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https://github.com/dexter/dexter
Dexter is a framework that implements some popular algorithms and provides all the tools needed to develop any entity linking technique.
https://github.com/dexter/dexter
Last synced: about 2 months ago
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Dexter is a framework that implements some popular algorithms and provides all the tools needed to develop any entity linking technique.
- Host: GitHub
- URL: https://github.com/dexter/dexter
- Owner: dexter
- License: apache-2.0
- Created: 2013-02-08T10:20:48.000Z (over 11 years ago)
- Default Branch: master
- Last Pushed: 2017-04-09T22:29:45.000Z (over 7 years ago)
- Last Synced: 2024-06-20T05:29:26.709Z (3 months ago)
- Language: Java
- Homepage: http://www.dxtr.it
- Size: 16.8 MB
- Stars: 204
- Watchers: 24
- Forks: 56
- Open Issues: 25
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
README
The entity linking (aka Wikification) task aims
at identifying all the small text fragments in a document referring
to an entity contained in a given knowledge base, e.g., Wikipedia.
The annotation is usually organized in three tasks:
1. Given an input
document the first task consists in discovering the fragments that could
refer to an entity.
2. Since a mention could refer to multiple entities,
it is necessary to perform a disambiguation step, where the correct entity
is selected tamong the candidates.
3. Finally, discovered entities are ranked
by some measure of relevance.
Many entity linking algorithms have been proposed,
but unfortunately only a few authors have released the source code or some APIs.
As a result, evaluating today the performance of a method on a single subtask,
or comparing different techniques is difficult.
For these reasons we implemented Dexter, a framework that implements
some popular algorithms and provides all the tools needed to develop
any entity linking technique. We believe that a shared framework is
fundamental to perform fair comparisons and improve the state of the art.
For more information about the team and the framework.
please refer to the [website](http://dexter.isti.cnr.it).
A simple demo is also available on the website. The tagger used in the demo
is our implemented version of [TAGME](http://tagme.di.unipi.it), please
note that some annotations could be different since the two frameworks use
different Wikipedia dumps and different methods for extracting the spots.
# Ok, but I don't have time to understand how things _really_ work, I just want to use it
Hiha! just download the model with the binaries (model was generated from the [English Wikipedia dump 07/07/2014](http://dumps.wikimedia.org/enwiki/20140707/enwiki-20140707-pages-articles.xml.bz2)):
wget http://hpc.isti.cnr.it/~ceccarelli/dexter2.tar.gz
tar -xvzf dexter2.tar.gz
cd dexter2
java -Xmx4000m -jar dexter-2.1.0.jar
And then visit on your browser the address [http://localhost:8080/dexter-webapp/dev]().
It will show the available REST-API. Enjoy!
# Actually I do have some time and I like compiling things.
Dexter's pom includes the [json-wikipedia](https://github.com/diegoceccarelli/json-wikipedia) project as a project dependency. The first thing you need to do is make sure that this is also pulled to the project's root. To do this, just use a command like `git pull --recurse-submodules` to be sure that all dependencies are downloaded.
After that, a simple `mvn package` compiles the project. Note that the `maven` command line tool that comes bundled with some versions of linux (e.g. Ubuntu) has a problem fetching dependencies for `org.apache.commons.lang`. To avoid this error, just make sure that you have an updated version of maven (>= `3.0.5` would do).
# Cool, I want to know more!
The following sections describe a bit more in detail how the framework works.
# Developing
You can use Dexter in several different ways:
* Using the Rest API, after downloading the jar and its resources;
* Using the Java API;
* Jsonp API + JQuery plugin;
* Python Client.
## Compiling and Installing
In order to install and compile Dexter just run the following commands:
## Start a REST Server
### Download the Resources
Click on this [http://hpc.isti.cnr.it/~ceccarelli/dexter2.tar.gz](link) for downloading Dexter.
