Entity-Based Retrieval in Shared Semi-Structured Information Spaces

Proceedings of CIKIM. 1996
Entity-Based Retrieval in Shared Semi-Structured Information Spaces
Abstract

Semi-structured information sharing systems are gaining in popularity because they allow users to easily create shared collections of textual documents, organized by a common set of fields. Unfortunately, in a large organization this freedom can result in an unwieldy space of shared information that is difficult to retrieve.

Standard tools like full-text search do not alleviate the problem, in part because they do not make any use of the structure within each document collection. In this paper, we describe an approach that goes beyond full-text search by taking advantage of both the structure of the document collections and a knowledge of what information types are important within the organization sharing the information.

We present an implemented indexing/browsing system called Notes Explorer that allows users to browse for entities (companies, people, etc.) across a large semi-structured information space. Notes Explorer incorporates three key components:

(1) automatic classification of document fields to recognize common entity and document collection types;

(2) entity-based browsing over multiple document collections, with type-dependent normalization;

and

(3) content-based filtering of browse results.

Another publication from the same category: Machine Learning and Data Science

WWW '17 Perth Australia April 2017

Drawing Sound Conclusions from Noisy Judgments

David Goldberg, Andrew Trotman, Xiao Wang, Wei Min, Zongru Wan

The quality of a search engine is typically evaluated using hand-labeled data sets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too large to be created by the best annotators, and so less accurate labels (e.g. from crowdsourcing) must be used. This introduces errors in the labels, and thus errors in standard precision metrics (such as P@k and DCG); the lower the quality of the judge, the more errorful the labels, consequently the more inaccurate the metric. We introduce equations and algorithms that can adjust the metrics to the values they would have had if there were no annotation errors.

This is especially important when two search engines are compared by comparing their metrics. We give examples where one engine appeared to be statistically significantly better than the other, but the effect disappeared after the metrics were corrected for annotation error. In other words the evidence supporting a statistical difference was illusory, and caused by a failure to account for annotation error.

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