In digital humanities projects, particularly for historical research and cultural heritage, GIS has played an increasingly important role. However, most implementations have concentrated on displays which ignore the temporal dimension or express it as multiple snapshots for fixed or periodic points in time. Our project concentrates on historical biography and expresses a biography as a sequence of life events with in time and space. We utilize named entity recognition and extraction to automatically mark up biographies so that they can be displayed as dynamic maps. In so doing, contextual features and related happenings and people can be overlaid to facilitate serendipitous discovery of unanticipated and seemingly unrelated connections.

This demonstration utilizes a geographic information system interface to display multilingual news documents in time and space by extracting place names from text and matching them to a multilingual multi-script gazetteer which identifies the latitude and longitude of the location.

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Vocabulary incompatibilities arise when the terms used to index a document collection are largely unknown, or at least not well-known to the users who eventually search the collection. No matter how comprehensive or well-structured the indexing vocabulary, it is of little use if it is not used effectively in query formulation. This paper demonstrates that techniques for mapping user queries into the controlled indexing vocabulary have the potential to radically improve document retrieval performance. We also show how the use of controlled indexing vocabulary can be employed to achieve performance gains for collection selection. Finally, we demonstrate the potential benefit of combining these two techniques in an interactive retrieval environment. Given a user query, our evaluation approach simulates the human user's choice of terms for query augmentation given a list of controlled vocabulary terms suggested by a system. This strategy lets us evaluate interactive strategies without the need for human subjects.

It is generafly believed that words, rather than characters, should be the smallest indexing unit for Chinese text retrieval systems, and that it is essential to have a comprehensive Chinese dictionary or lexicon for Chhmse text retrieval systems to do well.Chinese text has no delimiters to mark woni boundaries.As a result, any text retrieval systems that build word-based indexes need to segment text into words.We implemented several statistical and dictionary-hazed word segmentation methods to study the effect on retrieval effectiveness of different segmentation methods using the TREC-S Chinese test collection and topics.The results show that, for all three sets of queries, the simple bigram indexing and the purely statistical word segmentation perform better than the popular dictionary-based maximum matching method with a dictionary of 138,955 entries.

Empirical studies of retrieval performance have shown a tendency for Precision to decline as Recall increases. This article examines the nature of the relationship between Precision and Recall. The relationships between Recall and the number of documents retrieved, between Precision and the number of documents retrieved, and between Precision and Recall are described in the context of different assumptions about retrieval performance. It is demonstrated that a tradeoff between Recall and Precision is unavoidable whenever retrieval performance is consistently better than retrieval at random. More generally, for the Precision–Recall trade-off to be avoided as the total number of documents retrieved increases, retrieval performance must be equal to or better than overall retrieval performance up to that point. Examination of the mathematical relationship between Precision and Recall shows that a quadratic Recall curve can resemble empirical Recall–Precision behavior if transformed into a tangent parabola. With very large databases and/or systems with limited retrieval capabilities there can be advantages to retrieval in two stages: Initial retrieval emphasizing high Recall, followed by more detailed searching of the initially retrieved set, can be used to improve both Recall and Precision simultaneously. Even so, a tradeoff between Precision and Recall remains. © 1994 John Wiley & Sons, Inc.

The goal of a probabilistic retrieval system design is to rank the elements of the search universe in descending order of their estimated probability of usefulness to the user. Previously explored methods for computing such a ranking have involved the use of statistical independence assumptions and multiple regression analysis on a learning sample. In this paper these techniques are recombined in a new way to achieve greater accuracy of probabilistic estimate without undue additional computational complexity. The novel element of the proposed design is that the regression analysis be carried out in two or more levels or stages. Such an approach allows composite or grouped retrieval clues to be analyzed in an orderly manner -- first within groups, and then between. It compensates automatically for systematic biases introduced by the statistical simplifying assumptions, and gives rise to search algorithms of reasonable computational efficiency.

A general software package was built to access individual data records stored on an IBM-1360 photo-digital mass storage device with a current on-line capacity of 50 billion characters (bytes) and an infinitely extensible off-line capacity. An existing data base management system was used to maintain the pointers to the data on the mass storage device and to store the controls for the data driven interactive code. Existing data dictionaries used for sequentially processing the data bases mere stored in the DBMS and used to display individual data items within the retrieved records.