VerbAtlas is funded by the MOUSSE ERC Grant no.726487 and the ELEXIS project no.731015 under the European Union's Horizon 2020 research and innovation programme.
InVeRo-SRL is part of the Universal Semantic Annotator (USeA) project funded by the European Language Grid (ELG), Project Number 825627.
VerbAtlas and InVeRo-SRL are both licensed under the CC BY-NC-SA 4.0 License
To encourage the integration of Semantic Role Labeling into downstream applications, the Model API offers a simple solution for out-of-the-box role labeling by providing an interface to a full end-to-end state-of-the-art pretrained model (Conia et al., 2021).
Returns Semantic Role Labeling annotations for a given sentence. In particular, it identifies and disambiguates the predicates in a sentence, and, for each predicate, it identifies and labels its arguments.
POST
https://verbatlas.org/api/model
| Name | Type | Description |
|---|---|---|
| documents | List<Document> | A list of documents to disambiguate. |
| Name | Type | Description |
|---|---|---|
| text | String | The document to disambiguate. |
| lang | String | Language of the document. |
curl -X 'POST' \
'https://verbatlas.org/api/model' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '[
{"text":"The quick brown fox jumps over the lazy dog.", "lang":"EN"},
{"text":"I walked along the river bank.", "lang":"EN"}
]'
The Web interface of InVeRo-XL supports the following 12 languages.
The offline version of InVeRo-XL supports the following 45 languages. Download InVeRo-XL here.
Where to download? Download InVeRo-XL here.
We release different types of Docker images depending on the annotation style you want to use and also depending on the hardware you have (i.e. CPU or GPU). In particular:
In order to run InVeRo-XL offline, you need to install Docker. Please refer to its official documentation to install it on your system.
To run a local instance of InVeRo-XL, users are required to perform a one-time setup to load one of the available images. Let's say that we want to load invero-xl-span-cpu:
#!/bin/bash docker load -i invero-xl-span-cpu-2.0.0.tar
After that, InVeRo-XL can be started by running the following commands:
#!/bin/bash PORT=12345 LANGUAGES="EN FR IT ZH" docker run \ --name invero-xl-span-cpu \ -p $PORT:80 \ -e LANGUAGES=$LANGUAGES \ invero-xl-span-cpu:2.0.0
This will run an invero-xl-span instance on CPU on port number 12345 loading the preprocessing models for English, French, Italian and Chinese.
If you need GPU support, you can load the cuda version of an image, for example invero-xl-span-cuda.
Before using a cuda image, you have to load it, just like the CPU image:
#!/bin/bash docker load -i invero-xl-span-cuda-2.0.0.tar
Then you can launch InVeRo-XL through the docker run command, with an additional flag, --gpus, for example:
#!/bin/bash PORT=12345 LANGUAGES="EN FR IT ZH" docker run \ --name invero-xl-span-cuda \ --gpus all \ -p $PORT:80 \ -e LANGUAGES=$LANGUAGES \ invero-xl-span-cuda:2.0.0
For more info about how to enable GPU support in Docker you can refer to the official documentation.
InVeRo-XL exposes an end-point named /api/model. The endpoint accepts POST requests with a JSON body, containing a list of documents. For each document, two parameters must be specified:
Each request to the Model API returns a JSON response containing a list of output objects, one for each input document, containing the automatic annotations according to each of the 7 linguistic inventories.
Let's try with a simple example. We want to disambiguate the words in the following sentence "The quick brown fox jumps over the lazy dog.":
curl -X 'POST' \
'http://127.0.0.1/api/model' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '[
{"text":"The quick brown fox jumps over the lazy dog.", "lang":"EN"},
]'
If everything went right, the output should be similar to:
There are a few environment variables that can be passed to the docker container:
A list of languages, separated by whitespaces. This list indicates the preprocessing models to load with InVeRo-XL. The SRL model of InVeRo-XL supports more tha 40 languages (see the list of supported languages), but each language requires loading its own preprocessing model. We suggest loading only those languages you need so as to reduce the memory footprint of InVeRo-XL.
LANGUAGES="EN"
Launching InVeRo-XL to disambiguate text in English, French and Italian.
docker run --name {$CONTAINER_NAME} -e LANGUAGES="EN FR IT" {$IMAGE_NAME}
When loading many languages, InVeRo-XL has to download multiple preprocessing models which may require a long time. If your container crashes, try to increase this value.
