{"id":364619,"date":"2017-02-16T15:59:10","date_gmt":"2017-02-16T23:59:10","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=364619"},"modified":"2024-09-04T14:05:37","modified_gmt":"2024-09-04T21:05:37","slug":"luandri-clean-lua-interface-indri-search-engine","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/luandri-clean-lua-interface-indri-search-engine\/","title":{"rendered":"Luandri: a Clean Lua Interface to the Indri Search Engine"},"content":{"rendered":"

In recent years, the information retrieval (IR) community has witnessed the first successful applications of deep neural network models to short-text matching and ad-hoc retrieval. It is exciting to see the research on deep neural networks and IR converge on these tasks of shared interest. However, the two communities have less in common when it comes to the choice of programming languages. Indri, an indexing framework popularly used by the IR community, is written in C++, while Torch, a popular machine learning library for deep learning, is written in the light-weight scripting language Lua. To bridge this gap, we introduce Luandri (pronounced “laundry<\/em>“), a simple interface for exposing the search capabilities of Indri to Torch models implemented in Lua.<\/p>\n","protected":false},"excerpt":{"rendered":"

In recent years, the information retrieval (IR) community has witnessed the first successful applications of deep neural network models to short-text matching and ad-hoc retrieval. It is exciting to see the research on deep neural networks and IR converge on these tasks of shared interest. However, the two communities have less in common when it 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