Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm star

• URL: http://arxiv.org/abs/2107.04781v1
• Date: Sat, 10 Jul 2021 07:18:03 GMT
• Title: Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm star
• Authors: Bryar A. Hassan, Tarik A. Rashid and Seyedali Mirjalili
• Abstract summary: The process of deriving concept hierarchies from corpora is typically a time-consuming and resource-intensive process. The resulting lattice of formal context is evaluated to the standard one using concept lattice-invariants. The results show that adaptive ECA* performs concept lattice faster than other mentioned competitive techniques in different fill ratios.
• Score: 15.154538450706474
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: It is beneficial to automate the process of deriving concept hierarchies from corpora since a manual construction of concept hierarchies is typically a time-consuming and resource-intensive process. As such, the overall process of learning concept hierarchies from corpora encompasses a set of steps: parsing the text into sentences, splitting the sentences and then tokenising it. After the lemmatisation step, the pairs are extracted using FCA. However, there might be some uninteresting and erroneous pairs in the formal context. Generating formal context may lead to a time-consuming process, so formal context size reduction is required to remove uninterested and erroneous pairs, taking less time to extract the concept lattice and concept hierarchies accordingly. In this premise, this study aims to propose two frameworks: (1) A framework to review the current process of deriving concept hierarchies from corpus utilising FCA; (2) A framework to decrease the formal contexts ambiguity of the first framework using an adaptive version of ECA*. Experiments are conducted by applying 385 sample corpora from Wikipedia on the two frameworks to examine the reducing size of formal context, which leads to yield concept lattice and concept hierarchy. The resulting lattice of formal context is evaluated to the standard one using concept lattice-invariants. Accordingly, the homomorphic between the two lattices preserves the quality of resulting concept hierarchies by 89% in contrast to the basic ones, and the reduced concept lattice inherits the structural relation of the standard one. The adaptive ECA* is examined against its four counterpart baseline algorithms to measure the execution time on random datasets with different densities (fill ratios). The results show that adaptive ECA* performs concept lattice faster than other mentioned competitive techniques in different fill ratios.

Related papers

• A Canonization Perspective on Invariant and Equivariant Learning [54.44572887716977]
  Canonization provides a principled understanding of the design of frames. We show that there exists an inherent connection between frames and canonical forms. We design novel frames for eigenvectors that are strictly superior to existing methods.
  arXiv  Detail & Related papers  (2024-05-28T17:22:15Z)
• Best of Both Worlds: A Pliable and Generalizable Neuro-Symbolic Approach for Relation Classification [17.398872494876365]
  This paper introduces a novel neuro-symbolic architecture for relation classification (RC) It combines rule-based methods with contemporary deep learning techniques. We show that our proposed method outperforms previous state-of-the-art models in three out of four settings.
  arXiv  Detail & Related papers  (2024-03-05T20:08:32Z)
• Ontology Learning Using Formal Concept Analysis and WordNet [0.9065034043031668]
  This project and dissertation provide a Formal Concept Analysis and WordNet framework for learning concept hierarchies from free texts. We compute formal idea lattice and create a classical concept hierarchy. Despite several system constraints and component discrepancies that may prevent logical conclusion, the following data imply idea hierarchies in this project and dissertation are promising.
  arXiv  Detail & Related papers  (2023-11-10T08:28:30Z)
• Coherent Entity Disambiguation via Modeling Topic and Categorical Dependency [87.16283281290053]
  Previous entity disambiguation (ED) methods adopt a discriminative paradigm, where prediction is made based on matching scores between mention context and candidate entities. We propose CoherentED, an ED system equipped with novel designs aimed at enhancing the coherence of entity predictions. We achieve new state-of-the-art results on popular ED benchmarks, with an average improvement of 1.3 F1 points.
  arXiv  Detail & Related papers  (2023-11-06T16:40:13Z)
• Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning [53.68371566336254]
  We argue that the key to better performance lies in meaningful latent modality structures instead of perfect modality alignment. Specifically, we design 1) a deep feature separation loss for intra-modality regularization; 2) a Brownian-bridge loss for inter-modality regularization; and 3) a geometric consistency loss for both intra- and inter-modality regularization.
  arXiv  Detail & Related papers  (2023-03-10T14:38:49Z)
• Progressive Tree-Structured Prototype Network for End-to-End Image Captioning [74.8547752611337]
  We propose a novel Progressive Tree-Structured prototype Network (dubbed PTSN) PTSN is the first attempt to narrow down the scope of prediction words with appropriate semantics by modeling the hierarchical textual semantics. Our method achieves a new state-of-the-art performance with 144.2% (single model) and 146.5% (ensemble of 4 models) CIDEr scores on Karpathy' split and 141.4% (c5) and 143.9% (c40) CIDEr scores on the official online test server.
  arXiv  Detail & Related papers  (2022-11-17T11:04:00Z)
• Two-stream Hierarchical Similarity Reasoning for Image-text Matching [66.43071159630006]
  A hierarchical similarity reasoning module is proposed to automatically extract context information. Previous approaches only consider learning single-stream similarity alignment. A two-stream architecture is developed to decompose image-text matching into image-to-text level and text-to-image level similarity computation.
  arXiv  Detail & Related papers  (2022-03-10T12:56:10Z)
• Concept and Attribute Reduction Based on Rectangle Theory of Formal Concept [5.657202839641533]
  It is known that there are three types of formal concepts: core concepts, relative necessary concepts and unnecessary concepts. We present the new judgment results for relative necessary concepts and unnecessary concepts. A fast algorithm for reducing attributes while preserving the extensions for a set of formal concepts is proposed.
  arXiv  Detail & Related papers  (2021-10-29T02:10:08Z)
• Artificial Intelligence Algorithms for Natural Language Processing and the Semantic Web Ontology Learning [0.76146285961466]
  A new evolutionary clustering algorithm star (ECA*) is proposed. Experiments were conducted to evaluate ECA* against five state-of-the-art approaches. The results indicate that ECA* overcomes its competitive techniques in terms of the ability to find the right clusters.
  arXiv  Detail & Related papers  (2021-08-31T11:57:41Z)
• Hierarchical Poset Decoding for Compositional Generalization in Language [52.13611501363484]
  We formalize human language understanding as a structured prediction task where the output is a partially ordered set (poset) Current encoder-decoder architectures do not take the poset structure of semantics into account properly. We propose a novel hierarchical poset decoding paradigm for compositional generalization in language.
  arXiv  Detail & Related papers  (2020-10-15T14:34:26Z)

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.