TIMEOUT="500"
Setting the TIMEOUT value to 1200.
docker run --name {$CONTAINER_NAME} -e TIMEOUT="1200" {$IMAGE_NAME}
Can be used to limit the number of simultaneously running processes (instances of InVeRo-XL).
MAX_WORKERS=1
Setting the number of InVeRo-XL instances to 4
docker run --name {$CONTAINER_NAME} -e MAX_WORKERS="4" {$IMAGE_NAME}
InVeRo-XL checks how many CPU cores are available in the current server running your container. It will set the number of workers to the number of CPU cores multiplied by this value.
WORKERS_PER_CORE=1
docker run --name {$CONTAINER_NAME} -e WORKERS_PER_CORE="3" {$IMAGE_NAME}
If you set the value to 3 in a server with 2 CPU cores, it will run 6 worker processes.
docker run --name {$CONTAINER_NAME} -e WORKERS_PER_CORE="0.5" {$IMAGE_NAME}
The log level for Gunicorn.
debug info warning error critical
LOG_LEVEL="info"
docker run --name {$CONTAINER_NAME} -e LOG_LEVEL="info" {$IMAGE_NAME}
Auto-generated interactive documentation for the API (thanks to FastAPI).
http://127.0.0.1:PORT/docs
where PORT is the port number you specify when starting up InVeRo-XL.
The Resource API provides a RESTful interface to easily link predicate-level information, e.g., predicate lemmas and/or predicate senses, to VerbAtlas specific features, e.g., frames and semantic roles.
Given a predicate (a lemma, a WordNet sense key, a WordNet synset ID or a BabelNet synset ID), it returns the corresponding VerbAtlas frame(s).
GET or POST
/api/verbatlas/predicate?lemma=lemma
/api/verbatlas/predicate?wordnetSenseKey=wordnetSenseKey
/api/verbatlas/predicate?wordnetID=wordnetID
/api/verbatlas/predicate?babelnetID=babelnetID
| Name | Type | Description |
|---|---|---|
| lemma | String | A predicate lemma, e.g., eat, drink, write, read... |
| wordnetSenseKey | String | A WordNet sense key, e.g., eat%2:34:00:: |
| wordnetID | String | A WordNet synset ID, e.g., wn:01168468v. |
| babelnetID | String | A BabelNet synset ID, e.g., bn:00087460v |
https://verbatlas.org/api/verbatlas/predicate?lemma=eat
Given a VerbAtlas frame ID, e.g. va:0001f, it returns all the predicates that belong to the given VerbAtlas frame.
GET or POST
/api/verbatlas/frame?verbatlasFrameID=verbatlasFrameID
| Name | Type | Description |
|---|---|---|
| verbatlasFrameID | String | A VerbAtlas frame ID, e.g., va:0001f |
https://verbatlas.org/api/verbatlas/frame?verbatlasFrameID=va:0001f
Given a PropBank predicate sense (e.g. eat.01) it returns the corresponding VerbAtlas frame (e.g. va:0051f/EAT_BITE).
GET or POST
/api/verbatlas/align/sense?propbankSenseID=propbankSenseID
| Name | Type | Description |
|---|---|---|
| propbankSenseID | String | A PropBank sense, e.g. eat.01 |
https://verbatlas.org/api/verbatlas/align/sense?propbankSenseID=eat.01
Given a PropBank predicate sense (e.g. eat.01) it returns the mapping from its PropBank semantic roles to the corresponding VerbAtlas semantic roles.
GET or POST
/api/verbatlas/align/roles?propbankSenseID=propbankSenseID
| Name | Type | Description |
|---|---|---|
| propbankSenseID | String | A PropBank sense, e.g. eat.01 |
https://verbatlas.org/api/verbatlas/align/roles?propbankSenseID=eat.01
If you use InVeRo-XL, please cite InVeRo-XL: Making Cross-Lingual Semantic Role Labeling Accessible with Intelligible Verbs and Roles:
@inproceedings{conia-etal-2021-invero-xl,
title = {{InVeRo-XL}: Making Cross-Lingual {S}emantic {R}ole {L}abeling Accessible with {I}ntelligible {V}erbs and {R}oles},
author = {Conia, Simone and Orlando, Riccardo and Brignone, Fabrizio and Cecconi, Francesco and Navigli, Roberto},
booktitle = {Proceedings of EMNLP},
year = {2021},
month = {nov},
address = {Punta Cana, Dominican Republic},
url = {https://aclanthology.org/2021.emnlp-demo.36/},
}
InVeRo-XL is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
This project is part of the Universal Semantic Annotator (USeA) project funded by the European Language Grid (ELG), Project Number 825627.