The literary works of Sir William Osler

A paper read at the first regular meeting of the Osler Society of McGill University, October 19, 1921.     

Multilingual modeling of cross-lingual spelling variants

Technical term translations are important for cross-lingual information retrieval. In many languages, new technical terms have a common origin rendered with different spelling of the underlying sounds, also known as cross-lingual spelling variants (CLSV). To find the best CLSV in a text database index, we contribute a formulation of the problem in a probabilistic framework, and implement this with an instance of the general edit distance using weighted finite-state transducers. Some training data is required when estimating the costs for the general edit distance. We demonstrate that after some basic training our new multilingual model is robust and requires little or no adaptation for covering additional languages, as the model takes advantage of language independent transliteration patterns. We train the model with medical terms in seven languages and test it with terms from varied domains in six languages. Two test languages are not in the training data. Against a large text database index, we achieve 64–78 % precision at the point of 100% recall. This is a relative improvement of 22% on the simple edit distance.     

Construction of query concepts based on feature clustering of documents

In Information Retrieval, since it is hard to identify users’ information needs, many approaches have been tried to solve this problem by expanding initial queries and reweighting the terms in the expanded queries using users’ relevance judgments. Although relevance feedback is most effective when relevance information about retrieved documents is provided by users, it is not always available. Another solution is to use correlated terms for query expansion. The main problem with this approach is how to construct the term-term correlations that can be used effectively to improve retrieval performance. In this study, we try to construct query concepts that denote users’ information needs from a document space, rather than to reformulate initial queries using the term correlations and/or users’ relevance feedback. To form query concepts, we extract features from each document, and then cluster the features into primitive concepts that are then used to form query concepts. Experiments are performed on the Associated Press (AP) dataset taken from the TREC collection. The experimental evaluation shows that our proposed framework called QCM (Query Concept Method) outperforms baseline probabilistic retrieval model on TREC retrieval.