While sentiment analysis systems try to determine the polarities of given targets based on key opinion expressions in input texts, implicit (ISA) cues come an and obscure manner. Thus detecting requires common-sense multi-hop reasoning ability infer latent intent opinion. Inspired by recent chain-of-thought (CoT) idea, this work we introduce a Three-hop Reasoning (THOR) CoT framework mimic human-like process for ISA. We design three-step prompting principle THOR step-by-step induce aspect,...

Universally modeling all typical information extraction tasks (UIE) with one generative language model (GLM) has revealed great potential by the latest study, where various IE predictions are unified into a linearized hierarchical expression under GLM. Syntactic structure information, type of effective feature which been extensively utilized in community, should also be beneficial to UIE. In this work, we propose novel structure-aware GLM, fully unleashing power syntactic knowledge for A...

Currently the unified semantic role labeling (SRL) that achieves predicate identification and argument in an end-to-end manner has received growing interests. Recent works show leveraging syntax knowledge significantly enhances SRL performances. In this paper, we investigate a novel framework based on sequence-to-sequence architecture with double enhancement both encoder decoder sides. side, propose label-aware graph convolutional network (LA-GCN) to encode syntactic dependent arcs labels...

It has been a hot research topic to enable machines understand human emotions in multimodal contexts under dialogue scenarios, which is tasked with emotion analysis conversation (MM-ERC). MM-ERC received consistent attention recent years, where diverse range of methods proposed for securing better task performance. Most existing works treat as standard classification problem and perform feature disentanglement fusion maximizing utility. Yet after revisiting the characteristic MM-ERC, we...

Bobo Li, Hao Fei, Fei Yuhan Wu, Jinsong Zhang, Shengqiong Jingye Yijiang Liu, Lizi Liao, Tat-Seng Chua, Donghong Ji. Findings of the Association for Computational Linguistics: ACL 2023.

A majority of research interests in irregular (e.g., nested or discontinuous) named entity recognition (NER) have been paid on entities, while discontinuous entities received limited attention. Existing work for NER, however, either suffers from decoding ambiguity predicting using token-level local features. In this work, we present an innovative model NER based pointer networks, where the simultaneously decides whether a token at each frame constitutes mention and next constituent is. Our...

As one of the core video semantic understanding tasks, Video Semantic Role Labeling (VidSRL) aims to detect salient events from given videos, by recognizing predict-argument event structures and interrelationships between events. While recent endeavors have put forth methods for VidSRL, they can be mostly subject two key drawbacks, including lack fine-grained spatial scene perception insufficiently modeling temporality. Towards this end, work explores a novel holistic spatio-temporal graph...

Pair-wise aspect and opinion terms extraction (PAOTE), aiming at detecting the pair of correlated jointly, recently has drawn increasing research attention in community sentiment analysis mining. Recent works largely employ joint methods for task, while they do not sufficiently incorporate external syntactic knowledge, such as dependency edges labels. Besides, these fail to capture underlying shared interactions among overlapping aspect-opinion structures. In this paper we address above...

Abstract The financial status of high‐tech startups directly reflects a country's economic vitality. Hence, the distress will hinder growth. In this study, we extract data seven countries from VICO 2.0 dataset; sample includes (labeled as acquired, nonacquired, failure, and nonfailure) between 2005 2014. We utilize algorithms in machine learning to identify critical variables that predict European Union (EU), thereby preventing startup failure or acquisition. Specifically, our experimental...

Existing works for document-level sentiment classification task treat the review document as an overall text unit, performing feature extraction with various sophisticated model architectures. In this paper, we draw inspiration from fine-grained analysis, proposing to first learn latent target-opinion distribution behind documents, and then leverage such prior knowledge into process. We hierarchical variables, where global-level variable captures target opinion, local-level variables...

Event extraction (EE) is an essential task of information extraction, which aims to extract structured event from unstructured text. Most prior work focuses on extracting flat events while neglecting overlapped or nested ones. A few models for and EE includes several successive stages triggers arguments,which suffer error propagation. Therefore, we design a simple yet effective tagging scheme model formulate as word-word relation recognition, called OneEE. The relations between trigger...

End-to-end semantic role labeling (SRL) has been received increasing interest. It performs the two subtasks of SRL: predicate identification and argument labeling, jointly. Recent work is mostly focused on graph-based neural models, while transition-based framework with networks which widely used in a number closely-related tasks, not studied for joint task yet. In this paper, we present first models end-to-end SRL. Our transition model incrementally discovers all sentential predicates as...

Dialogue Aspect-based Sentiment Quadruple (DiaASQ) is a newly-emergent task aiming to extract the sentiment quadruple (i.e., targets, aspects, opinions, and sentiments) from conversations. While showing promising performance, prior DiaASQ approach unfortunately falls prey key crux of DiaASQ, including insufficient modeling discourse features, lacking extraction, which hinders further improvement. To this end, we introduce novel framework that not only capitalizes on comprehensive feature...

Few-shot named entity recognition (NER) exploits limited annotated instances to identify mentions. Effectively transferring the internal or external resources thus becomes key few-shot NER. While existing prompt tuning methods have shown remarkable performances, they still fail make full use of knowledge. In this work, we investigate integration rich knowledge for stronger We propose incorporating deep framework with threefold (namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML"...

With the proliferation of dialogic data across Internet, Dialogue Commonsense Multi-choice Question Answering (DC-MCQ) task has emerged as a response to challenge comprehending user queries and intentions. Although prevailing methodologies exhibit effectiveness in addressing single-choice questions, they encounter difficulties handling multi-choice due heightened intricacy informational density. In this paper, inspired by human cognitive process progressively excluding options, we propose...

Few-shot named entity recognition (NER) exploits limited annotated instances to identify mentions. Effectively transferring the internal or external resources thus becomes key few-shot NER. While existing prompt tuning methods have shown remarkable performances, they still fail make full use of knowledge. In this work, we investigate integration rich knowledge for stronger We propose incorporating deep framework with threefold (namely TKDP), including 1) context and 2) label & 3) sememe TKDP...

Multimodal Named Entity Recognition (MNER) is a pivotal task designed to extract named entities from text with the support of pertinent images. Nonetheless, notable paucity data for Chinese MNER has considerably impeded progress this natural language processing within domain. Consequently, in study, we compile NER dataset (CMNER) utilizing sourced Weibo, China's largest social media platform. Our encompasses 5,000 Weibo posts paired 18,326 corresponding The are classified into four distinct...