Semantic Information Extraction for Text Data with Probability Graph

• URL: http://arxiv.org/abs/2309.08879v1
• Date: Sat, 16 Sep 2023 05:01:20 GMT
• Title: Semantic Information Extraction for Text Data with Probability Graph
• Authors: Zhouxiang Zhao, Zhaohui Yang, Ye Hu, Licheng Lin, Zhaoyang Zhang
• Abstract summary: This paper studies the problem of semantic information extraction for resource constrained text data transmission. A Floyd's algorithm based solution coupled with an efficient sorting mechanism is proposed.
• Score: 19.347123617466497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, the problem of semantic information extraction for resource constrained text data transmission is studied. In the considered model, a sequence of text data need to be transmitted within a communication resource-constrained network, which only allows limited data transmission. Thus, at the transmitter, the original text data is extracted with natural language processing techniques. Then, the extracted semantic information is captured in a knowledge graph. An additional probability dimension is introduced in this graph to capture the importance of each information. This semantic information extraction problem is posed as an optimization framework whose goal is to extract most important semantic information for transmission. To find an optimal solution for this problem, a Floyd's algorithm based solution coupled with an efficient sorting mechanism is proposed. Numerical results testify the effectiveness of the proposed algorithm with regards to two novel performance metrics including semantic uncertainty and semantic similarity.

Related papers

• GuideWalk: A Novel Graph-Based Word Embedding for Enhanced Text Classification [0.0]
  The processing of text data requires embedding, a method of translating the content of the text to numeric vectors. A new text embedding approach, namely the Guided Transition Probability Matrix (GTPM) model is proposed. The proposed method is tested with real-world data sets and eight well-known and successful embedding algorithms.
  arXiv  Detail & Related papers  (2024-04-25T18:48:11Z)
• Semantic-Native Communication: A Simplicial Complex Perspective [50.099494681671224]
  We study semantic communication from a topological space perspective. A transmitter first maps its data into a $k$-order simplicial complex and then learns its high-order correlations. The receiver decodes the structure and infers the missing or distorted data.
  arXiv  Detail & Related papers  (2022-10-30T22:33:44Z)
• Performance Optimization for Semantic Communications: An Attention-based Reinforcement Learning Approach [187.4094332217186]
  A semantic communication framework is proposed for textual data transmission. A metric of semantic similarity (MSS) that jointly captures the semantic accuracy and completeness of the recovered text is proposed.
  arXiv  Detail & Related papers  (2022-08-17T11:39:16Z)
• Neuro-Symbolic Artificial Intelligence (AI) for Intent based Semantic Communication [85.06664206117088]
  6G networks must consider semantics and effectiveness (at end-user) of the data transmission. NeSy AI is proposed as a pillar for learning causal structure behind the observed data. GFlowNet is leveraged for the first time in a wireless system to learn the probabilistic structure which generates the data.
  arXiv  Detail & Related papers  (2022-05-22T07:11:57Z)
• Fine-Grained Visual Entailment [51.66881737644983]
  We propose an extension of this task, where the goal is to predict the logical relationship of fine-grained knowledge elements within a piece of text to an image. Unlike prior work, our method is inherently explainable and makes logical predictions at different levels of granularity. We evaluate our method on a new dataset of manually annotated knowledge elements and show that our method achieves 68.18% accuracy at this challenging task.
  arXiv  Detail & Related papers  (2022-03-29T16:09:38Z)
• DEIM: An effective deep encoding and interaction model for sentence matching [0.0]
  We propose a sentence matching method based on deep encoding and interaction to extract deep semantic information. In the encoder layer,we refer to the information of another sentence in the process of encoding a single sentence, and later use a algorithm to fuse the information. In the interaction layer, we use a bidirectional attention mechanism and a self-attention mechanism to obtain deep semantic information.
  arXiv  Detail & Related papers  (2022-03-20T07:59:42Z)
• TRIE: End-to-End Text Reading and Information Extraction for Document Understanding [56.1416883796342]
  We propose a unified end-to-end text reading and information extraction network. multimodal visual and textual features of text reading are fused for information extraction. Our proposed method significantly outperforms the state-of-the-art methods in both efficiency and accuracy.
  arXiv  Detail & Related papers  (2020-05-27T01:47:26Z)
• Boosting Semantic Human Matting with Coarse Annotations [66.8725980604434]
  coarse annotated human dataset is much easier to acquire and collect from the public dataset. A matting refinement network takes in the unified mask and the input image to predict the final alpha matte.
  arXiv  Detail & Related papers  (2020-04-10T09:11:02Z)
• Selective Attention Encoders by Syntactic Graph Convolutional Networks for Document Summarization [21.351111598564987]
  We propose a graph to connect the parsing trees from the sentences in a document and utilize the stacked graph convolutional networks (GCNs) to learn the syntactic representation for a document. The proposed GCNs based selective attention approach outperforms the baselines and achieves the state-of-the-art performance on the dataset.
  arXiv  Detail & Related papers  (2020-03-18T01:30:02Z)
• Hybrid Attention-Based Transformer Block Model for Distant Supervision Relation Extraction [20.644215991166902]
  We propose a new framework using hybrid attention-based Transformer block with multi-instance learning to perform the DSRE task. The proposed approach can outperform the state-of-the-art algorithms on the evaluation dataset.
  arXiv  Detail & Related papers  (2020-03-10T13:05:52Z)

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.