Hypernym Detection Using Strict Partial Order Networks

Dash, Sarthak; Chowdhury, Faisal Mahbub; Gliozzo, Alfio; Mihindukulasooriya, Nandana; Fauceglia, Nicolas Rodolfo

doi:10.1609/aaai.v34i05.6263

Cited by 17 publications

(5 citation statements)

References 25 publications

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“…Dash et al [19] introduced a new neural network-based architecture, Strict Partial Order Networks (SPON) to detect hypernyms. They benchmarked it using SemEval 2018 general and domain specific hypernym discovery tasks.…”

Section: Semeval Shared Tasks On Hypernym Detectionmentioning

confidence: 99%

Learning Semantic Text Similarity to rank Hypernyms of Financial Terms

Ghosh¹,

Chopra²,

Naskar³

2023

Preprint

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Over the years, there has been a paradigm shift in how users access financial services. With the advancement of digitalization more users have been preferring the online mode of performing financial activities. This has led to the generation of a huge volume of financial content. Most investors prefer to go through these contents before making decisions. Every industry has terms that are specific to the domain it operates in. Banking and Financial Services are not an exception to this. In order to fully comprehend these contents, one needs to have a thorough understanding of the financial terms. Getting a basic idea about a term becomes easy when it is explained with the help of the broad category to which it belongs. This broad category is referred to as hypernym. For example, "bond" is a hypernym of the financial term "alternative debenture". In this paper, we propose a system capable of extracting and ranking hypernyms for a given financial term. The system has been trained with financial text corpora obtained from various sources like DBpedia [4], Investopedia, Financial Industry Business Ontology (FIBO), prospectus and so on. Embeddings of these terms have been extracted using FinBERT [3], FinISH [1] and fine-tuned using SentenceBERT [54]. A novel approach has been used to augment the training set with negative samples. It uses the hierarchy present in FIBO. Finally, we benchmark the system performance with that of the existing ones. We establish that it performs better than the existing ones and is also scalable. Our code base will be made available at: https:// github.com/sohomghosh/FinSim Financial Hypernym detection after acceptance of this manuscript.

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Section: Semeval Shared Tasks On Hypernym Detectionmentioning

confidence: 99%

Learning Semantic Text Similarity to rank Hypernyms of Financial Terms

Ghosh¹,

Chopra²,

Naskar³

2023

Preprint

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“…It employs the set expansion algorithm [29], which computes the similarity among terms based on the skip-gram features. Dash et al [30] proposed a neuron-based method based on order embedding. The method considers the partial orders among words.…”

Section: Related Workmentioning

confidence: 99%

Inferring the densest multi-profiled cross-community for a user

Taha

Yoo

Eddinari

et al. 2022

Knowledge-Based Systems

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“…They are quite popular and have been used in AI in many domains, from linguistic tools such as WordNet [113] to domain specific resources such as OBO Foundry [114] ontologies. These resources are expensive to build manually, thus methods have been developed to complement these resources or exploit the existing knowledge using automatic means [115,116].…”

Section: Knowledge Graph Learningmentioning

confidence: 99%

Understanding in Artificial Intelligence

Maetschke¹,

Iraola²,

Barnard³

et al. 2021

Preprint

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Current Artificial Intelligence (AI) methods, most based on deep learning, have facilitated progress in several fields, including computer vision and natural language understanding. The progress of these AI methods is measured using benchmarks designed to solve challenging tasks, such as visual question answering. A question remains of how much understanding is leveraged by these methods and how appropriate are the current benchmarks to measure understanding capabilities. To answer these questions, we have analysed existing benchmarks and their understanding capabilities, defined by a set of understanding capabilities, and current research streams. We show how progress has been made in benchmark development to measure understanding capabilities of AI methods and we review as well how current methods develop understanding capabilities.

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Hypernym Detection Using Strict Partial Order Networks

Cited by 17 publications

References 25 publications

Learning Semantic Text Similarity to rank Hypernyms of Financial Terms

Learning Semantic Text Similarity to rank Hypernyms of Financial Terms

Inferring the densest multi-profiled cross-community for a user

Understanding in Artificial Intelligence

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