docgen.templates.backgroundlinking20.template Maven / Gradle / Ivy

Go to download
# Anserini Regressions: TREC 2020 News Background Linking

**Models**: various bag-of-words approaches

This page describes regressions for the background linking task in the [TREC 2020 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 *v3*](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.backgroundlinking20.txt`](https://github.com/castorini/anserini-tools/tree/master/topics-and-qrels/topics.backgroundlinking20.txt): topics for the background linking task of the TREC 2020 News Track
+ [`qrels.backgroundlinking20.txt`](https://github.com/castorini/anserini-tools/tree/master/topics-and-qrels/qrels.backgroundlinking20.txt): qrels for the background linking task of the TREC 2020 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}