Exploiting Non-Taxonomic Relations for Measuring Semantic Similarity and Relatedness in WordNet

• URL: http://arxiv.org/abs/2006.12106v1
• Date: Mon, 22 Jun 2020 09:59:39 GMT
• Title: Exploiting Non-Taxonomic Relations for Measuring Semantic Similarity and Relatedness in WordNet
• Authors: Mohannad AlMousa, Rachid Benlamri, Richard Khoury
• Abstract summary: This paper explores the benefits of using all types of non-taxonomic relations in large linked data, such as WordNet knowledge graph. We propose a holistic poly-relational approach based on a new relation-based information content and non-taxonomic-based weighted paths.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Various applications in the areas of computational linguistics and artificial intelligence employ semantic similarity to solve challenging tasks, such as word sense disambiguation, text classification, information retrieval, machine translation, and document clustering. Previous work on semantic similarity followed a mono-relational approach using mostly the taxonomic relation "ISA". This paper explores the benefits of using all types of non-taxonomic relations in large linked data, such as WordNet knowledge graph, to enhance existing semantic similarity and relatedness measures. We propose a holistic poly-relational approach based on a new relation-based information content and non-taxonomic-based weighted paths to devise a comprehensive semantic similarity and relatedness measure. To demonstrate the benefits of exploiting non-taxonomic relations in a knowledge graph, we used three strategies to deploy non-taxonomic relations at different granularity levels. We conducted experiments on four well-known gold standard datasets, and the results demonstrated the robustness and scalability of the proposed semantic similarity and relatedness measure, which significantly improves existing similarity measures.

Related papers

• Tübingen-CL at SemEval-2024 Task 1:Ensemble Learning for Semantic Relatedness Estimation [0.0]
  The paper introduces our system for SemEval-2024 Task 1, which aims to predict the relatedness of sentence pairs. We employ an ensemble approach integrating various systems, including statistical textual features and outputs of deep learning models to predict relatedness scores.
  arXiv  Detail & Related papers  (2024-10-14T14:56:51Z)
• Learning Complete Topology-Aware Correlations Between Relations for Inductive Link Prediction [121.65152276851619]
  We show that semantic correlations between relations are inherently edge-level and entity-independent. We propose a novel subgraph-based method, namely TACO, to model Topology-Aware COrrelations between relations. To further exploit the potential of RCN, we propose Complete Common Neighbor induced subgraph.
  arXiv  Detail & Related papers  (2023-09-20T08:11:58Z)
• Relational Sentence Embedding for Flexible Semantic Matching [86.21393054423355]
  We present Sentence Embedding (RSE), a new paradigm to discover further the potential of sentence embeddings. RSE is effective and flexible in modeling sentence relations and outperforms a series of state-of-the-art embedding methods.
  arXiv  Detail & Related papers  (2022-12-17T05:25:17Z)
• Metric Learning as a Service with Covariance Embedding [7.5989847759545155]
  Metric learning aims to maximize and minimize inter- and intra-class similarities. Existing models mainly rely on distance measures to obtain a separable embedding space. We argue that to enable metric learning as a service for high-performance deep learning applications, we should also wisely deal with inter-class relationships.
  arXiv  Detail & Related papers  (2022-11-28T10:10:59Z)
• Evaluation of taxonomic and neural embedding methods for calculating semantic similarity [0.0]
  We study the mechanisms between taxonomic and distributional similarity measures. We find that taxonomic similarity measures can depend on the shortest path length as a prime factor to predict semantic similarity. The synergy of retrofitting neural embeddings with concept relations in similarity prediction may indicate a new trend to leverage knowledge bases on transfer learning.
  arXiv  Detail & Related papers  (2022-09-30T02:54:21Z)
• Exploiting Global Semantic Similarities in Knowledge Graphs by Relational Prototype Entities [55.952077365016066]
  An empirical observation is that the head and tail entities connected by the same relation often share similar semantic attributes. We propose a novel approach, which introduces a set of virtual nodes called textittextbfrelational prototype entities. By enforcing the entities' embeddings close to their associated prototypes' embeddings, our approach can effectively encourage the global semantic similarities of entities.
  arXiv  Detail & Related papers  (2022-06-16T09:25:33Z)
• Apport des ontologies pour le calcul de la similarit\'e s\'emantique au sein d'un syst\`eme de recommandation [0.0]
  Measurement of the semantic relatedness or likeness between terms, words, or text data plays an important role in different applications. We propose and carry on an approach for the calculation of semantic similarity using in the context of a recommender system.
  arXiv  Detail & Related papers  (2022-05-25T07:27:10Z)
• Transformer-based Dual Relation Graph for Multi-label Image Recognition [56.12543717723385]
  We propose a novel Transformer-based Dual Relation learning framework. We explore two aspects of correlation, i.e., structural relation graph and semantic relation graph. Our approach achieves new state-of-the-art on two popular multi-label recognition benchmarks.
  arXiv  Detail & Related papers  (2021-10-10T07:14:52Z)
• PAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense Passage Retrieval [87.68667887072324]
  We propose a novel approach that leverages query-centric and PAssage-centric sImilarity Relations (called PAIR) for dense passage retrieval. To implement our approach, we make three major technical contributions by introducing formal formulations of the two kinds of similarity relations. Our approach significantly outperforms previous state-of-the-art models on both MSMARCO and Natural Questions datasets.
  arXiv  Detail & Related papers  (2021-08-13T02:07:43Z)
• Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training [49.9995628166064]
  We propose CTEG, a model equipped with two mechanisms to learn to decouple easily-confused relations. On the one hand, an EGA mechanism is introduced to guide the attention to filter out information causing confusion. On the other hand, a Confusion-Aware Training (CAT) method is proposed to explicitly learn to distinguish relations.
  arXiv  Detail & Related papers  (2020-10-21T11:07:53Z)
• Semantic Relatedness and Taxonomic Word Embeddings [2.47944699884651]
  We show that there are different types of semantic relatedness and that different lexical representations encode different forms of relatedness. We present experiments that analyse taxonomic embeddings that have been trained on a synthetic corpus that has been generated via a random walk over a taxonomy. We explore the interactions between the relative sizes of natural and synthetic corpora on the performance of embeddings when taxonomic and thematic embeddings are combined.
  arXiv  Detail & Related papers  (2020-02-14T20:02:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.