Search Results for author:
Found 144 papers, 62 papers with code
WME 3.0: An Enhanced and Validated Lexicon of Medical Concepts
Information extraction in the medical domain is laborious and time-consuming due to the insufficient number of domain-specific lexicons and lack of involvement of domain experts such as doctors and medical practitioners.
WME: Sense, Polarity and Affinity based Concept Resource for Medical Events
In order to overcome the lack of medical corpora, we have developed a WordNet for Medical Events (WME) for identifying medical terms and their sense related information using a seed list.
SenticNet 7: A Commonsense-based Neurosymbolic AI Framework for Explainable Sentiment Analysis
In recent years, AI research has demonstrated enormous potential for the benefit of humanity and society.
Harnessing Large Language Models for Scientific Novelty Detection
In an era of exponential scientific growth, identifying novel research ideas is crucial and challenging in academia.
Understanding Refusal in Language Models with Sparse Autoencoders
Refusal is a key safety behavior in aligned language models, yet the internal mechanisms driving refusals remain opaque.
Deriving Strategic Market Insights with Large Language Models: A Benchmark for Forward Counterfactual Generation
This paves the way for scalable and automated solutions for exploring and anticipating future market developments, thereby providing structured insights for decision-making.
Beyond Specialization: Benchmarking LLMs for Transliteration of Indian Languages
Transliteration, the process of mapping text from one script to another, plays a crucial role in multilingual natural language processing, especially within linguistically diverse contexts such as India.
MOOSE-Chem2: Exploring LLM Limits in Fine-Grained Scientific Hypothesis Discovery via Hierarchical Search
Large language models (LLMs) have shown promise in automating scientific hypothesis generation, yet existing approaches primarily yield coarse-grained hypotheses lacking critical methodological and experimental details.
MOOSE-Chem3: Toward Experiment-Guided Hypothesis Ranking via Simulated Experimental Feedback
Hypothesis ranking is a crucial component of automated scientific discovery, particularly in natural sciences where wet-lab experiments are costly and throughput-limited.
Neural Brain: A Neuroscience-inspired Framework for Embodied Agents
The rapid evolution of artificial intelligence (AI) has shifted from static, data-driven models to dynamic systems capable of perceiving and interacting with real-world environments.
ClimaEmpact: Domain-Aligned Small Language Models and Datasets for Extreme Weather Analytics
By aligning SLMs with advanced reasoning strategies on ExtremeWeatherNews (and its derived dataset ExtremeAlign used specifically for SLM alignment), EWRA improves the SLMs' ability to generate well-grounded and domain-specific responses for extreme weather analytics.
SEA-LION: Southeast Asian Languages in One Network
Recently, Large Language Models (LLMs) have dominated much of the artificial intelligence scene with their ability to process and generate natural languages.
ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition
Large language models (LLMs) have demonstrated potential in assisting scientific research, yet their ability to discover high-quality research hypotheses remains unexamined due to the lack of a dedicated benchmark.
MAPS: A Multi-Agent Framework Based on Big Seven Personality and Socratic Guidance for Multimodal Scientific Problem Solving
Multimodal scientific problems (MSPs) involve complex issues that require the integration of multiple modalities, such as text and diagrams, presenting a significant challenge in artificial intelligence.
MARS: A Multi-Agent Framework Incorporating Socratic Guidance for Automated Prompt Optimization
The basic question-answering format of large language models involves inputting a prompt and receiving a response, and the quality of the prompt directly impacts the effectiveness of the response.
Deep Cut-informed Graph Embedding and Clustering
For the clustering module, we utilize the optimal transport theory to obtain the clustering assignments, which can balance the guidance of "proximity to the pre-learned cluster center".
Towards Robust ESG Analysis Against Greenwashing Risks: Aspect-Action Analysis with Cross-Category Generalization
Sustainability reports are key for evaluating companies' environmental, social and governance, ESG performance, but their content is increasingly obscured by greenwashing - sustainability claims that are misleading, exaggerated, and fabricated.
ESGSenticNet: A Neurosymbolic Knowledge Base for Corporate Sustainability Analysis
Evaluating corporate sustainability performance is essential to drive sustainable business practices, amid the need for a more sustainable economy.
