This paper compares several indexing methods for person names extracted from text, developed for an information retrieval system with requirements for fast approximate matching of noisy and multicultural Romanized names. Such matching algorithms are computationally expensive and unacceptably slow when used without an indexing or blocking step. The goal is to create a small candidate pool containing all the true matches that can be exhaustively searched by a more effective but slower name comparison method. In addition to dramatically faster search, some of the methods evaluated here led to modest gains in effectiveness by eliminating false positives. Four indexing techniques using either phonetic keys or substrings of name segments, with and without name segment stopword lists, were combined with three name matching algorithms. On a test set of 700 queries run against 70K noisy and multicultural names, the best-performing technique took just 2.1% as long as a naive exhaustive search and increased F1 by 3 points, showing that an appropriate indexing technique can increase both speed and effectiveness.

This paper describes the development and evaluation of enhancements to the specialized information retrieval capabilities of a multimodal reporting system. The system enables collection and dissemination of information through a distributed data architecture by allowing users to input free text documents, which are indexed for subsequent search and retrieval by other users. This unstructured data entry method is essential for users of this system, but it requires an intelligent support system for processing queries against the data. The system, known as TIGR (Tactical Ground Reporting), allows keyword searching and geospatial filtering of results, but lacked the ability to efficiently index and search person names and perform approximate name matching. To improve TIGR’s ability to provide accurate, comprehensive results for queries on person names we iteratively updated existing entity extraction and name matching technologies to better align with the TIGR use case. We evaluated each version of the entity extraction and name matching components to find the optimal configuration for the TIGR context, and combined those pieces into a named entity extraction, indexing, and search module that integrates with the current TIGR system. By comparing system-level evaluations of the original and updated TIGR search processes, we show that our enhancements to personal name search significantly improved the performance of the overall information retrieval capabilities of the TIGR system.

We have analyzed system rankings for person name search algorithms using a data set for which several versions of ground truth were developed by employing different means of resolving adjudicator conflicts. Thirteen algorithms were ranked by F-score, using bootstrap resampling for significance testing, on a dataset containing 70,000 romanized names from various cultures. We found some disagreement among the four adjudicators, with kappa ranging from 0.57 to 0.78. Truth sets based on a single adjudicator, and on the intersection or union of positive adjudications produced sizeable variability in scoring sensitivity - and to a lesser degree rank order - compared to the consensus truth set. However, results on truth sets constructed by randomly choosing an adjudicator for each item were highly consistent with the consensus. The implication is that an evaluation where one adjudicator has judged each item is nearly as good as a more expensive and labor-intensive one where multiple adjudicators have judged each item and conflicts are resolved through voting.

This paper describes the development of a ground truth dataset of culturally diverse Romanized names in which approximately 70,000 names are matched against a subset of 700. We ran the subset as queries against the complete list using several matchers, created adjudication pools, adjudicated the results, and compiled two versions of ground truth based on different sets of adjudication guidelines and methods for resolving adjudicator conflicts. The name list, drawn from publicly available sources, was manually seeded with over 1500 name variants. These names include transliteration variation, database fielding errors, segmentation differences, incomplete names, titles, initials, abbreviations, nicknames, typos, OCR errors, and truncated data. These diverse types of matches, along with the coincidental name similarities already in the list, make possible a comprehensive evaluation of name matching systems. We have used the dataset to evaluate several open source and commercial algorithms and provide some of those results.

This paper describes a Name Matching Evaluation Laboratory that is a joint effort across multiple projects. The lab houses our evaluation infrastructure as well as multiple name matching engines and customized analytical tools. Included is an explanation of the methodology used by the lab to carry out evaluations. This methodology is based on standard information retrieval evaluation, which requires a carefully-constructed test data set. The paper describes how we created that test data set, including the “ground truth” used to score the systems’ performance. Descriptions and snapshots of the lab’s various tools are provided, as well as information on how the different tools are used throughout the evaluation process. By using this evaluation process, the lab has been able to identify strengths and weaknesses of different name matching engines. These findings have led the lab to an ongoing investigation into various techniques for combining results from multiple name matching engines to achieve optimal results, as well as into research on the more general problem of identity management and resolution.