#Who uses babelnet code#
#Who uses babelnet download#

Furthermore, we may have negative instances where we do not have a definition in the target language, for which the model should give a low score to all the candidate definitions. On the other hand, when evaluating the model, we are given a single definition in a source language, and we have to link it to the corresponding definition (synset) from BabelNet. Here, the model is only given a pair and the loss is computed based on the similarity score between the two definitions in a binary fashion. This is different than the evaluation at inference time. The model is trained on a binary task which consists in determining whether a given pair of definitions belong to the same synset. Instead of BERT we used multilingual encoders that have been pretrained on NLI tasks:

However, it is able to compute the similarity score between the two definitions as input, leading to a better performance, which is the main advantage of this approach. if there are N source senses and M target senses for a given lemma and pos tag, it will have to encode N*M inputs. The cross-encoder on the other hand is slower to compute since it has to encode both definitions simultaneously, ie. However since it is based solely on the embeddings independently, there is no interaction between the two definitions. if there are N source senses and M target senses for a given lemma and pos tag, it only needs to encode N+M senses. The embedding-based approach has the advantage of being able to compute the similarity score much faster, due to only encoding each definition once, ie. Cross-encoder based: This model is based on the embedding of two senses using a cross-encoder, which receives as input the two definitions simultaneously, computing the similarity score with a Dense layer.Embedding based (Bi-encoder): This model is based on the embedding of two definitions independently, and the cosine similarity between them is used as the similarity score.The REST API expects the endpoint at, but it can be modified at:ĭocker exec dict_api bash -c 'python3 run_async.py'Īs far as the models which we used to perform cross-lingual lexical resource linking, we devised two Transformer-based architectures, trained on our own curated datasets using BabelNet definitions. It makes a request to an endpoint that returns all definitions, in English, for a given lemma, pos tag and language. The REST API requires access to definitions from BabelNet. We provide details about the models used in what follows.

#Who uses babelnet download#

To download the model trained for the task, please visit the SapienzaNLP website. For the docker container to access the model files, please place them in the model/ directory at the root of the project. This is dealt with by the backend with a cronjob. The model is loaded when inference is performed on the pending requests.

#Who uses babelnet code#

REST API: This module contains the code that will be used to obtain the dictionary senses from LEXONOMY of a given lemma and pos tag and communicate with the other two modules to perform the linking task.BabelNet: This module contains the code to obtain the BabelNet senses of a given lemma and POS tag.Model: This module contains the model used to perform cross-lingual dictionary linking.The present API is made up of the following three components: Importantly, this API will make it possible to map the dictionaries made available within the ELEXIS Consortium at definition level by pivoting through BabelNet.

Specifically, this API allows a definition in any language to be mapped to a semantically-equivalent English definition in BabelNet by relying on state-of-the-art Transformer-based architectures. The BabelNet-linker API allows a dictionary to be linked to BabelNet at definition level.