Global Vectors for Node Representations

Brochier, Robin; Guille, Adrien; Velcin, Julien

doi:10.1145/3308558.3313595

Cited by 38 publications

(24 citation statements)

References 14 publications

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“…Finally, to evaluate the model of step (2), link prediction constitutes a good evaluation task. Several ways to generate a pair of training/test set exist (random, temporal).…”

Section: Methodsmentioning

confidence: 99%

Representation Learning for Recommender Systems with Application to the Scientific Literature

Brochier

2019

Companion Proceedings of the 2019 World Wide Web Conference

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The scientific literature is a large information network linking various actors (laboratories, companies, institutions, etc.). The vast amount of data generated by this network constitutes a dynamic heterogeneous attributed network (HAN), in which new information is constantly produced and from which it is increasingly difficult to extract content of interest. In this article, I present my first thesis works in partnership with an industrial company, Digital Scientific Research Technology. This later offers a scientific watch tool, Peerus, addressing various issues, such as the real time recommendation of newly published papers or the search for active experts to start new collaborations. To tackle this diversity of applications, a common approach consists in learning representations of the nodes and attributes of this HAN and use them as features for a variety of recommendation tasks. However, most works on attributed network embedding pay too little attention to textual attributes and do not fully take advantage of recent natural language processing techniques. Moreover, proposed methods that jointly learn node and document representations do not provide a way to effectively infer representations for new documents for which network information is missing, which happens to be crucial in real time recommender systems. Finally, the interplay between textual and graph data in text-attributed heterogeneous networks remains an open research direction.

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“…Finally, to evaluate the model of step (2), link prediction constitutes a good evaluation task. Several ways to generate a pair of training/test set exist (random, temporal).…”

Section: Methodsmentioning

confidence: 99%

Representation Learning for Recommender Systems with Application to the Scientific Literature

Brochier

2019

Companion Proceedings of the 2019 World Wide Web Conference

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“…It combines the advantages of two vector learning techniques: global matrix factorization methods and local context window methods ( Pennington, Socher & Manning, 2014 ). This algorithm has many applications in different fields such as text similarity ( Kenter & De Rijke, 2015 ), node representations ( Brochier, Guille & Velcin, 2019 ), emotion detection ( George, Barathi Ganesh & Soman, 2018 ) and many others. This algorithm found its way in many biomedicine such as finding semantic similarity ( Muneeb, Sahu & Anand, 2015 ), extracting Adverse Drug Reactions (ADR) ( Lin et al, 2015 ), and analyzing protein sequences ( George et al, 2019 ).…”

Section: Related Workmentioning

confidence: 99%

An automated method to enrich consumer health vocabularies using GloVe word embeddings and an auxiliary lexical resource

Ibrahim¹,

Gauch²,

Salman³

et al. 2021

PeerJ Computer Science

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Background Clear language makes communication easier between any two parties. A layman may have difficulty communicating with a professional due to not understanding the specialized terms common to the domain. In healthcare, it is rare to find a layman knowledgeable in medical terminology which can lead to poor understanding of their condition and/or treatment. To bridge this gap, several professional vocabularies and ontologies have been created to map laymen medical terms to professional medical terms and vice versa. Objective Many of the presented vocabularies are built manually or semi-automatically requiring large investments of time and human effort and consequently the slow growth of these vocabularies. In this paper, we present an automatic method to enrich laymen’s vocabularies that has the benefit of being able to be applied to vocabularies in any domain. Methods Our entirely automatic approach uses machine learning, specifically Global Vectors for Word Embeddings (GloVe), on a corpus collected from a social media healthcare platform to extend and enhance consumer health vocabularies. Our approach further improves the consumer health vocabularies by incorporating synonyms and hyponyms from the WordNet ontology. The basic GloVe and our novel algorithms incorporating WordNet were evaluated using two laymen datasets from the National Library of Medicine (NLM), Open-Access Consumer Health Vocabulary (OAC CHV) and MedlinePlus Healthcare Vocabulary. Results The results show that GloVe was able to find new laymen terms with an F-score of 48.44%. Furthermore, our enhanced GloVe approach outperformed basic GloVe with an average F-score of 61%, a relative improvement of 25%. Furthermore, the enhanced GloVe showed a statistical significance over the two ground truth datasets with P < 0.001. Conclusions This paper presents an automatic approach to enrich consumer health vocabularies using the GloVe word embeddings and an auxiliary lexical source, WordNet. Our approach was evaluated used healthcare text downloaded from MedHelp.org, a healthcare social media platform using two standard laymen vocabularies, OAC CHV, and MedlinePlus. We used the WordNet ontology to expand the healthcare corpus by including synonyms, hyponyms, and hypernyms for each layman term occurrence in the corpus. Given a seed term selected from a concept in the ontology, we measured our algorithms’ ability to automatically extract synonyms for those terms that appeared in the ground truth concept. We found that enhanced GloVe outperformed GloVe with a relative improvement of 25% in the F-score.

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“…Even if these approaches yield good results, they require tuning a lot of hyperparameters. Two methods are based on factorization approaches: GVNR-t [4], that extends GloVe [14], and AANE [7]. None of these methods learn documents and words embedding in the same space.…”

Section: Related Workmentioning

confidence: 99%

Document Network Projection in Pretrained Word Embedding Space

Gourru

Guille

Velcin

et al. 2020

Lecture Notes in Computer Science

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We present Regularized Linear Embedding (RLE), a novel method that projects a collection of linked documents (e.g. citation network) into a pretrained word embedding space. In addition to the textual content, we leverage a matrix of pairwise similarities providing complementary information (e.g., the network proximity of two documents in a citation graph). We first build a simple word vector average for each document, and we use the similarities to alter this average representation. The document representations can help to solve many information retrieval tasks, such as recommendation, classification and clustering. We demonstrate that our approach outperforms or matches existing document network embedding methods on node classification and link prediction tasks. Furthermore, we show that it helps identifying relevant keywords to describe document classes.

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Global Vectors for Node Representations

Cited by 38 publications

References 14 publications

Representation Learning for Recommender Systems with Application to the Scientific Literature

Representation Learning for Recommender Systems with Application to the Scientific Literature

An automated method to enrich consumer health vocabularies using GloVe word embeddings and an auxiliary lexical resource

Document Network Projection in Pretrained Word Embedding Space

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