The archive requires around 2.5 Gigabytes, and contains the
Dexter binary code (''dexter2.1.0.jar'') and the
model used by Dexter for annotating.
The current model is generated from the 07/07/2014 English
Wikipedia dump, available [http://dumps.wikimedia.org/enwiki/20140707/enwiki-20140707-pages-articles.xml.bz2](here).
(we plan to release updated models for English and other languages).
Once the download is finished, untar the package, and from the directory ''dexter2'', just run
java -Xmx3000m -jar dexter-2.1.0.jar
(you will need at least 3G of ram and Java 7).
The framework should be available in few seconds at the address:
http://localhost:8080/
The REST-api is available at:
http://localhost:8080/dexter-webapp/dev
First query will take a bit because Dexter will have to load all the model in main
memory.
## Configuring Dexter
Dexter 2 is configured through an XML file `dexter-conf.xml`.
Don't worry, is not to hard to understand ;), by default Dexter
searches for the configuration file in the root directory.
### Set the Dexter model
In the beginning of the file:
en
en
FIXME
it
data/it
replace the path in `FIXME` with the absolute or relative path to the folder that contains the dexter model. If you download
the model from the website, the folder is called `en-model-20140707`. Once you setup the folder just start the server running the command:
java -Xmx3000m -jar dexter-2.1.0.jar
### Use the client
Once you performed the installation, you will have to add to your maven
project the dependency:
it.cnr.isti.hpc
dexter-client
2.1.0
Then will be able to call the REST api from your have project
using the DexterRestClient as in the following example:
DexterRestClient client = new DexterRestClient("http://localhost:8080/dexter-webapp/api/rest");
AnnotatedDocument ad = client
.annotate("Dexter is an American television drama series which debuted on Showtime on October 1, 2006. The series centers on Dexter Morgan (Michael C. Hall), a blood spatter pattern analyst for the fictional Miami Metro Police Department (based on the real life Miami-Dade Police Department) who also leads a secret life as a serial killer. Set in Miami, the show's first season was largely based on the novel Darkly Dreaming Dexter, the first of the Dexter series novels by Jeff Lindsay. It was adapted for television by screenwriter James Manos, Jr., who wrote the first episode. ");
System.out.println(ad);
SpottedDocument sd = client
.spot("Dexter is an American television drama series which debuted on Showtime on October 1, 2006. The series centers on Dexter Morgan (Michael C. Hall), a blood spatter pattern analyst for the fictional Miami Metro Police Department (based on the real life Miami-Dade Police Department) who also leads a secret life as a serial killer. Set in Miami, the show's first season was largely based on the novel Darkly Dreaming Dexter, the first of the Dexter series novels by Jeff Lindsay. It was adapted for television by screenwriter James Manos, Jr., who wrote the first episode. ");
System.out.println(sd);
ArticleDescription desc = client.getDesc(5981816);
System.out.println(desc);
If you have installed the code with `mvn install`, and run the server on your machine, you can create an instance of the client with the this address
DexterRestClient client = new DexterRestClient(
"http://localhost:8080/dexter-webapp/api/rest");
### More details about the configuration file
The configuration file mainly contains details on the paths of the model files, and it allows to register plugin and to configure the tagger.
More in detail, it allows to register:
#### Spotters
The component that detects mentions in the text, and maps each mention to a list of candidate entities, you can register a spotter writing inside the ``tag:
```
wiki-dictionary
wiki-dictionary
it.cnr.isti.hpc.dexter.spotter.DictionarySpotter
```
In this example, the standard dictionary spotter is registered with the name `wiki-dictionary` and set as default dictionary, you can also register other dictionaries, for example:
```
wiki-dictionary
wiki-dictionary
it.cnr.isti.hpc.dexter.spotter.DictionarySpotter
my-dictionary
com.mycompany.Myspotter
```
Here we registered a new spotter with the name `my-dictionary`. You can create a new spotter extending the abstract class `AbstractSpotter` and then adding it to the classpath of the dexter webapp (you can put the jar in the lib folder, or more easily, install the jar with maven and add the dependency in the `dexter-webapp/pom.xml`).
