Learn from Syntax: Improving Pair-wise Aspect and Opinion Terms Extractionwith Rich Syntactic Knowledge

• URL: http://arxiv.org/abs/2105.02520v1
• Date: Thu, 6 May 2021 08:45:40 GMT
• Title: Learn from Syntax: Improving Pair-wise Aspect and Opinion Terms Extractionwith Rich Syntactic Knowledge
• Authors: Shengqiong Wu and Hao Fei and Yafeng Ren and Donghong Ji and Jingye Li
• Abstract summary: We propose to enhance the pair-wise aspect and opinion terms extraction (PAOTE) task by incorporating rich syntactic knowledge. We first build a syntax fusion encoder for encoding syntactic features, including a label-aware graph convolutional network (LAGCN) for modeling the dependency edges and labels. During pairing, we then adopt Biaffine and Triaffine scoring for high-order aspect-opinion term pairing, in the meantime re-harnessing the syntax-enriched representations in LAGCN for syntactic-aware scoring.
• Score: 17.100366742363803
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose to enhance the pair-wise aspect and opinion terms extraction (PAOTE) task by incorporating rich syntactic knowledge. We first build a syntax fusion encoder for encoding syntactic features, including a label-aware graph convolutional network (LAGCN) for modeling the dependency edges and labels, as well as the POS tags unifiedly, and a local-attention module encoding POS tags for better term boundary detection. During pairing, we then adopt Biaffine and Triaffine scoring for high-order aspect-opinion term pairing, in the meantime re-harnessing the syntax-enriched representations in LAGCN for syntactic-aware scoring. Experimental results on four benchmark datasets demonstrate that our model outperforms current state-of-the-art baselines, meanwhile yielding explainable predictions with syntactic knowledge.

Related papers

• RCA-NOC: Relative Contrastive Alignment for Novel Object Captioning [18.13275250206568]
  We introduce a novel approach to novel object captioning which employs relative contrastive learning to learn visual and semantic alignment. We evaluate our approach on two datasets and show that our proposed RCA-NOC approach outperforms state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2023-12-11T11:06:32Z)
• A semantically enhanced dual encoder for aspect sentiment triplet extraction [0.7291396653006809]
  Aspect sentiment triplet extraction (ASTE) is a crucial subtask of aspect-based sentiment analysis (ABSA) Previous research has focused on enhancing ASTE through innovative table-filling strategies. We propose a framework that leverages both a basic encoder, primarily based on BERT, and a particular encoder comprising a Bi-LSTM network and graph convolutional network (GCN) Experiments conducted on benchmark datasets demonstrate the state-of-the-art performance of our proposed framework.
  arXiv  Detail & Related papers  (2023-06-14T09:04:14Z)
• Scalable Learning of Latent Language Structure With Logical Offline Cycle Consistency [71.42261918225773]
  Conceptually, LOCCO can be viewed as a form of self-learning where the semantic being trained is used to generate annotations for unlabeled text. As an added bonus, the annotations produced by LOCCO can be trivially repurposed to train a neural text generation model.
  arXiv  Detail & Related papers  (2023-05-31T16:47:20Z)
• Neural Machine Translation with Dynamic Graph Convolutional Decoder [32.462919670070654]
  We propose an end-to-end translation architecture from the (graph & sequence) structural inputs to the (graph & sequence) outputs, where the target translation and its corresponding syntactic graph are jointly modeled and generated. We conduct extensive experiments on five widely acknowledged translation benchmarks, verifying our proposal achieves consistent improvements over baselines and other syntax-aware variants.
  arXiv  Detail & Related papers  (2023-05-28T11:58:07Z)
• Multilingual Extraction and Categorization of Lexical Collocations with Graph-aware Transformers [86.64972552583941]
  We put forward a sequence tagging BERT-based model enhanced with a graph-aware transformer architecture, which we evaluate on the task of collocation recognition in context. Our results suggest that explicitly encoding syntactic dependencies in the model architecture is helpful, and provide insights on differences in collocation typification in English, Spanish and French.
  arXiv  Detail & Related papers  (2022-05-23T16:47:37Z)
• Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification [68.06496970320595]
  Cross-domain sentiment classification (CDSC) aims to use the transferable semantics learned from the source domain to predict the sentiment of reviews in the unlabeled target domain. We present Graph Adaptive Semantic Transfer (GAST) model, an adaptive syntactic graph embedding method that is able to learn domain-invariant semantics from both word sequences and syntactic graphs.
  arXiv  Detail & Related papers  (2022-05-18T07:47:01Z)
• Stacked Hybrid-Attention and Group Collaborative Learning for Unbiased Scene Graph Generation [62.96628432641806]
  Scene Graph Generation aims to first encode the visual contents within the given image and then parse them into a compact summary graph. We first present a novel Stacked Hybrid-Attention network, which facilitates the intra-modal refinement as well as the inter-modal interaction. We then devise an innovative Group Collaborative Learning strategy to optimize the decoder.
  arXiv  Detail & Related papers  (2022-03-18T09:14:13Z)
• KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction [111.74812895391672]
  We propose a Knowledge-aware Prompt-tuning approach with synergistic optimization (KnowPrompt) We inject latent knowledge contained in relation labels into prompt construction with learnable virtual type words and answer words.
  arXiv  Detail & Related papers  (2021-04-15T17:57:43Z)
• Keyphrase Extraction with Dynamic Graph Convolutional Networks and Diversified Inference [50.768682650658384]
  Keyphrase extraction (KE) aims to summarize a set of phrases that accurately express a concept or a topic covered in a given document. Recent Sequence-to-Sequence (Seq2Seq) based generative framework is widely used in KE task, and it has obtained competitive performance on various benchmarks. In this paper, we propose to adopt the Dynamic Graph Convolutional Networks (DGCN) to solve the above two problems simultaneously.
  arXiv  Detail & Related papers  (2020-10-24T08:11:23Z)
• Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation [14.823029993354794]
  Event detection is a key subtask of information extraction. We propose a novel architecture named Edge-Enhanced Graph Convolution Networks (EE-GCN) EE-GCN simultaneously exploits syntactic structure and typed dependency label information to perform ED.
  arXiv  Detail & Related papers  (2020-02-25T09:18:26Z)

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.