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Found 19 papers, 8 papers with code
What Does Your Smile Mean? Jointly Detecting Multi-Modal Sarcasm and Sentiment Using Quantum Probability
The QPM framework involves a complex-valued multi-modal representation encoder, a quantum-like fusion subnetwork and a quantum measurement mechanism.
Pushing The Limit of LLM Capacity for Text Classification
The value of text classification's future research has encountered challenges and uncertainties, due to the extraordinary efficacy demonstrated by large language models (LLMs) across numerous downstream NLP tasks.
Digital-analog hybrid matrix multiplication processor for optical neural networks
Here, we propose a digital-analog hybrid optical computing architecture for ONNs, which utilizes digital optical inputs in the form of binary words.
DialogueLLM: Context and Emotion Knowledge-Tuned Large Language Models for Emotion Recognition in Conversations
Large language models (LLMs) and their variants have shown extraordinary efficacy across numerous downstream natural language processing (NLP) tasks, which has presented a new vision for the development of NLP.
A Survey of Quantum-Cognitively Inspired Sentiment Analysis Models
Quantum theory, originally proposed as a physical theory to describe the motions of microscopic particles, has been applied to various non-physics domains involving human cognition and decision-making that are inherently uncertain and exhibit certain non-classical, quantum-like characteristics.
Adapting Pre-trained Language Models for Quantum Natural Language Processing
The emerging classical-quantum transfer learning paradigm has brought a decent performance to quantum computational models in many tasks, such as computer vision, by enabling a combination of quantum models and classical pre-trained neural networks.
Template-based Recruitment Email Generation For Job Recommendation
Text generation has long been a popular research topic in NLP.
Quantum Cognitively Motivated Decision Fusion for Video Sentiment Analysis
Thus the cognitive process exhibits "quantum-like" biases that cannot be captured by classical probability theories.
A Multi-task Learning Framework for Opinion Triplet Extraction
The state-of-the-art Aspect-based Sentiment Analysis (ABSA) approaches are mainly based on either detecting aspect terms and their corresponding sentiment polarities, or co-extracting aspect and opinion terms.
Aspect-Based Sentiment Analysis
Aspect Sentiment Triplet Extraction
+2
End-to-end Emotion-Cause Pair Extraction via Learning to Link
Specifically, our model regards pair extraction as a link prediction task, and learns to link from emotion clauses to cause clauses, i. e., the links are directional.
Ranked #12 on
Emotion-Cause Pair Extraction
on ECPE
Assessing the Memory Ability of Recurrent Neural Networks
To tackle the problem, in this paper, we identify and analyze the internal and external factors that affect the memory ability of RNNs, and propose a Semantic Euclidean Space to represent the semantics expressed by a sequence.
Encoding word order in complex embeddings
The benefit of continuous functions over variable positions is that word representations shift smoothly with increasing positions.
Syntax-Aware Aspect-Level Sentiment Classification with Proximity-Weighted Convolution Network
It has been widely accepted that Long Short-Term Memory (LSTM) network, coupled with attention mechanism and memory module, is useful for aspect-level sentiment classification.
Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks
Due to their inherent capability in semantic alignment of aspects and their context words, attention mechanism and Convolutional Neural Networks (CNNs) are widely applied for aspect-based sentiment classification.
CNM: An Interpretable Complex-valued Network for Matching
This paper seeks to model human language by the mathematical framework of quantum physics.
Semantic Hilbert Space for Text Representation Learning
To address this issue, we propose a new framework that models different levels of semantic units (e. g. sememe, word, sentence, and semantic abstraction) on a single \textit{Semantic Hilbert Space}, which naturally admits a non-linear semantic composition by means of a complex-valued vector word representation.
Quantum-inspired Complex Word Embedding
A challenging task for word embeddings is to capture the emergent meaning or polarity of a combination of individual words.