Owls are wise and foxes are unfaithful: Uncovering animal stereotypes in vision-language models
Animal stereotypes are deeply embedded in human culture and language.
Gender Bias in Text-to-Video Generation Models: A case study of Sora
The advent of text-to-video generation models has revolutionized content creation as it produces high-quality videos from textual prompts.
Towards Rich Emotions in 3D Avatars: A Text-to-3D Avatar Generation Benchmark
T3DEM is the most crucial step in determining the quality of Emo3D generation and encompasses three key challenges: Expression Diversity, Emotion-Content Consistency, and Expression Fluidity.
Towards Faithful Natural Language Explanations: A Study Using Activation Patching in Large Language Models
Large Language Models (LLMs) are capable of generating persuasive Natural Language Explanations (NLEs) to justify their answers.
MOOSE-Chem: Large Language Models for Rediscovering Unseen Chemistry Scientific Hypotheses
To address this, we propose a formal mathematical decomposition grounded in a fundamental assumption: that most chemistry hypotheses can be composed from a research background and a set of inspirations.
Recent Trends of Multimodal Affective Computing: A Survey from NLP Perspective
The goal of this survey is to explore the current landscape of multimodal affective research, identify development trends, and highlight the similarities and differences across various tasks, offering a comprehensive report on the recent progress in multimodal affective computing from an NLP perspective.
Aspect-Based Sentiment Analysis
Emotion Recognition in Conversation
+3
Negation Blindness in Large Language Models: Unveiling the NO Syndrome in Image Generation
In the present work, we have identified a fundamental limitation related to the image generation ability of LLMs, and termed it The NO Syndrome.
Has Multimodal Learning Delivered Universal Intelligence in Healthcare? A Comprehensive Survey
The rapid development of artificial intelligence has constantly reshaped the field of intelligent healthcare and medicine.
PanoSent: A Panoptic Sextuple Extraction Benchmark for Multimodal Conversational Aspect-based Sentiment Analysis
While existing Aspect-based Sentiment Analysis (ABSA) has received extensive effort and advancement, there are still gaps in defining a more holistic research target seamlessly integrating multimodality, conversation context, fine-granularity, and also covering the changing sentiment dynamics as well as cognitive causal rationales.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
XAI meets LLMs: A Survey of the Relation between Explainable AI and Large Language Models
In this survey, we address the key challenges in Large Language Models (LLM) research, focusing on the importance of interpretability.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+2
Prompted Aspect Key Point Analysis for Quantitative Review Summarization
A majority of KPA studies for reviews adopt supervised learning to extract short sentences as KPs before matching KPs to review comments for quantification of KP prevalence.
Explainable Natural Language Processing for Corporate Sustainability Analysis
Sustainability commonly refers to entities, such as individuals, companies, and institutions, having a non-detrimental (or even positive) impact on the environment, society, and the economy.
Self-training Large Language Models through Knowledge Detection
Large language models (LLMs) often necessitate extensive labeled datasets and training compute to achieve impressive performance across downstream tasks.
The MuSe 2024 Multimodal Sentiment Analysis Challenge: Social Perception and Humor Recognition
The Multimodal Sentiment Analysis Challenge (MuSe) 2024 addresses two contemporary multimodal affect and sentiment analysis problems: In the Social Perception Sub-Challenge (MuSe-Perception), participants will predict 16 different social attributes of individuals such as assertiveness, dominance, likability, and sincerity based on the provided audio-visual data.
SemEval-2024 Task 3: Multimodal Emotion Cause Analysis in Conversations
The ability to understand emotions is an essential component of human-like artificial intelligence, as emotions greatly influence human cognition, decision making, and social interactions.
MER 2024: Semi-Supervised Learning, Noise Robustness, and Open-Vocabulary Multimodal Emotion Recognition
However, this process may lead to inaccurate annotations, such as ignoring non-majority or non-candidate labels.
SemEval 2024 -- Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF)
We present SemEval-2024 Task 10, a shared task centred on identifying emotions and finding the rationale behind their flips within monolingual English and Hindi-English code-mixed dialogues.
How Interpretable are Reasoning Explanations from Prompting Large Language Models?
