Leveraging Linguistic Linked Data for Cross-Lingual Model Transfer in the Pharmaceutical Domain

Gracia, Jorge; Fäth, Christian; Hartung, Matthias; Ionov, Maxim; Bosque-Gil, Julia; Veríssimo, Susana; Chiarcos, Christian; Orlikowski, Matthias

doi:10.1007/978-3-030-62466-8_31

Cited by 5 publications

(13 citation statements)

References 16 publications

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“…parallel or aligned corpora as in early work on cross-lingual transfer [6]. In particular, we adopt workflows for using LLOD in cross-lingual transfer learning based on task-informed, bilingual word embeddings (adopted from bilingual sentiment embeddings [7]) presented in [8] and apply them to a different target language (Spanish vs. French), a much more varied task (HRQoL aspect detection vs. sentiment analysis) and different text genre (online health community posts vs. medical experts' interview transcripts).…”

Section: Related Workmentioning

confidence: 99%

“…Our approach to language-and task-informed transfer learning (LTTL) relies on the framework described in our previous work [8]. Using this architecture based on bilingual word embeddings [7], task-informed bilingual embedding spaces can be learned for any task which can be framed as text classification.…”

Section: Language-and Task-informed Cross-lingual Transfer Learningmentioning

confidence: 99%

“…In this section we describe the relevant lexical resources used in LTTL. A detailed description of the LLOD pipeline used to generate these resources and the individual processing steps involved is presented in [8]. While our focus is on Apertium RDF as a bilingual lexicon in this paper, these workflows have the potential of growing the LLOD cloud over time both in terms of data volume and richness of available resources.…”

Section: Language Resourcesmentioning

confidence: 99%

“…https://github.com/acoli-repo/acoli-dicts/tree/master/stable/apertium/ apertium-rdf-2020-03-183 https://doi.org/10.5281/zenodo.22056904 This procedure relies on the PoS information that is integrated into Apertium 2.0 via mapping lexical entries to the LexInfo ontology[8].K. Allgaier et al / LLOD-Driven Bilingual Word Embeddings Rivaling Cross-Lingual Transformers 93…”

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confidence: 99%

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LLOD-Driven Bilingual Word Embeddings Rivaling Cross-Lingual Transformers in Quality of Life Concept Detection from French Online Health Communities

Allgaier¹,

Veríssimo²,

Tan³

et al. 2021

Studies on the Semantic Web

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We describe the use of Linguistic Linked Open Data (LLOD) to support a cross-lingual transfer framework for concept detection in online health communities. Our goal is to develop multilingual text analytics as an enabler for analyzing health-related quality of life (HRQoL) from self-reported patient narratives. The framework capitalizes on supervised cross-lingual projection methods, so that labeled training data for a source language are sufficient and are not needed for target languages. Cross-lingual supervision is provided by LLOD lexical resources to learn bilingual word embeddings that are simultaneously tuned to represent an inventory of HRQoL concepts based on the World Health Organization’s quality of life surveys (WHOQOL). We demonstrate that lexicon induction from LLOD resources is a powerful method that yields rich and informative lexical resources for the cross-lingual concept detection task which can outperform existing domain-specific lexica. Furthermore, in a comparative evaluation we find that our models based on bilingual word embeddings exhibit a high degree of complementarity with an approach that integrates machine translation and rule-based extraction algorithms. In a combined configuration, our models rival the performance of state-of-the-art cross-lingual transformers, despite being of considerably lower model complexity.

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Section: Related Workmentioning

confidence: 99%

Section: Language-and Task-informed Cross-lingual Transfer Learningmentioning

confidence: 99%

Section: Language Resourcesmentioning

confidence: 99%

mentioning

confidence: 99%

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LLOD-Driven Bilingual Word Embeddings Rivaling Cross-Lingual Transformers in Quality of Life Concept Detection from French Online Health Communities

Allgaier¹,

Veríssimo²,

Tan³

et al. 2021

Studies on the Semantic Web

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“…We retain the Fig. 1: Apertium RDF graph (figure taken from [8]), which covers 44 languages and 53 language pairs. The nodes represent monolingual lexicons and edges the translation sets among them.…”

Section: System Summarymentioning

confidence: 99%

From Pivots to Graphs: Augmented CycleDensity as a Generalization to One Time InverseConsultation

Goel,

Grover

2021

Preprint

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This paper describes an approach used to generate new translations using raw bilingual dictionaries as part of the 4 th Task Inference Across Dictionaries (TIAD 2021) shared task. We propose Augmented Cycle Density (ACD) as a framework that combines insights from two state of the art methods that require no sense information and parallel corpora: Cycle Density (CD) and One Time Inverse Consultation (OTIC). The task results show that across 3 unseen language pairs, ACD's predictions, has more than double (74%) the coverage of OTIC at almost the same precision (76%). ACD combines CD's scalability -leveraging rich multilingual graphs for better predictions, and OTIC's data efficiency -producing good results with the minimum possible resource of one pivot language.

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Bilingual dictionary generation and enrichment via graph exploration

Goel

Gracia

Forcada

2022

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In recent years, we have witnessed a steady growth of linguistic information represented and exposed as linked data on the Web. Such linguistic linked data have stimulated the development and use of openly available linguistic knowledge graphs, as is the case with the Apertium RDF, a collection of interconnected bilingual dictionaries represented and accessible through Semantic Web standards. In this work, we explore techniques that exploit the graph nature of bilingual dictionaries to automatically infer new links (translations). We build upon a cycle density based method: partitioning the graph into biconnected components for a speed-up, and simplifying the pipeline through a careful structural analysis that reduces hyperparameter tuning requirements. We also analyse the shortcomings of traditional evaluation metrics used for translation inference and propose to complement them with new ones, both-word precision (BWP) and both-word recall (BWR), aimed at being more informative of algorithmic improvements. Over twenty-seven language pairs, our algorithm produces dictionaries about 70% the size of existing Apertium RDF dictionaries at a high BWP of 85% from scratch within a minute. Human evaluation shows that 78% of the additional translations generated for dictionary enrichment are correct as well. We further describe an interesting use-case: inferring synonyms within a single language, on which our initial human-based evaluation shows an average accuracy of 84%. We release our tool as free/open-source software which can not only be applied to RDF data and Apertium dictionaries, but is also easily usable for other formats and communities.

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Leveraging Linguistic Linked Data for Cross-Lingual Model Transfer in the Pharmaceutical Domain

Cited by 5 publications

References 16 publications

LLOD-Driven Bilingual Word Embeddings Rivaling Cross-Lingual Transformers in Quality of Life Concept Detection from French Online Health Communities

LLOD-Driven Bilingual Word Embeddings Rivaling Cross-Lingual Transformers in Quality of Life Concept Detection from French Online Health Communities

From Pivots to Graphs: Augmented CycleDensity as a Generalization to One Time InverseConsultation

Bilingual dictionary generation and enrichment via graph exploration

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