Robert N Oddy

Using cause-effect relations in text to improve information retrieval precision

Abstract This study attempted to use semantic relations expressed in text, in particular cause-effect relations, to improve information retrieval effectiveness. The study investigated whether the information obtained by matching cause-effect relations expressed in documents with the cause-effect relations expressed in users’ queries can be used to improve document retrieval results, in comparison to using just keyword matching without considering relations. An automatic method for identifying and extracting cause-effect information in Wall Street Journal text was developed. Causal relation matching was found to yield a small but significant improvement in retrieval results when the weights used for combining the scores from different types of matching were customized for each query. Causal relation matching did not perform better than word proximity matching (i.e. matching pairs of causally related words in the query with pairs of words that co-occur within document sentences), but the best results were obtained when causal relation matching was combined with word proximity matching. The best kind of causal relation matching was found to be one in which one member of the causal relation (either the cause or the effect) was represented as a wildcard that could match with any word.

An application of plausible reasoning to information retrieval

Two versions of the extended plausible reasoning system were implemented, one using dominance weights (described in the paper) and the other using &id’ (Term Frequency Inverse Document Frequency) weights. Experiments were conducted using the titles and abstracts of the CACM collection and it was found that both versions of the extended plausible reasoning system are better than the vector space model and the system using dominance weights performed better than the system with ~. id~weights.

PThomas: an adaptive information retrieval system on the connection machine

Abstract This paper reports the state of development of PThomas , a network based document retrieval system implemented on a massively parallel fine-grained computer, the Connection Machine. The program is written in C ∗ , an enhancement of the C programming language which exploits the parallelism of the Connection Machine. The system is based on Oddys original Thomas program, which was highly parallel in concept, and makes use of the Connection Machines single instruction multiple data (SIMD) processing capabilities. After an introduction to systems like Thomas , and their relationship to spreading activation and neural network models, the current state of PThomas is described, including details about the network representation and the parallel operations that are executed during a typical PThomas session.