Jul 25, 2020 · We investigate causal retrieval, which involves retrieving a set of documents that describe a set of potential causes leading to an effect specified in the ...

Jul 25, 2020 · To address this, we propose a feedback model to estimate a distribution of terms which are relatively infrequent but associated with high.

Jul 25, 2020 · To address this, we propose a feedback model to estimate a distribution of terms which are relatively infrequent but associated with high ...

Jul 25, 2020 · We believe that one of the products of this task is lists of queries and relevant documents that defines the connections between documents ...

Jul 25, 2020 · To address this, we propose a feedback model to estimate a distribution of terms which are relatively infrequent but associated with high.

Jul 5, 2020 · Retrieving Potential Causes From a Query Event. July 5, 2020. Debasis Ganguly. Charles Jochim. DwaipayanRoy. Mandar Mitra. Suchana Datta.

Mar 6, 2024 · We introduce the task of backtracing, in which systems retrieve the text segment that most likely caused a user query. We formalize three real- ...

Nov 8, 2024 · This work uses a Convolutional Neural Network (CNN) and a Transformer-based model to give an in-depth structure for causality-driven document classification.

The path of the Lucene index on which the retrieval will be performed. 2. The path of the query file in complete XML format. 3. Directory path in which the .res ...

The CAusality-based Information Retrieval (CAIR) track at FIRE 2021 focuses on the task of retrieving potentially relevant documents in response to a query ...