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backgroundlinking19.template
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backgroundlinking19.template
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# Anserini Regressions: TREC 2019 News Background Linking
**Models**: various bag-of-words approaches
This page describes regressions for the background linking task in the [TREC 2019 News Track](http://trec-news.org/).
The exact configurations for these regressions are stored in [this YAML file](${yaml}).
Note that this page is automatically generated from [this template](${template}) as part of Anserini's regression pipeline, so do not modify this page directly; modify the template instead.
From one of our Waterloo servers (e.g., `orca`), the following command will perform the complete regression, end to end:
```
python src/main/python/run_regression.py --index --verify --search --regression ${test_name}
```
## Indexing
Typical indexing command:
```
${index_cmds}
```
The directory `/path/to/core18/` should be the root directory of the [TREC Washington Post Corpus](https://trec.nist.gov/data/wapost/), i.e., `ls /path/to/core18/`
should bring up a single JSON file.
For additional details, see explanation of [common indexing options](${root_path}/docs/common-indexing-options.md).
## Retrieval
Topics and qrels are stored [here](https://github.com/castorini/anserini-tools/tree/master/topics-and-qrels), which is linked to the Anserini repo as a submodule.
They are downloaded from NIST:
+ [`topics.backgroundlinking19.txt`](https://github.com/castorini/anserini-tools/tree/master/topics-and-qrels/topics.backgroundlinking19.txt): topics for the background linking task of the TREC 2019 News Track
+ [`qrels.backgroundlinking19.txt`](https://github.com/castorini/anserini-tools/tree/master/topics-and-qrels/qrels.backgroundlinking19.txt): qrels for the background linking task of the TREC 2019 News Track
After indexing has completed, you should be able to perform retrieval as follows:
```
${ranking_cmds}
```
Evaluation can be performed using `trec_eval`:
```
${eval_cmds}
```
## Effectiveness
With the above commands, you should be able to reproduce the following results:
${effectiveness}