We present a comprehensive and multifaceted evaluation of interpretability, examining not only faithfulness but also robustness and utility across multiple commonsense reasoning benchmarks.
Plausible Extractive Rationalization through Semi-Supervised Entailment Signal
The increasing use of complex and opaque black box models requires the adoption of interpretable measures, one such option is extractive rationalizing models, which serve as a more interpretable alternative.
Diff-Oracle: Deciphering Oracle Bone Scripts with Controllable Diffusion Model
In particular, on the challenging OBC306 dataset, Diff-Oracle leads to an accuracy gain of 7. 70% in the zero-shot setting and is able to recognize unseen oracle character images with the accuracy of 84. 62%, achieving a new benchmark for deciphering oracle bone scripts.
Hypergrah-Enhanced Dual Convolutional Network for Bundle Recommendation
While previous approaches have demonstrated notable performance, we argue that they may compromise the ternary relationship among users, items, and bundles.
Rethinking Large Language Models in Mental Health Applications
Large Language Models (LLMs) have become valuable assets in mental health, showing promise in both classification tasks and counseling applications.
A Survey on Semantic Processing Techniques
We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks.
A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics
Secondly, we conduct a comparison between the previous PLMs and the latest LLMs, as well as comparing various LLMs with each other.
A Comprehensive Review on Financial Explainable AI
The success of artificial intelligence (AI), and deep learning models in particular, has led to their widespread adoption across various industries due to their ability to process huge amounts of data and learn complex patterns.
Large Language Models for Automated Open-domain Scientific Hypotheses Discovery
Hypothetical induction is recognized as the main reasoning type when scientists make observations about the world and try to propose hypotheses to explain those observations.
A Wide Evaluation of ChatGPT on Affective Computing Tasks
In this work, we widely study the capabilities of the ChatGPT models, namely GPT-4 and GPT-3. 5, on 13 affective computing problems, namely aspect extraction, aspect polarity classification, opinion extraction, sentiment analysis, sentiment intensity ranking, emotions intensity ranking, suicide tendency detection, toxicity detection, well-being assessment, engagement measurement, personality assessment, sarcasm detection, and subjectivity detection.
GPTEval: A Survey on Assessments of ChatGPT and GPT-4
The emergence of ChatGPT has generated much speculation in the press about its potential to disrupt social and economic systems.
EnTri: Ensemble Learning with Tri-level Representations for Explainable Scene Recognition
Through experiments on benchmark scene classification datasets, EnTri has demonstrated superiority in terms of recognition accuracy, achieving competitive performance compared to state-of-the-art approaches, with an accuracy of 87. 69%, 75. 56%, and 99. 17% on the MIT67, SUN397, and UIUC8 datasets, respectively.
TbExplain: A Text-based Explanation Method for Scene Classification Models with the Statistical Prediction Correction
Additionally, objects and regions of the input image that are relevant to the model prediction are frequently not entirely differentiated by heatmaps.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Can ChatGPT's Responses Boost Traditional Natural Language Processing?
In this work, we extend this by exploring if ChatGPT has novel knowledge that would enhance existing specialised models when they are fused together.
Recent Developments in Recommender Systems: A Survey
The objective of this study is to provide an overview of the current state-of-the-art in the field and highlight the latest trends in the development of recommender systems.
Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond
Logical reasoning consistently plays a fundamental and significant role in the domains of knowledge engineering and artificial intelligence.
Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning
In cross-lingual named entity recognition (NER), self-training is commonly used to bridge the linguistic gap by training on pseudo-labeled target-language data.
Finding the Pillars of Strength for Multi-Head Attention
Specifically, the heads of MHA were originally designed to attend to information from different representation subspaces, whereas prior studies found that some attention heads likely learn similar features and can be pruned without harming performance.
The MuSe 2023 Multimodal Sentiment Analysis Challenge: Mimicked Emotions, Cross-Cultural Humour, and Personalisation
Participants predict the presence of spontaneous humour in a cross-cultural setting.
A Review of Deep Learning for Video Captioning
Video captioning (VC) is a fast-moving, cross-disciplinary area of research that bridges work in the fields of computer vision, natural language processing (NLP), linguistics, and human-computer interaction.
