Exploring Ordinality in Text Classification: A Comparative Study of Explicit and Implicit Techniques

• URL: http://arxiv.org/abs/2405.11775v1
• Date: Mon, 20 May 2024 04:31:04 GMT
• Title: Exploring Ordinality in Text Classification: A Comparative Study of Explicit and Implicit Techniques
• Authors: Siva Rajesh Kasa, Aniket Goel, Karan Gupta, Sumegh Roychowdhury, Anish Bhanushali, Nikhil Pattisapu, Prasanna Srinivasa Murthy,
• Abstract summary: Ordinal Classification (OC) is a widely encountered challenge in Natural Language Processing (NLP) Previous approaches to tackle OC have primarily focused on modifying existing or creating novel loss functions that textbfexplicitly account for the ordinal nature of labels. With the advent of Pretrained Language Models (PLMs), it became possible to tackle ordinality through the textbfimplicit semantics of the labels as well.
• Score: 3.197435100145382
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ordinal Classification (OC) is a widely encountered challenge in Natural Language Processing (NLP), with applications in various domains such as sentiment analysis, rating prediction, and more. Previous approaches to tackle OC have primarily focused on modifying existing or creating novel loss functions that \textbf{explicitly} account for the ordinal nature of labels. However, with the advent of Pretrained Language Models (PLMs), it became possible to tackle ordinality through the \textbf{implicit} semantics of the labels as well. This paper provides a comprehensive theoretical and empirical examination of both these approaches. Furthermore, we also offer strategic recommendations regarding the most effective approach to adopt based on specific settings.

Related papers

• Detecting Statements in Text: A Domain-Agnostic Few-Shot Solution [1.3654846342364308]
  State-of-the-art approaches usually involve fine-tuning models on large annotated datasets, which are costly to produce. We propose and release a qualitative and versatile few-shot learning methodology as a common paradigm for any claim-based textual classification task. We illustrate this methodology in the context of three tasks: climate change contrarianism detection, topic/stance classification and depression-relates symptoms detection.
  arXiv  Detail & Related papers  (2024-05-09T12:03:38Z)
• Cross-domain Chinese Sentence Pattern Parsing [67.1381983012038]
  Sentence Pattern Structure (SPS) parsing is a syntactic analysis method primarily employed in language teaching. Existing SPSs rely heavily on textbook corpora for training, lacking cross-domain capability. This paper proposes an innovative approach leveraging large language models (LLMs) within a self-training framework.
  arXiv  Detail & Related papers  (2024-02-26T05:30:48Z)
• 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)
• 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)
• A Comprehensive Empirical Evaluation of Existing Word Embedding Approaches [5.065947993017158]
  We present the characteristics of existing word embedding approaches and analyze them with regard to many classification tasks. Traditional approaches mostly use matrix factorization to produce word representations, and they are not able to capture the semantic and syntactic regularities of the language very well. On the other hand, Neural-network-based approaches can capture sophisticated regularities of the language and preserve the word relationships in the generated word representations.
  arXiv  Detail & Related papers  (2023-03-13T15:34:19Z)
• Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches [0.6767885381740952]
  Similarity-based approaches attempt to classify instances based on similarities between text document representations and class description representations. Zero-shot text classification approaches aim to generalize knowledge gained from a training task by assigning appropriate labels of unknown classes to text documents. This paper conducts a systematic evaluation of different similarity-based and zero-shot approaches for text classification of unseen classes.
  arXiv  Detail & Related papers  (2022-11-29T15:14:47Z)
• f-Domain-Adversarial Learning: Theory and Algorithms [82.97698406515667]
  Unsupervised domain adaptation is used in many machine learning applications where, during training, a model has access to unlabeled data in the target domain. We derive a novel generalization bound for domain adaptation that exploits a new measure of discrepancy between distributions based on a variational characterization of f-divergences.
  arXiv  Detail & Related papers  (2021-06-21T18:21:09Z)
• Set-valued classification -- overview via a unified framework [15.109906768606644]
  Multi-class datasets can be extremely ambiguous and single-output predictions fail to deliver satisfactory performance. By allowing predictors to predict a set of label candidates, set-valued classification offers a natural way to deal with this ambiguity. We provide infinite sample optimal set-valued classification strategies and review a general plug-in principle to construct data-driven algorithms.
  arXiv  Detail & Related papers  (2021-02-24T14:54:07Z)
• Multitask Learning for Class-Imbalanced Discourse Classification [74.41900374452472]
  We show that a multitask approach can improve 7% Micro F1-score upon current state-of-the-art benchmarks. We also offer a comparative review of additional techniques proposed to address resource-poor problems in NLP.
  arXiv  Detail & Related papers  (2021-01-02T07:13:41Z)
• Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding [71.2260967797055]
  We propose a weakly-supervised approach for aspect-based sentiment analysis. We learn sentiment, aspect> joint topic embeddings in the word embedding space. We then use neural models to generalize the word-level discriminative information.
  arXiv  Detail & Related papers  (2020-10-13T21:33:24Z)

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.