}, isbn = {1-58113-881-4}, pages = {500--501}, location = {Sheffield, United Kingdom}, doi = {http://doi.acm.org/10.1145/1008992.1009090}, publisher = {ACM Press}, }  ©2005 Association for Computing Machinery |
