Estimating the Influence of Sequentially Correlated Literary Properties in Textual Classification: A Data-Centric Hypothesis-Testing Approach

• URL: http://arxiv.org/abs/2411.04950v1
• Date: Thu, 07 Nov 2024 18:28:40 GMT
• Title: Estimating the Influence of Sequentially Correlated Literary Properties in Textual Classification: A Data-Centric Hypothesis-Testing Approach
• Authors: Gideon Yoffe, Nachum Dershowitz, Ariel Vishne, Barak Sober,
• Abstract summary: Stylometry aims to distinguish authors by analyzing literary traits assumed to reflect semi-conscious choices distinct from elements like genre or theme. While some literary properties, such as thematic content, are likely to manifest as correlations between adjacent text units, others, like authorial style, may be independent thereof. We introduce a hypothesis-testing approach to evaluate the influence of sequentially correlated literary properties on text classification.
• Score: 4.161155428666988
• License:
• Abstract: Stylometry aims to distinguish authors by analyzing literary traits assumed to reflect semi-conscious choices distinct from elements like genre or theme. However, these components often overlap, complicating text classification based solely on feature distributions. While some literary properties, such as thematic content, are likely to manifest as correlations between adjacent text units, others, like authorial style, may be independent thereof. We introduce a hypothesis-testing approach to evaluate the influence of sequentially correlated literary properties on text classification, aiming to determine when these correlations drive classification. Using a multivariate binary distribution, our method models sequential correlations between text units as a stochastic process, assessing the likelihood of clustering across varying adjacency scales. This enables us to examine whether classification is dominated by sequentially correlated properties or remains independent. In experiments on a diverse English prose corpus, our analysis integrates traditional and neural embeddings within supervised and unsupervised frameworks. Results demonstrate that our approach effectively identifies when textual classification is not primarily influenced by sequentially correlated literary properties, particularly in cases where texts differ in authorial style or genre rather than by a single author within a similar genre.

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)
• A Comparative Study of Sentence Embedding Models for Assessing Semantic Variation [0.0]
  We compare several recent sentence embedding methods via time-series of semantic similarity between successive sentences and matrices of pairwise sentence similarity for multiple books of literature. We find that most of the sentence embedding methods considered do infer highly correlated patterns of semantic similarity in a given document, but show interesting differences.
  arXiv  Detail & Related papers  (2023-08-08T23:31:10Z)
• Topics in the Haystack: Extracting and Evaluating Topics beyond Coherence [0.0]
  We propose a method that incorporates a deeper understanding of both sentence and document themes. This allows our model to detect latent topics that may include uncommon words or neologisms. We present correlation coefficients with human identification of intruder words and achieve near-human level results at the word-intrusion task.
  arXiv  Detail & Related papers  (2023-03-30T12:24:25Z)
• 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)
• Textual Entailment Recognition with Semantic Features from Empirical Text Representation [60.31047947815282]
  A text entails a hypothesis if and only if the true value of the hypothesis follows the text. In this paper, we propose a novel approach to identifying the textual entailment relationship between text and hypothesis. We employ an element-wise Manhattan distance vector-based feature that can identify the semantic entailment relationship between the text-hypothesis pair.
  arXiv  Detail & Related papers  (2022-10-18T10:03:51Z)
• Author Clustering and Topic Estimation for Short Texts [69.54017251622211]
  We propose a novel model that expands on the Latent Dirichlet Allocation by modeling strong dependence among the words in the same document. We also simultaneously cluster users, removing the need for post-hoc cluster estimation. Our method performs as well as -- or better -- than traditional approaches to problems arising in short text.
  arXiv  Detail & Related papers  (2021-06-15T20:55:55Z)
• Semantic Analysis for Automated Evaluation of the Potential Impact of Research Articles [62.997667081978825]
  This paper presents a novel method for vector representation of text meaning based on information theory. We show how this informational semantics is used for text classification on the basis of the Leicester Scientific Corpus. We show that an informational approach to representing the meaning of a text has offered a way to effectively predict the scientific impact of research papers.
  arXiv  Detail & Related papers  (2021-04-26T20:37:13Z)
• 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)
• Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection [69.2370349274216]
  Few-shot Intent Detection is challenging due to the scarcity of available annotated utterances. Semantic components are distilled from utterances via multi-head self-attention. Our method provides a comprehensive matching measure to enhance representations of both labeled and unlabeled instances.
  arXiv  Detail & Related papers  (2020-10-06T05:16:38Z)
• Identifying Spurious Correlations for Robust Text Classification [9.457737910527829]
  We propose a method to distinguish spurious and genuine correlations in text classification. We use features derived from treatment effect estimators to distinguish spurious correlations from "genuine" ones. Experiments on four datasets suggest that using this approach to inform feature selection also leads to more robust classification.
  arXiv  Detail & Related papers  (2020-10-06T03:49:22Z)
• A computational model implementing subjectivity with the 'Room Theory'. The case of detecting Emotion from Text [68.8204255655161]
  This work introduces a new method to consider subjectivity and general context dependency in text analysis. By using similarity measure between words, we are able to extract the relative relevance of the elements in the benchmark. This method could be applied to all the cases where evaluating subjectivity is relevant to understand the relative value or meaning of a text.
  arXiv  Detail & Related papers  (2020-05-12T21:26:04Z)

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.