Position-Aware Tagging for Aspect Sentiment Triplet Extraction

• URL: http://arxiv.org/abs/2010.02609v3
• Date: Tue, 9 Mar 2021 15:38:12 GMT
• Title: Position-Aware Tagging for Aspect Sentiment Triplet Extraction
• Authors: Lu Xu, Hao Li, Wei Lu, and Lidong Bing
• Abstract summary: Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the sentiment. Our observation is that the three elements within a triplet are highly related to each other, and this motivates us to build a joint model to extract such triplets. We propose the first end-to-end model with a novel position-aware tagging scheme that is capable of jointly extracting the triplets.
• Score: 37.76744150888183
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the sentiment. Existing research efforts mostly solve this problem using pipeline approaches, which break the triplet extraction process into several stages. Our observation is that the three elements within a triplet are highly related to each other, and this motivates us to build a joint model to extract such triplets using a sequence tagging approach. However, how to effectively design a tagging approach to extract the triplets that can capture the rich interactions among the elements is a challenging research question. In this work, we propose the first end-to-end model with a novel position-aware tagging scheme that is capable of jointly extracting the triplets. Our experimental results on several existing datasets show that jointly capturing elements in the triplet using our approach leads to improved performance over the existing approaches. We also conducted extensive experiments to investigate the model effectiveness and robustness.

Related papers

• Few-shot Knowledge Graph Relational Reasoning via Subgraph Adaptation [51.47994645529258]
  Few-shot Knowledge Graph (KG) Reasoning aims to predict unseen triplets (i.e., query triplets) for rare relations in KGs. We propose SAFER (Subgraph Adaptation for Few-shot Reasoning), a novel approach that effectively adapts the information in contextualized graphs to various subgraphs.
  arXiv  Detail & Related papers  (2024-06-19T21:40:35Z)
• Prompt Based Tri-Channel Graph Convolution Neural Network for Aspect Sentiment Triplet Extraction [63.0205418944714]
  Aspect Sentiment Triplet Extraction (ASTE) is an emerging task to extract a given sentence's triplets, which consist of aspects, opinions, and sentiments. Recent studies tend to address this task with a table-filling paradigm, wherein word relations are encoded in a two-dimensional table. We propose a novel model for the ASTE task, called Prompt-based Tri-Channel Graph Convolution Neural Network (PT-GCN), which converts the relation table into a graph to explore more comprehensive relational information.
  arXiv  Detail & Related papers  (2023-12-18T12:46:09Z)
• A Pairing Enhancement Approach for Aspect Sentiment Triplet Extraction [3.5838781091072143]
  Aspect Sentiment Triplet Extraction aims to extract the triplet of an aspect term, an opinion term, and their corresponding sentiment polarity from the review texts. Due to the complexity of language and the existence of multiple aspect terms and opinion terms in a single sentence, current models often confuse the connections between an aspect term and the opinion term describing it. We propose a pairing enhancement approach for ASTE, which incorporates contrastive learning during the training stage to inject aspect-opinion pairing knowledge into the triplet extraction model.
  arXiv  Detail & Related papers  (2023-06-11T07:32:10Z)
• Span-level Bidirectional Cross-attention Framework for Aspect Sentiment Triplet Extraction [10.522014946035664]
  Aspect Sentiment Triplet Extraction (ASTE) is a new fine-grained sentiment analysis task that aims to extract triplets of aspect terms, sentiments, and opinion terms from review sentences. We propose a span-level bidirectional cross-attention framework for ASTE. Our framework significantly outperforms state-of-the-art methods, achieving better performance in predicting triplets with multi-token entities.
  arXiv  Detail & Related papers  (2022-04-27T02:55:43Z)
• PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction [12.921737393688245]
  Aspect Sentiment Triplet Extraction (ASTE) deals with extracting opinion triplets, consisting of an opinion target or aspect, its associated sentiment, and the corresponding opinion term/span. We adapt an encoder-decoder architecture with a Pointer Network-based decoding framework that generates an entire opinion triplet at each time step.
  arXiv  Detail & Related papers  (2021-10-10T13:39:39Z)
• Aspect Sentiment Triplet Extraction Using Reinforcement Learning [14.21689018940387]
  We present a novel paradigm, ASTE-RL, by regarding the aspect and opinion terms as arguments of the expressed sentiment. We first focus on sentiments expressed in a sentence, then identify the target aspect and opinion terms for that sentiment. This takes into account the mutual interactions among the triplet's components while improving exploration and sample efficiency.
  arXiv  Detail & Related papers  (2021-08-13T07:38:48Z)
• Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction [18.331779474247323]
  Aspect Sentiment Triplet Extraction aims to extract triplets from sentences, where each triplet includes an entity, its associated sentiment, and the opinion span explaining the reason for the sentiment. We propose a Semantic and Syntactic Enhanced aspect Sentiment triplet Extraction model (S3E2) to fully exploit the syntactic and semantic relationships between the triplet elements and jointly extract them.
  arXiv  Detail & Related papers  (2021-06-07T03:16:51Z)
• First Target and Opinion then Polarity: Enhancing Target-opinion Correlation for Aspect Sentiment Triplet Extraction [45.82241446769157]
  Aspect Sentiment Triplet Extraction (ASTE) aims to extract triplets from a sentence, including target entities, associated sentiment polarities, and opinion spans which rationalize the polarities. Existing methods are short on building correlation between target-opinion pairs, and neglect the mutual interference among different sentiment triplets. We propose a novel two-stage method which enhances the correlation between targets and opinions through sequence tagging.
  arXiv  Detail & Related papers  (2021-02-17T03:28:17Z)
• Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks [61.950353376870154]
  Joint-event-extraction is a sequence-to-sequence labeling task with a tag set composed of tags of triggers and entities. We propose a Cross-Supervised Mechanism (CSM) to alternately supervise the extraction of triggers or entities. Our approach outperforms the state-of-the-art methods in both entity and trigger extraction.
  arXiv  Detail & Related papers  (2020-10-13T11:51:17Z)
• Contrastive Triple Extraction with Generative Transformer [72.21467482853232]
  We introduce a novel model, contrastive triple extraction with a generative transformer. Specifically, we introduce a single shared transformer module for encoder-decoder-based generation. To generate faithful results, we propose a novel triplet contrastive training object.
  arXiv  Detail & Related papers  (2020-09-14T05:29:24Z)

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.