Metaphor, as a common type of linguistic expression, helps people intuitively understand complex concepts in communication, writing, and cognition. Metaphor components, including source-domain words and target-domain words, are critical elements for metaphor identification and interpretation. This article focuses on metaphor components and proposes a metaphor components identification framework employing F eedback-enhanced F eature-driven I n- C ontext L earning (FF-ICL) based on the large language model (LLM). Specifically, in-context learning and feedback mechanisms inspired by human learning are integrated. Firstly, a machine feedback mechanism is designed to perform prior predictions on training samples, constructing a candidate demonstration pool enriched with prediction results and feedback information. Secondly, a multi-head graph attention network (GAT) is introduced to capture the linguistic and structural information embedded in metaphorical expressions, producing feature-rich representations and establishing a vector repository. Based on the repository, the framework retrieves demonstrations most relevant to the input query across different feature dimensions, incorporating in-context prompts to effectively fine-tune the LLM. Experiments and analyses on public datasets demonstrate the superiority of FF-ICL. Furthermore, the metaphor concept mapping experiment validates the crucial role of metaphor components in downstream computational metaphor tasks. Relevant data and codes are available at https://github.com/WXLJZ/FF-ICL .

Emotion recognition in conversations (ERC) has garnered significant attention from the research community. However, due to the complexity of visual scenes and dialogue contextual dependencies in conversations, previous ERC methods fail to handle emotional cues from both visual sources and discourse structures. Furthermore, existing state-of-the-art ERC models are trained and tested separately on each single ERC dataset, not verifying their effectiveness across multiple datasets simultaneously. To address these challenges, this paper proposes an innovative framework for ERC, called Dialogue Scenes Understanding Enhanced Multi-modal Multi-task Tuning (DialogueMMT). More concretely, a novel video-language connector is applied within the large vision-language model for capturing video features effectively. Additionally, we utilize multi-task instruction tuning with a unified ERC dataset to enhance the model’s understanding of multi-modal dialogue scenes and employ a chain-of-thought strategy to improve emotion classification performance. Extensive experimental results on three benchmark ERC datasets indicate that the proposed DialogueMMT framework consistently outperforms existing state-of-the-art approaches in terms of overall performance.

Entities are important to understanding literary works, which emphasize characters, plots and environment. The research on entity recognition, especially nested entity recognition in the literary domain is still insufficient partly due to insufficient annotated data. To address this issue, we construct the first Genre-oriented Corpus for Entity Recognition in Chinese Web Novels, namely GenWebNovel, comprising 400 chapters totaling 1,214,283 tokens under two genres, XuanHuan (Eastern Fantasy) and History. Based on the corpus, we analyze the distribution of different types of entities, including person, location, and organization. We also compare the nesting patterns of nested entities between GenWebNovel and the English corpus LitBank. Even though both belong to the literary domain, entities in different genres share few overlaps, making genre adaptation of NER (Named Entity Recognition) a hard problem. We propose a novel method that utilizes a pre-trained language model as an In-context learning example retriever to boost the performance of large language models. Our experiments show that this approach significantly enhances entity recognition, matching state-of-the-art (SOTA) models without requiring additional training data. Our code, dataset, and model are available at https://github.com/hjzhao73/GenWebNovel.

In recent years, fine-grained sentiment analysis in finance has gained significant attention, but the scarcity of entity-level datasets remains a key challenge. To address this, we have constructed the largest English and Chinese financial entity-level sentiment analysis datasets to date. Building on this foundation, we propose a novel two-stage sentiment analysis approach called Self-aware In-context Learning Correction (SILC). The first stage involves fine-tuning a base large language model to generate pseudo-labeled data specific to our task. In the second stage, we train a correction model using a GNN-based example retriever, which is informed by the pseudo-labeled data. This two-stage strategy has allowed us to achieve state-of-the-art performance on the newly constructed datasets, advancing the field of financial sentiment analysis. In a case study, we demonstrate the enhanced practical utility of our data and methods in monitoring the cryptocurrency market. Our datasets and code are available at https://github.com/NLP-Bin/SILC-EFSA.

This paper focuses on Dialogue Aspect-based Sentiment Quadruple (DiaASQ) analysis, aiming to extract structured quadruples from multiturn conversations.Applying Large Language Models (LLMs) for this specific task presents two primary challenges: the accurate extraction of multiple elements and the understanding of complex dialogue reply structure.To tackle these issues, we propose a novel LLMbased multi-task approach, named Task-aware Contrastive Mixture of Experts (TaCoMoE), to tackle the DiaASQ task by integrating expertlevel contrastive loss within task-oriented mixture of experts layer.TaCoMoE minimizes the distance between the representations of the same expert in the semantic space while maximizing the distance between the representations of different experts to efficiently learn representations of different task samples.Additionally, we design a Graph-Centric Dialogue Structuring strategy for representing dialogue reply structure and perform non-opinion utterances detection to enhance the performance of quadruple extraction.Extensive experiments are conducted on the DiaASQ dataset, demonstrating that our method significantly outperforms existing parameter-efficient fine-tuning techniques in terms of both accuracy and computational efficiency.The code is available at https://github.com/he2720/TaCoMoE.

Multi-domain aspect-based sentiment analysis (ABSA) seeks to capture fine-grained sentiment across diverse domains. While existing research narrowly focuses on single-domain applications constrained by methodological limitations and data scarcity, the reality is that sentiment naturally traverses multiple domains. Although large language models (LLMs) offer a promising solution for ABSA, it is difficult to integrate effectively with established techniques, including graph-based models and linguistics, because modifying their internal architecture is not easy. To alleviate this problem, we propose a novel framework, Feature-aware In-context Learning for Multi-domain ABSA (FaiMA). The core insight of FaiMA is to utilize in-context learning (ICL) as a feature-aware mechanism that facilitates adaptive learning in multi-domain ABSA tasks. Specifically, we employ a multi-head graph attention network as a text encoder optimized by heuristic rules for linguistic, domain, and sentiment features. Through contrastive learning, we optimize sentence representations by focusing on these diverse features. Additionally, we construct an efficient indexing mechanism, allowing FaiMA to stably retrieve highly relevant examples across multiple dimensions for any given input. To evaluate the efficacy of FaiMA, we build the first multi-domain ABSA benchmark dataset. Extensive experimental results demonstrate that FaiMA achieves significant performance improvements in multiple domains compared to baselines, increasing F1 by 2.07% on average. Source code and data sets are available at https://github.com/SupritYoung/FaiMA.