Embarrassingly Simple Unsupervised Aspect Based Sentiment Tuple Extraction

• URL: http://arxiv.org/abs/2404.13751v1
• Date: Sun, 21 Apr 2024 19:20:42 GMT
• Title: Embarrassingly Simple Unsupervised Aspect Based Sentiment Tuple Extraction
• Authors: Kevin Scaria, Abyn Scaria, Ben Scaria,
• Abstract summary: We propose a simple and novel unsupervised approach to extract opinion terms and the corresponding sentiment polarity for aspect terms in a sentence. Our experimental evaluations, conducted on four benchmark datasets, demonstrate compelling performance to extract the oriented aspect opinion words.
• Score: 0.6429156819529861
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Aspect Based Sentiment Analysis (ABSA) tasks involve the extraction of fine-grained sentiment tuples from sentences, aiming to discern the author's opinions. Conventional methodologies predominantly rely on supervised approaches; however, the efficacy of such methods diminishes in low-resource domains lacking labeled datasets since they often lack the ability to generalize across domains. To address this challenge, we propose a simple and novel unsupervised approach to extract opinion terms and the corresponding sentiment polarity for aspect terms in a sentence. Our experimental evaluations, conducted on four benchmark datasets, demonstrate compelling performance to extract the aspect oriented opinion words as well as assigning sentiment polarity. Additionally, unsupervised approaches for opinion word mining have not been explored and our work establishes a benchmark for the same.

Related papers

• Goodhart's Law Applies to NLP's Explanation Benchmarks [57.26445915212884]
  We critically examine two sets of metrics: the ERASER metrics (comprehensiveness and sufficiency) and the EVAL-X metrics. We show that we can inflate a model's comprehensiveness and sufficiency scores dramatically without altering its predictions or explanations on in-distribution test inputs. Our results raise doubts about the ability of current metrics to guide explainability research, underscoring the need for a broader reassessment of what precisely these metrics are intended to capture.
  arXiv  Detail & Related papers  (2023-08-28T03:03:03Z)
• Context-Aware Semantic Similarity Measurement for Unsupervised Word Sense Disambiguation [0.0]
  This research proposes a new context-aware approach to unsupervised word sense disambiguation. It provides a flexible mechanism for incorporating contextual information into the similarity measurement process. Our findings underscore the significance of integrating contextual information in semantic similarity measurements.
  arXiv  Detail & Related papers  (2023-05-05T13:50:04Z)
• Unsupervised Learning of Debiased Representations with Pseudo-Attributes [85.5691102676175]
  We propose a simple but effective debiasing technique in an unsupervised manner. We perform clustering on the feature embedding space and identify pseudoattributes by taking advantage of the clustering results. We then employ a novel cluster-based reweighting scheme for learning debiased representation.
  arXiv  Detail & Related papers  (2021-08-06T05:20:46Z)
• WSSOD: A New Pipeline for Weakly- and Semi-Supervised Object Detection [75.80075054706079]
  We propose a weakly- and semi-supervised object detection framework (WSSOD) An agent detector is first trained on a joint dataset and then used to predict pseudo bounding boxes on weakly-annotated images. The proposed framework demonstrates remarkable performance on PASCAL-VOC and MSCOCO benchmark, achieving a high performance comparable to those obtained in fully-supervised settings.
  arXiv  Detail & Related papers  (2021-05-21T11:58:50Z)
• Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition [59.52434325897716]
  We propose a solution, named DMUE, to address the problem of annotation ambiguity from two perspectives. For the former, an auxiliary multi-branch learning framework is introduced to better mine and describe the latent distribution in the label space. For the latter, the pairwise relationship of semantic feature between instances are fully exploited to estimate the ambiguity extent in the instance space.
  arXiv  Detail & Related papers  (2021-04-01T03:21:57Z)
• A Multi-task Learning Framework for Opinion Triplet Extraction [24.983625011760328]
  We present a novel view of ABSA as an opinion triplet extraction task. We propose a multi-task learning framework to jointly extract aspect terms and opinion terms. We evaluate the proposed framework on four SemEval benchmarks for ASBA.
  arXiv  Detail & Related papers  (2020-10-04T08:31:54Z)
• A Commentary on the Unsupervised Learning of Disentangled Representations [63.042651834453544]
  The goal of the unsupervised learning of disentangled representations is to separate the independent explanatory factors of variation in the data without access to supervision. We discuss the theoretical result showing that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases.
  arXiv  Detail & Related papers  (2020-07-28T13:13:45Z)
• MatchGAN: A Self-Supervised Semi-Supervised Conditional Generative Adversarial Network [51.84251358009803]
  We present a novel self-supervised learning approach for conditional generative adversarial networks (GANs) under a semi-supervised setting. We perform augmentation by randomly sampling sensible labels from the label space of the few labelled examples available. Our method surpasses the baseline with only 20% of the labelled examples used to train the baseline.
  arXiv  Detail & Related papers  (2020-06-11T17:14:55Z)
• Aspect Term Extraction using Graph-based Semi-Supervised Learning [1.0499611180329804]
  This paper proposes a graph-based semi-supervised learning approach for aspect term extraction. Every identified token in the review document is classified as aspect or non-aspect term from a small set of labeled tokens. The proposed work is further extended to determine the polarity of the opinion words associated with the identified aspect terms in review sentence.
  arXiv  Detail & Related papers  (2020-02-20T13:11:02Z)

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.