Domain-specific Continued Pretraining of Language Models for Capturing Long Context in Mental Health
In the mental health domain, domain-specific language models are pretrained and released, which facilitates the early detection of mental health conditions.
MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning
The first Multimodal Emotion Recognition Challenge (MER 2023) was successfully held at ACM Multimedia.
Logical Reasoning over Natural Language as Knowledge Representation: A Survey
This paper provides a comprehensive overview on a new paradigm of logical reasoning, which uses natural language as knowledge representation and pretrained language models as reasoners, including philosophical definition and categorization of logical reasoning, advantages of the new paradigm, benchmarks and methods, challenges of the new paradigm, possible future directions, and relation to related NLP fields.
Adaptive Knowledge Distillation between Text and Speech Pre-trained Models
Learning on a massive amount of speech corpus leads to the recent success of many self-supervised speech models.
FinXABSA: Explainable Finance through Aspect-Based Sentiment Analysis
This paper presents a novel approach for explainability in financial analysis by deriving financially-explainable statistical relationships through aspect-based sentiment analysis, Pearson correlation, Granger causality & uncertainty coefficient.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Will Affective Computing Emerge from Foundation Models and General AI? A First Evaluation on ChatGPT
We utilise three baselines, a robust language model (RoBERTa-base), a legacy word model with pretrained embeddings (Word2Vec), and a simple bag-of-words baseline (BoW).
Language Models as Inductive Reasoners
To this end, we propose a new paradigm (task) for inductive reasoning, which is to induce natural language rules from natural language facts, and create a dataset termed DEER containing 1. 2k rule-fact pairs for the task, where rules and facts are written in natural language.
ConNER: Consistency Training for Cross-lingual Named Entity Recognition
We propose ConNER as a novel consistency training framework for cross-lingual NER, which comprises of: (1) translation-based consistency training on unlabeled target-language data, and (2) dropoutbased consistency training on labeled source-language data.
Knowledge Representation for Conceptual, Motivational, and Affective Processes in Natural Language Communication
However, in human-robot collaborative social communication and in using natural language for delivering precise instructions to robots, a deeper representation of the conceptual, motivational, and affective processes is needed.
Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings
Automatic depression detection on Twitter can help individuals privately and conveniently understand their mental health status in the early stages before seeing mental health professionals.
Proceedings of the ICML 2022 Expressive Vocalizations Workshop and Competition: Recognizing, Generating, and Personalizing Vocal Bursts
The first, ExVo-MultiTask, requires participants to train a multi-task model to recognize expressed emotions and demographic traits from vocal bursts.
The MuSe 2022 Multimodal Sentiment Analysis Challenge: Humor, Emotional Reactions, and Stress
For this year's challenge, we feature three datasets: (i) the Passau Spontaneous Football Coach Humor (Passau-SFCH) dataset that contains audio-visual recordings of German football coaches, labelled for the presence of humour; (ii) the Hume-Reaction dataset in which reactions of individuals to emotional stimuli have been annotated with respect to seven emotional expression intensities, and (iii) the Ulm-Trier Social Stress Test (Ulm-TSST) dataset comprising of audio-visual data labelled with continuous emotion values (arousal and valence) of people in stressful dispositions.
The ICML 2022 Expressive Vocalizations Workshop and Competition: Recognizing, Generating, and Personalizing Vocal Bursts
ExVo 2022, includes three competition tracks using a large-scale dataset of 59, 201 vocalizations from 1, 702 speakers.
Deep-Attack over the Deep Reinforcement Learning
Recent adversarial attack developments have made reinforcement learning more vulnerable, and different approaches exist to deploy attacks against it, where the key is how to choose the right timing of the attack.
Polarity and Subjectivity Detection with Multitask Learning and BERT Embedding
The proposed approach reports baseline performances for both polarity detection and subjectivity detection.
MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare
Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without adequate treatment.
Fusing task-oriented and open-domain dialogues in conversational agents
It features inter-mode contextual dependency, i. e., the dialogue turns from the two modes depend on each other.
Ranked #1 on
Dialogue Generation
on FusedChat
Multitask Balanced and Recalibrated Network for Medical Code Prediction
Nevertheless, automated medical coding is still challenging because of the imbalanced class problem, complex code association, and noise in lengthy documents.
MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER
Data augmentation is an effective solution to data scarcity in low-resource scenarios.
MuSe-Toolbox: The Multimodal Sentiment Analysis Continuous Annotation Fusion and Discrete Class Transformation Toolbox
With this in mind, the MuSe-Toolbox provides the functionality to run exhaustive searches for meaningful class clusters in the continuous gold standards.
Graph Routing between Capsules
Therefore, in this paper, we introduce a new capsule network with graph routing to learn both relationships, where capsules in each layer are treated as the nodes of a graph.
MICE: A Crosslinguistic Emotion Corpus in Malay, Indonesian, Chinese and English
MICE is a corpus of emotion words in four languages which is currently working progress.
Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey
To the best of our knowledge, this survey is the most comprehensive and up-to-date one at present for deep learning based dialogue systems, extensively covering the popular techniques.
BLM-17m: A Large-Scale Dataset for Black Lives Matter Topic Detection on Twitter
In this paper, our aim is to provide a dataset which covers one of the most significant human rights contradiction in recent months affected the whole world, George Floyd incident.
The MuSe 2021 Multimodal Sentiment Analysis Challenge: Sentiment, Emotion, Physiological-Emotion, and Stress
Multimodal Sentiment Analysis (MuSe) 2021 is a challenge focusing on the tasks of sentiment and emotion, as well as physiological-emotion and emotion-based stress recognition through more comprehensively integrating the audio-visual, language, and biological signal modalities.
Multitask Recalibrated Aggregation Network for Medical Code Prediction
Medical coding translates professionally written medical reports into standardized codes, which is an essential part of medical information systems and health insurance reimbursement.
A Novel Context-Aware Multimodal Framework for Persian Sentiment Analysis
Most recent works on sentiment analysis have exploited the text modality.
A Survey on Personality-Aware Recommendation Systems
With the emergence of personality computing as a new research field related to artificial intelligence and personality psychology, we have witnessed an unprecedented proliferation of personality-aware recommendation systems.
Multitask Learning for Emotion and Personality Detection
In recent years, deep learning-based automated personality trait detection has received a lot of attention, especially now, due to the massive digital footprints of an individual.
A Survey on Deep Reinforcement Learning for Audio-Based Applications
We begin with an introduction to the general field of DL and reinforcement learning (RL), then progress to the main DRL methods and their applications in the audio domain.
Improving Zero Shot Learning Baselines with Commonsense Knowledge
Zero shot learning -- the problem of training and testing on a completely disjoint set of classes -- relies greatly on its ability to transfer knowledge from train classes to test classes.
JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models against Commonsense Validation and Explanation
As for the last subtask, our models reach 16. 10 BLEU score and 1. 94 human evaluation score placing our team in the 5th and 3rd places according to these two metrics, respectively.
Financial Sentiment Analysis: An Investigation into Common Mistakes and Silver Bullets
The recent dominance of machine learning-based natural language processing methods has fostered the culture of overemphasizing model accuracies rather than studying the reasons behind their errors.
Autonomous Driving
Cultural Vocal Bursts Intensity Prediction
+1
Personality Trait Detection Using Bagged SVM over BERT Word Embedding Ensembles
In this work, we present a novel deep learning-based approach for automated personality detection from text.
Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text
Medical code assignment, which predicts medical codes from clinical texts, is a fundamental task of intelligent medical information systems.
BiERU: Bidirectional Emotional Recurrent Unit for Conversational Sentiment Analysis
Sentiment analysis in conversations has gained increasing attention in recent years for the growing amount of applications it can serve, e. g., sentiment analysis, recommender systems, and human-robot interaction.
Ranked #44 on
Emotion Recognition in Conversation
on IEMOCAP
A review of sentiment analysis research in Arabic language
Sentiment analysis is a task of natural language processing which has recently attracted increasing attention.
MuSe 2020 -- The First International Multimodal Sentiment Analysis in Real-life Media Challenge and Workshop
Multimodal Sentiment Analysis in Real-life Media (MuSe) 2020 is a Challenge-based Workshop focusing on the tasks of sentiment recognition, as well as emotion-target engagement and trustworthiness detection by means of more comprehensively integrating the audio-visual and language modalities.
