Transforming Term Extraction: Transformer-Based Approaches to Multilingual Term Extraction Across Domains

Lang, Christian; Wachowiak, Lennart; Heinisch, Barbara; Gromann, Dagmar

doi:10.18653/v1/2021.findings-acl.316

Cited by 13 publications

(5 citation statements)

References 25 publications

(28 reference statements)

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“…Machine learning-based approaches for automated term extraction utilize, among other things, external data sets and web search (Ramisch et al, 2010) and word embeddings (Wang et al, 2016;Amjadian et al, 2018). Newest approaches are also based on language models (e.g., (Gao and Yuan, 2019;Lang et al, 2021)), but require, as many other approaches, more context than a few keywords as input as for our system.…”

Section: Related Workmentioning

confidence: 99%

VOCAB-EXPANDER: A System for Creating Domain-Specific Vocabularies Based on Word Embeddings

Färber,

Popovic

2023

Proceedings of the Conference Recent Advances in Natural Language Processing - Large Language Models for Natural Language Proce

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In this paper, we propose VOCAB-EXPANDER at https://vocab-expander.com, an online tool that enables end-users (e.g., technology scouts) to create and expand a vocabulary of their domain of interest. It utilizes an ensemble of state-of-the-art word embedding techniques based on web text and ConceptNet, a common-sense knowledge base, to suggest related terms for already given terms. The system has an easy-to-use interface that allows users to quickly confirm or reject term suggestions. VOCAB-EXPANDER offers a variety of potential use cases, such as improving concept-based information retrieval in technology and innovation management, enhancing communication and collaboration within organizations or interdisciplinary projects, and creating vocabularies for specific courses in education.

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Section: Related Workmentioning

confidence: 99%

VOCAB-EXPANDER: A System for Creating Domain-Specific Vocabularies Based on Word Embeddings

Färber,

Popovic

2023

Proceedings of the Conference Recent Advances in Natural Language Processing - Large Language Models for Natural Language Proce

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“…Other approaches include transformer-based methods 44 , model-based approaches 45 , graph-based approaches 46 , learning-based approaches 47 , and entropy-based approaches 34 . Additionally, fully convolutional networks (FCN) 48 have been used for image-to-pixel-level classification to reduce the computational workload of pre-processing.…”

Section: Related Workmentioning

confidence: 99%

White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learning

Rashid,

Raza,

Sharif

et al. 2023

Sci Rep

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White blood cells (WBCs) are an indispensable constituent of the immune system. Efficient and accurate categorization of WBC is a critical task for disease diagnosis by medical experts. This categorization helps in the correct identification of medical problems. In this research work, WBC classes are categorized with the help of a transform learning model in combination with our proposed virtual hexagonal trellis (VHT) structure feature extraction method. The VHT feature extractor is a kernel-based filter model designed over a square lattice. In the first step, Graft Net CNN model is used to extract features of augmented data set images. Later, the VHT base feature extractor extracts useful features. The CNN-extracted features are passed to ant colony optimization (ACO) module for optimal features acquisition. Extracted features from the VHT base filter and ACO are serially merged to create a single feature vector. The merged features are passed to the support vector machine (SVM) variants for optimal classification. Our strategy yields 99.9% accuracy, which outperforms other existing methods.

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“…Usually, the main purpose of these tools is to generate plain lists of terms with information about their frequency in the corpus, but no additional linguistic data. Recent approaches are also trying to extract multilingual terminology across domain using transformers, which is a great step forward within the area [33].…”

Section: Terminology-related Technologymentioning

confidence: 99%

TermitUp: Generation and enrichment of linked terminologies

Martín-Chozas

Vázquez-Flores

Calleja

et al. 2022

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Domain-specific terminologies play a central role in many language technology solutions. Substantial manual effort is still involved in the creation of such resources, and many of them are published in proprietary formats that cannot be easily reused in other applications. Automatic term extraction tools help alleviate this cumbersome task. However, their results are usually in the form of plain lists of terms or as unstructured data with limited linguistic information. Initiatives such as the Linguistic Linked Open Data cloud (LLOD) foster the publication of language resources in open structured formats, specifically RDF, and their linking to other resources on the Web of Data. In order to leverage the wealth of linguistic data in the LLOD and speed up the creation of linked terminological resources, we propose TermitUp, a service that generates enriched domain specific terminologies directly from corpora, and publishes them in open and structured formats. TermitUp is composed of five modules performing terminology extraction, terminology post-processing, terminology enrichment, term relation validation and RDF publication. As part of the pipeline implemented by this service, existing resources in the LLOD are linked with the resulting terminologies, contributing in this way to the population of the LLOD cloud. TermitUp has been used in the framework of European projects tackling different fields, such as the legal domain, with promising results. Different alternatives on how to model enriched terminologies are considered and good practices illustrated with examples are proposed.

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Transforming Term Extraction: Transformer-Based Approaches to Multilingual Term Extraction Across Domains

Cited by 13 publications

References 25 publications

VOCAB-EXPANDER: A System for Creating Domain-Specific Vocabularies Based on Word Embeddings

VOCAB-EXPANDER: A System for Creating Domain-Specific Vocabularies Based on Word Embeddings

White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learning

TermitUp: Generation and enrichment of linked terminologies

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