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aihn-ucsd

This repo is used to release code that is generated by the AI Horizons Network as needed to supplement conference papers and workshops.

christos42.github.io

cldr_clner_models

Though language model text embeddings have revolutionized NLP research, their ability to capture high-level semantic information, such as relations between entities in text, is limited. In this paper, we propose a novel contrastive learning framework that trains sentence embeddings to encode the relations in a graph structure. Given a sentence (unstructured text) and its graph, we use contrastive learning to impose relation-related structure on the token level representations of the sentence obtained with a CharacterBERT (El Boukkouri et al., 2020) model. The resulting relation-aware sentence embeddings achieve state-of-the-art results on the relation extraction task using only a simple KNN classifier, thereby demonstrating the success of the proposed method. Additional visualization by a tSNE analysis shows the effectiveness of the learned representation space compared to baselines. Furthermore, we show that we can learn a different space for named entity recognition, again using a contrastive learning objective, and demonstrate how to successfully combine both representation spaces in an entity-relation task.

flair

A very simple framework for state-of-the-art Natural Language Processing (NLP)

hspo-ontology

Ontology representing a 360-view of a person (or cohort) that spans across multiple domains, from health to social.

inductive_bias_ie

An Information Extraction Study: Take In Mind the Tokenization! (official repository of the paper)

machine-learning-engineering-for-production-public

Public repo for DeepLearning.AI MLEP Specialization