Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction

• URL: http://arxiv.org/abs/2106.03315v1
• Date: Mon, 7 Jun 2021 03:16:51 GMT
• Title: Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction
• Authors: Zhexue Chen, Hong Huang, Bang Liu, Xuanhua Shi, Hai Jin
• Abstract summary: 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.
• Score: 18.331779474247323
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aspect Sentiment Triplet Extraction (ASTE) 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. Most existing research addresses this problem in a multi-stage pipeline manner, which neglects the mutual information between such three elements and has the problem of error propagation. In this paper, 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. Specifically, we design a Graph-Sequence duel representation and modeling paradigm for the task of ASTE: we represent the semantic and syntactic relationships between word pairs in a sentence by graph and encode it by Graph Neural Networks (GNNs), as well as modeling the original sentence by LSTM to preserve the sequential information. Under this setting, we further apply a more efficient inference strategy for the extraction of triplets. Extensive evaluations on four benchmark datasets show that S3E2 significantly outperforms existing approaches, which proves our S3E2's superiority and flexibility in an end-to-end fashion.

Related papers

• A Bi-consolidating Model for Joint Relational Triple Extraction [3.972061685570092]
  Current methods to extract relational triples directly make a prediction based on a possible entity pair in a raw sentence without depending on entity recognition. The task suffers from a serious semantic overlapping problem, in which several relation triples may share one or two entities in a sentence. A bi-consolidating model is proposed to address this problem by simultaneously reinforcing the local and global semantic features relevant to a relation triple.
  arXiv  Detail & Related papers  (2024-04-05T04:04:23Z)
• Dual Encoder: Exploiting the Potential of Syntactic and Semantic for Aspect Sentiment Triplet Extraction [19.375196127313348]
  Aspect Sentiment Triple Extraction (ASTE) is an emerging task in fine-grained sentiment analysis. We propose a dual-channel encoder with a BERT channel to capture semantic information, and an enhanced LSTM channel for comprehensive syntactic information capture. We leverage the synergy of these modules to harness the significant potential of syntactic and semantic information in ASTE tasks.
  arXiv  Detail & Related papers  (2024-02-23T15:07:13Z)
• S^2Former-OR: Single-Stage Bi-Modal Transformer for Scene Graph Generation in OR [50.435592120607815]
  Scene graph generation (SGG) of surgical procedures is crucial in enhancing holistically cognitive intelligence in the operating room (OR) Previous works have primarily relied on multi-stage learning, where the generated semantic scene graphs depend on intermediate processes with pose estimation and object detection. In this study, we introduce a novel single-stage bi-modal transformer framework for SGG in the OR, termed S2Former-OR.
  arXiv  Detail & Related papers  (2024-02-22T11:40:49Z)
• 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)
• Collaborative Group: Composed Image Retrieval via Consensus Learning from Noisy Annotations [67.92679668612858]
  We propose the Consensus Network (Css-Net), inspired by the psychological concept that groups outperform individuals. Css-Net comprises two core components: (1) a consensus module with four diverse compositors, each generating distinct image-text embeddings; and (2) a Kullback-Leibler divergence loss that encourages learning of inter-compositor interactions. On benchmark datasets, particularly FashionIQ, Css-Net demonstrates marked improvements. Notably, it achieves significant recall gains, with a 2.77% increase in R@10 and 6.67% boost in R@50, underscoring its
  arXiv  Detail & Related papers  (2023-06-03T11:50:44Z)
• HIORE: Leveraging High-order Interactions for Unified Entity Relation Extraction [85.80317530027212]
  We propose HIORE, a new method for unified entity relation extraction. The key insight is to leverage the complex association among word pairs, which contains richer information than the first-order word-by-word interactions. Experiments show that HIORE achieves the state-of-the-art performance on relation extraction and an improvement of 1.11.8 F1 points over the prior best unified model.
  arXiv  Detail & Related papers  (2023-05-07T14:57:42Z)
• STAGE: Span Tagging and Greedy Inference Scheme for Aspect Sentiment Triplet Extraction [17.192861356588597]
  Aspect Sentiment Triplet Extraction (ASTE) has become an emerging task in sentiment analysis research. We propose Span TAgging and Greedy infErence (STAGE) to extract sentiment triplets in span-level.
  arXiv  Detail & Related papers  (2022-11-28T02:07:03Z)
• 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)
• Semantic Compositional Learning for Low-shot Scene Graph Generation [122.51930904132685]
  Many scene graph generation (SGG) models solely use the limited annotated relation triples for training. We propose a novel semantic compositional learning strategy that makes it possible to construct additional, realistic relation triples. For three recent SGG models, adding our strategy improves their performance by close to 50%, and all of them substantially exceed the current state-of-the-art.
  arXiv  Detail & Related papers  (2021-08-19T10:13:55Z)
• Syntactic representation learning for neural network based TTS with syntactic parse tree traversal [49.05471750563229]
  We propose a syntactic representation learning method based on syntactic parse tree to automatically utilize the syntactic structure information. Experimental results demonstrate the effectiveness of our proposed approach. For sentences with multiple syntactic parse trees, prosodic differences can be clearly perceived from the synthesized speeches.
  arXiv  Detail & Related papers  (2020-12-13T05:52:07Z)
• Position-Aware Tagging for Aspect Sentiment Triplet Extraction [37.76744150888183]
  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.
  arXiv  Detail & Related papers  (2020-10-06T10:40:34Z)

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.