Application of the Multi-label Residual Convolutional Neural Network text classifier using Content-Based Routing process

• URL: http://arxiv.org/abs/2110.15801v1
• Date: Tue, 19 Oct 2021 19:10:34 GMT
• Title: Application of the Multi-label Residual Convolutional Neural Network text classifier using Content-Based Routing process
• Authors: Tounsi Achraf, Elkefi Safa
• Abstract summary: We will present an NLP application in text classifying process using the content-based router. The ultimate goal throughout this article is to predict the event described by a legal ad from the plain text of the ad.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this article, we will present an NLP application in text classifying process using the content-based router. The ultimate goal throughout this article is to predict the event described by a legal ad from the plain text of the ad. This problem is purely a supervised problem that will involve the use of NLP techniques and conventional modeling methodologies through the use of the Multi-label Residual Convolutional Neural Network for text classification. We will explain the approach put in place to solve the problem of classified ads, the difficulties encountered and the experimental results.

Related papers

• Open-Vocabulary Segmentation with Semantic-Assisted Calibration [73.39366775301382]
  We study open-vocabulary segmentation (OVS) through calibrating in-vocabulary and domain-biased embedding space with contextual prior of CLIP. We present a Semantic-assisted CAlibration Network (SCAN) to achieve state-of-the-art performance on open-vocabulary segmentation benchmarks.
  arXiv  Detail & Related papers  (2023-12-07T07:00:09Z)
• A Process for Topic Modelling Via Word Embeddings [0.0]
  This work combines algorithms based on word embeddings, dimensionality reduction, and clustering. The objective is to obtain topics from a set of unclassified texts.
  arXiv  Detail & Related papers  (2023-10-06T15:10:35Z)
• A Transformer-based Approach for Arabic Offline Handwritten Text Recognition [0.0]
  We introduce two alternative architectures for recognizing offline Arabic handwritten text. Our approach can model language dependencies and relies only on the attention mechanism, thereby making it more parallelizable and less complex. Our evaluation on the Arabic KHATT dataset demonstrates that our proposed method outperforms the current state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-07-27T17:51:52Z)
• CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation [56.58365347854647]
  We introduce a novel cost-based approach to adapt vision-language foundation models, notably CLIP. Our method potently adapts CLIP for segmenting seen and unseen classes by fine-tuning its encoders.
  arXiv  Detail & Related papers  (2023-03-21T12:28:21Z)
• Revisiting the Roles of "Text" in Text Games [102.22750109468652]
  This paper investigates the roles of text in the face of different reinforcement learning challenges. We propose a simple scheme to extract relevant contextual information into an approximate state hash. Such a lightweight plug-in achieves competitive performance with state-of-the-art text agents.
  arXiv  Detail & Related papers  (2022-10-15T21:52:39Z)
• Collaborative Domain Blocking: Using federated NLP To Detect Malicious Domains [0.0]
  We propose a novel system that aims to remedy the issues by examining deep textual patterns of network-oriented content. We also propose to use federated learning that allows users to take advantage of each other's localized knowledge/experience regarding what should or should not be blocked on a network.
  arXiv  Detail & Related papers  (2022-10-08T18:52:43Z)
• Speaker Embedding-aware Neural Diarization for Flexible Number of Speakers with Textual Information [55.75018546938499]
  We propose the speaker embedding-aware neural diarization (SEND) method, which predicts the power set encoded labels. Our method achieves lower diarization error rate than the target-speaker voice activity detection.
  arXiv  Detail & Related papers  (2021-11-28T12:51:04Z)
• Transformer Interpretability Beyond Attention Visualization [87.96102461221415]
  Self-attention techniques, and specifically Transformers, are dominating the field of text processing. In this work, we propose a novel way to compute relevancy for Transformer networks.
  arXiv  Detail & Related papers  (2020-12-17T18:56:33Z)
• Unsupervised Domain Adaptation in Semantic Segmentation: a Review [22.366638308792734]
  The aim of this paper is to give an overview of the recent advancements in the Unsupervised Domain Adaptation (UDA) of deep networks for semantic segmentation.
  arXiv  Detail & Related papers  (2020-05-21T20:10:38Z)
• How recurrent networks implement contextual processing in sentiment analysis [21.511325784114142]
  We propose methods for reverse engineering recurrent neural networks (RNNs) to identify and elucidate contextual processing. This work yields a new understanding of how RNNs process contextual information, and provides tools that should provide similar insight more broadly.
  arXiv  Detail & Related papers  (2020-04-17T00:58:30Z)
• Towards Accurate Scene Text Recognition with Semantic Reasoning Networks [52.86058031919856]
  We propose a novel end-to-end trainable framework named semantic reasoning network (SRN) for accurate scene text recognition. GSRM is introduced to capture global semantic context through multi-way parallel transmission. Results on 7 public benchmarks, including regular text, irregular text and non-Latin long text, verify the effectiveness and robustness of the proposed method.
  arXiv  Detail & Related papers  (2020-03-27T09:19:25Z)

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.