On Correcting Misspelled Queries in Email Search
Authors: Abhijit Bhole, Raghavendra Udupa
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We do a systematic empirical study of Sp EQ on two public domain email corpora. We compare the performance of Spe Q against two baselines a source-channel based ranking approach and the spelling correction feature of a popular email service. Our experiments show that Sp EQ gives significant improvement in accuracy over the two baselines. We did a feature ablative study and found that each class of feature functions contributed to the effectiveness of our approach. |
| Researcher Affiliation | Industry | Abhijit Bhole and Raghavendra Udupa Microsoft Research 9 Lavelle Road, Bangalore 560001 {a-abhina, raghavu}@microsoft.com |
| Pseudocode | No | The paper describes the components of its ranking framework and feature functions but does not include a pseudocode block or algorithm. |
| Open Source Code | No | The paper does not provide any information about the availability of its source code. |
| Open Datasets | Yes | Data We used two public domain email corpora for our experiments: PALIN: About 15, 000 mails of the former Alaskan governor Sarah Palin (from the period December 2006 to September 2008 made public in the year 2011) in two folders Inbox and Sent Mail. ENRON: All mails (about 2100) in the All Documents folder of rogers-b, an employee of Enron corporation. |
| Dataset Splits | No | The paper mentions training data but does not specify any explicit training/validation/test splits, percentages, or absolute counts for reproduction. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes the components of its Sp EQ framework and feature functions but does not provide specific experimental setup details such as hyperparameters (e.g., learning rate, batch size, number of epochs) or other system-level training settings. |