Chinese Financial Text Emotion Mining: GCGTS -- A Character Relationship-based Approach for Simultaneous Aspect-Opinion Pair Extraction

• URL: http://arxiv.org/abs/2308.02113v1
• Date: Fri, 4 Aug 2023 02:20:56 GMT
• Title: Chinese Financial Text Emotion Mining: GCGTS -- A Character Relationship-based Approach for Simultaneous Aspect-Opinion Pair Extraction
• Authors: Qi Chen, Dexi Liu
• Abstract summary: Aspect-Opinion Pair Extraction (AOPE) from Chinese financial texts is a specialized task in fine-grained text sentiment analysis. Previous studies have mainly focused on developing grid annotation schemes within grid-based models to facilitate this extraction process. We propose a novel method called Graph-based Character-level Grid Tagging Scheme (GCGTS) The GCGTS method explicitly incorporates syntactic structure using Graph Convolutional Networks (GCN) and unifies the encoding of characters within the same semantic unit (Chinese word level)
• Score: 7.484918031250864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aspect-Opinion Pair Extraction (AOPE) from Chinese financial texts is a specialized task in fine-grained text sentiment analysis. The main objective is to extract aspect terms and opinion terms simultaneously from a diverse range of financial texts. Previous studies have mainly focused on developing grid annotation schemes within grid-based models to facilitate this extraction process. However, these methods often rely on character-level (token-level) feature encoding, which may overlook the logical relationships between Chinese characters within words. To address this limitation, we propose a novel method called Graph-based Character-level Grid Tagging Scheme (GCGTS). The GCGTS method explicitly incorporates syntactic structure using Graph Convolutional Networks (GCN) and unifies the encoding of characters within the same syntactic semantic unit (Chinese word level). Additionally, we introduce an image convolutional structure into the grid model to better capture the local relationships between characters within evaluation units. This innovative structure reduces the excessive reliance on pre-trained language models and emphasizes the modeling of structure and local relationships, thereby improving the performance of the model on Chinese financial texts. Through comparative experiments with advanced models such as Synchronous Double-channel Recurrent Network (SDRN) and Grid Tagging Scheme (GTS), the proposed GCGTS model demonstrates significant improvements in performance.

Related papers

• A Pure Transformer Pretraining Framework on Text-attributed Graphs [50.833130854272774]
  We introduce a feature-centric pretraining perspective by treating graph structure as a prior. Our framework, Graph Sequence Pretraining with Transformer (GSPT), samples node contexts through random walks. GSPT can be easily adapted to both node classification and link prediction, demonstrating promising empirical success on various datasets.
  arXiv  Detail & Related papers  (2024-06-19T22:30:08Z)
• Unleashing the Potential of Text-attributed Graphs: Automatic Relation Decomposition via Large Language Models [31.443478448031886]
  RoSE (Relation-oriented Semantic Edge-decomposition) is a novel framework that decomposes the graph structure by analyzing raw text attributes. Our framework significantly enhances node classification performance across various datasets, with improvements of up to 16% on the Wisconsin dataset.
  arXiv  Detail & Related papers  (2024-05-28T20:54:47Z)
• Cross-domain Chinese Sentence Pattern Parsing [67.1381983012038]
  Sentence Pattern Structure (SPS) parsing is a syntactic analysis method primarily employed in language teaching. Existing SPSs rely heavily on textbook corpora for training, lacking cross-domain capability. This paper proposes an innovative approach leveraging large language models (LLMs) within a self-training framework.
  arXiv  Detail & Related papers  (2024-02-26T05:30:48Z)
• 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)
• Improving Graph-Based Text Representations with Character and Word Level N-grams [30.699644290131044]
  We propose a new word-character text graph that combines word and character n-gram nodes together with document nodes. We also propose two new graph-based neural models, WCTextGCN and WCTextGAT, for modeling our proposed text graph.
  arXiv  Detail & Related papers  (2022-10-12T08:07:54Z)
• 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)
• SubGraph Networks based Entity Alignment for Cross-lingual Knowledge Graph [7.892065498202909]
  We introduce the subgraph network (SGN) method into the GCN-based cross-lingual KG entity alignment method. Experiments show that the proposed method outperforms the state-of-the-art GCN-based method.
  arXiv  Detail & Related papers  (2022-05-07T05:13:15Z)
• GN-Transformer: Fusing Sequence and Graph Representation for Improved Code Summarization [0.0]
  We propose a novel method, GN-Transformer, to learn end-to-end on a fused sequence and graph modality. The proposed methods achieve state-of-the-art performance in two code summarization datasets and across three automatic code summarization metrics.
  arXiv  Detail & Related papers  (2021-11-17T02:51:37Z)
• Graph Convolutional Network for Swahili News Classification [78.6363825307044]
  This work empirically demonstrates the ability of Text Graph Convolutional Network (Text GCN) to outperform traditional natural language processing benchmarks for the task of semi-supervised Swahili news classification.
  arXiv  Detail & Related papers  (2021-03-16T21:03:47Z)
• GTAE: Graph-Transformer based Auto-Encoders for Linguistic-Constrained Text Style Transfer [119.70961704127157]
  Non-parallel text style transfer has attracted increasing research interests in recent years. Current approaches still lack the ability to preserve the content and even logic of original sentences. We propose a method called Graph Transformer based Auto-GTAE, which models a sentence as a linguistic graph and performs feature extraction and style transfer at the graph level.
  arXiv  Detail & Related papers  (2021-02-01T11:08:45Z)

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.