Suicidal Ideation and Mental Disorder Detection with Attentive Relation Networks
Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without effective treatment.
Deep Learning Based Text Classification: A Comprehensive Review
Deep learning based models have surpassed classical machine learning based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural language inference.
The Four Dimensions of Social Network Analysis: An Overview of Research Methods, Applications, and Software Tools
Social network based applications have experienced exponential growth in recent years.
A Survey on Knowledge Graphs: Representation, Acquisition and Applications
In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research.
Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications
Suicide is a critical issue in modern society.
Recent Trends in Deep Learning Based Personality Detection
This review paper provides an overview of the most popular approaches to automated personality detection, various computational datasets, its industrial applications, and state-of-the-art machine learning models for personality detection with specific focus on multimodal approaches.
Business Taxonomy Construction Using Concept-Level Hierarchical Clustering
Business taxonomies are indispensable tools for investors to do equity research and make professional decisions.
Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications
Obstacles hindering the development of capsule networks for challenging NLP applications include poor scalability to large output spaces and less reliable routing processes.
Ranked #1 on
Text Classification
on RCV1
(P@1 metric)
Can a Humanoid Robot be part of the Organizational Workforce? A User Study Leveraging Sentiment Analysis
In this study, we propose to have a humanoid social robot, Nadine, as a customer service agent in an open social work environment.
PhonSenticNet: A Cognitive Approach to Microtext Normalization for Concept-Level Sentiment Analysis
The usage of microtext poses a considerable performance issue in concept-level sentiment analysis, since models are trained on standard words.
"Hang in There": Lexical and Visual Analysis to Identify Posts Warranting Empathetic Responses
In the past few years, social media has risen as a platform where people express and share personal incidences about abuse, violence and mental health issues.
Aspect-Sentiment Embeddings for Company Profiling and Employee Opinion Mining
With the multitude of companies and organizations abound today, ranking them and choosing one out of the many is a difficult and cumbersome task.
Sentiment and Sarcasm Classification with Multitask Learning
We argue that knowledge in sarcasm detection can also be beneficial to sentiment classification and vice versa.
Phonetic-enriched Text Representation for Chinese Sentiment Analysis with Reinforcement Learning
Furthermore, we also fuse phonetic features with textual and visual features in order to mimic the way humans read and understand Chinese text.
Marshall-Olkin Power-Law Distributions in Length-Frequency of Entities
Entities have different forms in different linguistic tasks and researchers treat those different forms as different concepts.
DialogueRNN: An Attentive RNN for Emotion Detection in Conversations
Emotion detection in conversations is a necessary step for a number of applications, including opinion mining over chat history, social media threads, debates, argumentation mining, understanding consumer feedback in live conversations, etc.
Ranked #3 on
Emotion Recognition in Conversation
on SEMAINE
Emotion Classification
Emotion Recognition in Conversation
+2
Named Entity Analysis and Extraction with Uncommon Words
Most previous research treats named entity extraction and classification as an end-to-end task.
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations.
ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection
Emotion recognition in conversations is crucial for building empathetic machines.
Ranked #55 on
Emotion Recognition in Conversation
on IEMOCAP
Emotion Recognition in Conversation
General Classification
+2
IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis
Sentiment analysis has immense implications in e-commerce through user feedback mining.
Semantically Enhanced Models for Commonsense Knowledge Acquisition
Commonsense knowledge is paramount to enable intelligent systems.
Concept-Based Embeddings for Natural Language Processing
In this work, we focus on effectively leveraging and integrating information from concept-level as well as word-level via projecting concepts and words into a lower dimensional space while retaining most critical semantics.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph
Analyzing human multimodal language is an emerging area of research in NLP.
Ranked #11 on
Multimodal Sentiment Analysis
on CMU-MOSEI
(using extra training data)
Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos
Emotion recognition in conversations is crucial for the development of empathetic machines.
