This Java toolkit enables the development of probabilistic search engines. Its functionality includes the ability to build models, identify correlations, and make predictions.
Terrier is packed with a range of advanced features. It has parameter-free probabilistic retrieval approaches such as Divergence from Randomness models. The software also provides automatic query expansion/re-formulation methodologies and efficient data compression techniques.
One of the key benefits of Terrier is its powerful proof-of-concept desktop search application. This comes with screenshots that showcase its capabilities. The software also has full TREC capabilities that allow it to index, query, and evaluate standard TREC collections such as AP, WSJ, WT10G, .GOV, and .GOV2.
Terrier is written in Java, and it has already been used successfully for adhoc retrieval, web search, and cross-language retrieval. It can be used in both centralised and distributed settings, and it is currently being used for running various applications.
Overall, Terrier is a powerful software that can be used to build fast, efficient, and customisable searching applications. Its modular nature, advanced features, and powerful proof-of-concept desktop search application make it a great choice for developers and companies looking to build their search engine capabilities.
Version 2.2: N/A