At runtime, the rest functions allow you to specify the spotter that you want to use with the parameter `spt`. If you don't specify the name of the spotter, `default` is used.
#### SpotFilters
A spot filter allows you to filter the mentions produced by the Spotter before they are sent to the Disambiguator. For example, if you would like to filter short spots, or spots with low probability etc etc, still you can write your filters or use the filters provided by Dexter. As for the spotter, filters must be registered and then you can use them in a spotter.
For example:
```
probability-filter
it.cnr.isti.hpc.dexter.spotter.filter.SpotProbabilityFilter
lp
0.02
```
Registers a filter that removes mentions with low probability to be links to entities (note the parameter `lp`).
You could register two different filters:
```
f0.02
it.cnr.isti.hpc.dexter.spotter.filter.SpotProbabilityFilter
lp
0.02
f0.5
it.cnr.isti.hpc.dexter.spotter.filter.SpotProbabilityFilter
lp
0.5
```
Here the first filter `f0.02` removes all the spots with link probability lower than 0.02, while the second `f0.5` removes all the spots with link probability lower than 0.5.
Then you can set several spot filters with a spotter:
```
wiki-dictionary
it.cnr.isti.hpc.dexter.spotter.DictionarySpotter
probability-filter
overlaps-filter
```
The `overlaps-filter` removes spots that overlap in the text. Filters are applied in the order they appear in the configuration.
#### Disambiguation functions
As for the spotters, you can register a disambiguation function and call it from the rest interface.
tagme
it.cnr.isti.hpc.tagme.Tagme
window-size
30
epsilon
0.7
Tagme and Wikiminer are provided as a dependency, in dexter-code you can find a disambiguator that always selects the most probable entity for a mention.
If you want to add a disambiguator, just implement the interface `Disambiguator` (in `dexter-core`).
The interface has two methods:
public EntityMatchList disambiguate(DexterLocalParams localParams,
SpotMatchList sml);
public void init(DexterParams dexterParams,
DexterLocalParams dexterModuleParams);
Init will be called just once when the disambiguator object is created, if there are params in the disambiguator snippet
(as for tagme) these params will be passed in the `dexterModuleParams` variable. At run time, when you annotate a document, the parameter that you put in the post/get query will be pushed in the `localParams` object, so you can play with the parameters
of you disambiguator. This works also for spotter and spot filters.
## Citation
If you use the Dexter framework, you must cite:
> Ceccarelli, D., Lucchese, C., Orlando, S., Perego, R., & Trani, S.
> Dexter: an open source framework for entity linking.
> *In Proceedings of the sixth international workshop on Exploiting semantic annotations in information retrieval (pp. 17-20). ACM.* DOI: http://dx.doi.org/10.1145/2513204.2513212
Bibtex format:
@inproceedings{DBLP:conf/cikm/CeccarelliLOPT13a,
author = {Diego Ceccarelli and
Claudio Lucchese and
Salvatore Orlando and
Raffaele Perego and
Salvatore Trani},
title = {Dexter: an open source framework for entity linking},
booktitle = {ESAIR'13, Proceedings of the Sixth International Workshop on Exploiting
Semantic Annotations in Information Retrieval, co-located with {CIKM}
2013, San Francisco, CA, USA, October 28, 2013},
pages = {17--20},
year = {2013},
crossref = {DBLP:conf/cikm/2013esair},
url = {http://doi.acm.org/10.1145/2513204.2513212},
doi = {10.1145/2513204.2513212},
timestamp = {Thu, 15 May 2014 15:51:38 +0200},
biburl = {http://dblp.uni-trier.de/rec/bib/conf/cikm/CeccarelliLOPT13a},
bibsource = {dblp computer science bibliography, http://dblp.org}
}