Ranked #57 on
Emotion Recognition in Conversation
on IEMOCAP
Modeling Inter-Aspect Dependencies for Aspect-Based Sentiment Analysis
Aspect-based Sentiment Analysis is a fine-grained task of sentiment classification for multiple aspects in a sentence.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+4
Anaphora and Coreference Resolution: A Review
Entity resolution aims at resolving repeated references to an entity in a document and forms a core component of natural language processing (NLP) research.
CASCADE: Contextual Sarcasm Detection in Online Discussion Forums
The literature in automated sarcasm detection has mainly focused on lexical, syntactic and semantic-level analysis of text.
Ranked #1 on
Sarcasm Detection
on SARC (all-bal)
Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM
Analyzing people’s opinions and sentiments towards certain aspects is an important task of natural language understanding.
Ranked #4 on
Aspect-Based Sentiment Analysis (ABSA)
on Sentihood
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines
We compile baselines, along with dataset split, for multimodal sentiment analysis.
A Deep Learning Approach for Multimodal Deception Detection
Automatic deception detection is an important task that has gained momentum in computational linguistics due to its potential applications.
Discovering Bayesian Market Views for Intelligent Asset Allocation
Along with the advance of opinion mining techniques, public mood has been found to be a key element for stock market prediction.
Multi-attention Recurrent Network for Human Communication Comprehension
AI must understand each modality and the interactions between them that shape human communication.
Ranked #10 on
Multimodal Sentiment Analysis
on MOSI
Memory Fusion Network for Multi-view Sequential Learning
In this paper, we present a new neural architecture for multi-view sequential learning called the Memory Fusion Network (MFN) that explicitly accounts for both interactions in a neural architecture and continuously models them through time.
Basic tasks of sentiment analysis
Subjectivity detection is the task of identifying objective and subjective sentences.
Augmenting End-to-End Dialog Systems with Commonsense Knowledge
Building dialog agents that can converse naturally with humans is a challenging yet intriguing problem of artificial intelligence.
Disentangled Variational Auto-Encoder for Semi-supervised Learning
The majority of existing semi-supervised VAEs utilize a classifier to exploit label information, where the parameters of the classifier are introduced to the VAE.
Recent Trends in Deep Learning Based Natural Language Processing
Deep learning methods employ multiple processing layers to learn hierarchical representations of data and have produced state-of-the-art results in many domains.
Benchmarking Multimodal Sentiment Analysis
We propose a framework for multimodal sentiment analysis and emotion recognition using convolutional neural network-based feature extraction from text and visual modalities.
Tensor Fusion Network for Multimodal Sentiment Analysis
Multimodal sentiment analysis is an increasingly popular research area, which extends the conventional language-based definition of sentiment analysis to a multimodal setup where other relevant modalities accompany language.
Developing a concept-level knowledge base for sentiment analysis in Singlish
In this paper, we present Singlish sentiment lexicon, a concept-level knowledge base for sentiment analysis that associates multiword expressions to a set of emotion labels and a polarity value.
Context-Dependent Sentiment Analysis in User-Generated Videos
Multimodal sentiment analysis is a developing area of research, which involves the identification of sentiments in videos.
Ranked #3 on
Emotion Recognition in Conversation
on CPED
Emotion Recognition in Conversation
General Classification
+4
Time Expression Analysis and Recognition Using Syntactic Token Types and General Heuristic Rules
Extracting time expressions from free text is a fundamental task for many applications.
Tracing Linguistic Relations in Winning and Losing Sides of Explicit Opposing Groups
Linguistic relations in oral conversations present how opinions are constructed and developed in a restricted time.
SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives
An important difference between traditional AI systems and human intelligence is the human ability to harness commonsense knowledge gleaned from a lifetime of learning and experience to make informed decisions.
Label Embedding for Zero-shot Fine-grained Named Entity Typing
Named entity typing is the task of detecting the types of a named entity in context.
From Node Embedding To Community Embedding
Most of the existing graph embedding methods focus on nodes, which aim to output a vector representation for each node in the graph such that two nodes being "close" on the graph are close too in the low-dimensional space.
A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks
Sarcasm detection is a key task for many natural language processing tasks.
Affective Common Sense Knowledge Acquisition for Sentiment Analysis
Thanks to the advent of Web 2. 0, the potential for opinion sharing today is unmatched in history.
