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Integrate link graph into main wiki pipeline #429
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doc/guide/caspar.md
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| ## Preparing the training data | ||
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| The LDC2013T19 OntoNotes 5 corpus is needed to produce the training data for | ||
| CASPAR. This is licensed by LDC and you need a LDC license to use the corpus: |
doc/guide/caspar.md
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| ## Pre-trained word embeddings | ||
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| The CASPAR parser uses pre-trained word embeddings which can be download from |
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download -> downloaded
doc/guide/install.md
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| with Python 3 you can install a pre-built wheel: | ||
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| ``` | ||
| sudo -H pip3 install http://www.jbox.dk/sling/sling-2.0.0-py3-none-linux_x86_64.whl | ||
| ``` | ||
| and download the pre-trained model: | ||
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| You can test the installing by trying to import the `sling` package: |
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installing -> installation
doc/guide/wikiflow.md
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| graph that is stored in the `/w/item/links` property for each item. The link | ||
| graph is built over all the Wikipedias being processed. The fan-in, | ||
| i.e. the number of links to the item, is also computed and stored in the | ||
| `/w/item/popularity` property. Tge popularity count also includes the number of |
doc/guide/wikiflow.md
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| graph is built over all the Wikipedias being processed. The fan-in, | ||
| i.e. the number of links to the item, is also computed and stored in the | ||
| `/w/item/popularity` property. Tge popularity count also includes the number of | ||
| times the item is a fact taget in other items. |
sling/nlp/wiki/wikipedia-links.cc
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| Text id = store->FrameId(target); | ||
| if (id.empty()) continue; | ||
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| if (!store->IsFrame(target)) continue; |
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Looks like this line might be redundant.
sling/nlp/wiki/wikipedia-links.cc
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| if (id.empty()) continue; | ||
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| if (!store->IsFrame(target)) continue; | ||
| accumulator_.Increment(id); |
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indention off by one
anders-sandholm
approved these changes
Nov 15, 2019
ringgaard
commented
Nov 15, 2019
doc/guide/caspar.md
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| ## Preparing the training data | ||
|
|
||
| The LDC2013T19 OntoNotes 5 corpus is needed to produce the training data for | ||
| CASPAR. This is licensed by LDC and you need a LDC license to use the corpus: |
doc/guide/caspar.md
Outdated
|
|
||
| ## Pre-trained word embeddings | ||
|
|
||
| The CASPAR parser uses pre-trained word embeddings which can be download from |
doc/guide/install.md
Outdated
| with Python 3 you can install a pre-built wheel: | ||
|
|
||
| ``` | ||
| sudo -H pip3 install http://www.jbox.dk/sling/sling-2.0.0-py3-none-linux_x86_64.whl | ||
| ``` | ||
| and download the pre-trained model: | ||
|
|
||
| You can test the installing by trying to import the `sling` package: |
doc/guide/wikiflow.md
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| graph that is stored in the `/w/item/links` property for each item. The link | ||
| graph is built over all the Wikipedias being processed. The fan-in, | ||
| i.e. the number of links to the item, is also computed and stored in the | ||
| `/w/item/popularity` property. Tge popularity count also includes the number of |
doc/guide/wikiflow.md
Outdated
| graph is built over all the Wikipedias being processed. The fan-in, | ||
| i.e. the number of links to the item, is also computed and stored in the | ||
| `/w/item/popularity` property. Tge popularity count also includes the number of | ||
| times the item is a fact taget in other items. |
sling/nlp/wiki/wikipedia-links.cc
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| Text id = store->FrameId(target); | ||
| if (id.empty()) continue; | ||
|
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| if (!store->IsFrame(target)) continue; |
sling/nlp/wiki/wikipedia-links.cc
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| if (id.empty()) continue; | ||
|
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| if (!store->IsFrame(target)) continue; | ||
| accumulator_.Increment(id); |
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I have integrated the link graph into the main wiki pipeline. The fanin values now also includes counts from basic facts from the items.
I have also updated the documentation to use the new Myelin-based parser trainer.