A Top-down Graph-based Tool for Modeling Classical Semantic Maps: A Crosslinguistic Case Study of Supplementary Adverbs

• URL: http://arxiv.org/abs/2412.01423v2
• Date: Sat, 08 Feb 2025 08:10:57 GMT
• Title: A Top-down Graph-based Tool for Modeling Classical Semantic Maps: A Crosslinguistic Case Study of Supplementary Adverbs
• Authors: Zhu Liu, Cunliang Kong, Ying Liu, Maosong Sun,
• Abstract summary: Semantic map models (SMMs) construct a network-like conceptual space from cross-linguistic instances or forms. Most SMMs are manually built by human experts using bottom-up procedures. We propose a novel graph-based algorithm that automatically generates conceptual spaces and SMMs in a top-down manner.
• Score: 50.982315553104975
• License:
• Abstract: Semantic map models (SMMs) construct a network-like conceptual space from cross-linguistic instances or forms, based on the connectivity hypothesis. This approach has been widely used to represent similarity and entailment relationships in cross-linguistic concept comparisons. However, most SMMs are manually built by human experts using bottom-up procedures, which are often labor-intensive and time-consuming. In this paper, we propose a novel graph-based algorithm that automatically generates conceptual spaces and SMMs in a top-down manner. The algorithm begins by creating a dense graph, which is subsequently pruned into maximum spanning trees, selected according to metrics we propose. These evaluation metrics include both intrinsic and extrinsic measures, considering factors such as network structure and the trade-off between precision and coverage. A case study on cross-linguistic supplementary adverbs demonstrates the effectiveness and efficiency of our model compared to human annotations and other automated methods. The tool is available at https://github.com/RyanLiut/SemanticMapModel.

Related papers

• Instruction-Following Pruning for Large Language Models [58.329978053711024]
  We move beyond the traditional static pruning approach of determining a fixed pruning mask for a model. In our method, the pruning mask is input-dependent and adapts dynamically based on the information described in a user instruction. Our approach, termed "instruction-following pruning", introduces a sparse mask predictor that takes the user instruction as input and dynamically selects the most relevant model parameters for the given task.
  arXiv  Detail & Related papers  (2025-01-03T20:19:14Z)
• Incremental Multimodal Surface Mapping via Self-Organizing Gaussian Mixture Models [1.0878040851638]
  This letter describes an incremental multimodal surface mapping methodology, which represents the environment as a continuous probabilistic model. The strategy employed in this work utilizes Gaussian mixture models (GMMs) to represent the environment. To bridge this gap, this letter introduces a spatial hash map for rapid GMM submap extraction combined with an approach to determine relevant and redundant data in a point cloud.
  arXiv  Detail & Related papers  (2023-09-19T19:49:03Z)
• Semantic Graph Representation Learning for Handwritten Mathematical Expression Recognition [57.60390958736775]
  We propose a simple but efficient method to enhance semantic interaction learning (SIL) We first construct a semantic graph based on the statistical symbol co-occurrence probabilities. Then we design a semantic aware module (SAM), which projects the visual and classification feature into semantic space. Our method achieves better recognition performance than prior arts on both CROHME and HME100K datasets.
  arXiv  Detail & Related papers  (2023-08-21T06:23:41Z)
• Contextual Dictionary Lookup for Knowledge Graph Completion [32.493168863565465]
  Knowledge graph completion (KGC) aims to solve the incompleteness of knowledge graphs (KGs) by predicting missing links from known triples. Most existing embedding models map each relation into a unique vector, overlooking the specific fine-grained semantics of them under different entities. We present a novel method utilizing contextual dictionary lookup, enabling conventional embedding models to learn fine-grained semantics of relations in an end-to-end manner.
  arXiv  Detail & Related papers  (2023-06-13T12:13:41Z)
• MAUVE Scores for Generative Models: Theory and Practice [95.86006777961182]
  We present MAUVE, a family of comparison measures between pairs of distributions such as those encountered in the generative modeling of text or images. We find that MAUVE can quantify the gaps between the distributions of human-written text and those of modern neural language models. We demonstrate in the vision domain that MAUVE can identify known properties of generated images on par with or better than existing metrics.
  arXiv  Detail & Related papers  (2022-12-30T07:37:40Z)
• Detecting Human-Object Interactions with Object-Guided Cross-Modal Calibrated Semantics [6.678312249123534]
  We aim to boost end-to-end models with object-guided statistical priors. We propose to utilize a Verb Semantic Model (VSM) and use semantic aggregation to profit from this object-guided hierarchy. The above modules combined composes Object-guided Cross-modal Network (OCN)
  arXiv  Detail & Related papers  (2022-02-01T07:39:04Z)
• Temporal Graph Network Embedding with Causal Anonymous Walks Representations [54.05212871508062]
  We propose a novel approach for dynamic network representation learning based on Temporal Graph Network. For evaluation, we provide a benchmark pipeline for the evaluation of temporal network embeddings. We show the applicability and superior performance of our model in the real-world downstream graph machine learning task provided by one of the top European banks.
  arXiv  Detail & Related papers  (2021-08-19T15:39:52Z)
• A Survey and Implementation of Performance Metrics for Self-Organized Maps [0.0]
  Self-Organizing Map algorithms have been used for almost 40 years across various application domains. This paper introduces each metric available in our module along with usage examples.
  arXiv  Detail & Related papers  (2020-11-11T15:28:33Z)
• Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion [59.549664231655726]
  A case-based reasoning (CBR) system solves a new problem by retrieving cases' that are similar to the given problem. In this paper, we demonstrate that such a system is achievable for reasoning in knowledge-bases (KBs) Our approach predicts attributes for an entity by gathering reasoning paths from similar entities in the KB.
  arXiv  Detail & Related papers  (2020-10-07T17:48:12Z)
• Robust Similarity and Distance Learning via Decision Forests [8.587164648430251]
  We propose a novel decision forest algorithm for the task of distance learning, which we call Similarity and Metric Random Forests (SMERF) Its ability to approximate arbitrary distances and identify important features is empirically demonstrated on simulated data sets.
  arXiv  Detail & Related papers  (2020-07-27T20:17:42Z)

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.